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references.bib
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@article{keinanen_dual_2019,
title = {Dual {Radionuclide} {Theranostic} {Pretargeting}},
issn = {1543-8384, 1543-8392},
url = {http://pubs.acs.org/doi/10.1021/acs.molpharmaceut.9b00746},
doi = {10.1021/acs.molpharmaceut.9b00746},
language = {en},
urldate = {2019-09-05},
journal = {Molecular Pharmaceutics},
author = {Keinänen, Outi and Brennan, James and Membreno, Rosemery and Fung, Kimberly and Gangangari, Kishore and Dayts, Eric and Williams, Carter J. and Zeglis, Brian M.},
month = sep,
year = {2019},
pages = {acs.molpharmaceut.9b00746}
}
@article{lu_identification_2016,
title = {Identification of new candidate drugs for lung cancer using chemical–chemical interactions, chemical–protein interactions and a {K}-means clustering algorithm},
volume = {34},
issn = {0739-1102, 1538-0254},
url = {http://www.tandfonline.com/doi/full/10.1080/07391102.2015.1060161},
doi = {10.1080/07391102.2015.1060161},
language = {en},
number = {4},
urldate = {2019-09-30},
journal = {Journal of Biomolecular Structure and Dynamics},
author = {Lu, Jing and Chen, Lei and Yin, Jun and Huang, Tao and Bi, Yi and Kong, Xiangyin and Zheng, Mingyue and Cai, Yu-Dong},
month = apr,
year = {2016},
pages = {906--917}
}
@article{martinez_drugnet:_2015,
title = {{DrugNet}: {Network}-based drug–disease prioritization by integrating heterogeneous data},
volume = {63},
issn = {09333657},
shorttitle = {{DrugNet}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0933365714001444},
doi = {10.1016/j.artmed.2014.11.003},
language = {en},
number = {1},
urldate = {2019-09-30},
journal = {Artificial Intelligence in Medicine},
author = {Martínez, Víctor and Navarro, Carmen and Cano, Carlos and Fajardo, Waldo and Blanco, Armando},
month = jan,
year = {2015},
pages = {41--49}
}
@article{ashburn_drug_2004,
title = {Drug repositioning: identifying and developing new uses for existing drugs},
volume = {3},
issn = {1474-1776},
shorttitle = {Drug repositioning},
doi = {10.1038/nrd1468},
language = {eng},
number = {8},
journal = {Nature Reviews. Drug Discovery},
author = {Ashburn, Ted T. and Thor, Karl B.},
month = aug,
year = {2004},
pmid = {15286734},
keywords = {Drug Delivery Systems, Drug Design, Humans, Pharmacokinetics, Technology, Pharmaceutical, Therapeutic Equivalency},
pages = {673--683}
}
@article{novac_challenges_2013,
title = {Challenges and opportunities of drug repositioning},
volume = {34},
issn = {01656147},
url = {https://linkinghub.elsevier.com/retrieve/pii/S016561471300045X},
doi = {10.1016/j.tips.2013.03.004},
language = {en},
number = {5},
urldate = {2019-10-02},
journal = {Trends in Pharmacological Sciences},
author = {Novac, Natalia},
month = may,
year = {2013},
pages = {267--272}
}
@article{hay_clinical_2014,
title = {Clinical development success rates for investigational drugs},
volume = {32},
issn = {1546-1696},
doi = {10.1038/nbt.2786},
language = {eng},
number = {1},
journal = {Nature Biotechnology},
author = {Hay, Michael and Thomas, David W. and Craighead, John L. and Economides, Celia and Rosenthal, Jesse},
month = jan,
year = {2014},
pmid = {24406927},
keywords = {Humans, Biomedical Research, Drug Approval, Drugs, Investigational, United States, United States Food and Drug Administration},
pages = {40--51}
}
@article{kobayashi_current_2019,
title = {Current state and outlook for drug repositioning anticipated in the field of ovarian cancer},
volume = {30},
issn = {2005-0380, 2005-0399},
url = {https://synapse.koreamed.org/DOIx.php?id=10.3802/jgo.2019.30.e10},
doi = {10.3802/jgo.2019.30.e10},
language = {en},
number = {1},
urldate = {2019-10-02},
journal = {Journal of Gynecologic Oncology},
author = {Kobayashi, Yusuke and Banno, Kouji and Kunitomi, Haruko and Tominaga, Eiichiro and Aoki, Daisuke},
year = {2019},
pages = {e10},
file = {Full Text:/home/lxu/Zotero/storage/49JX3RJY/Kobayashi et al. - 2019 - Current state and outlook for drug repositioning a.pdf:application/pdf}
}
@article{liu_silico_2013,
title = {In silico drug repositioning – what we need to know},
volume = {18},
issn = {13596446},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1359644612002991},
doi = {10.1016/j.drudis.2012.08.005},
language = {en},
number = {3-4},
urldate = {2019-10-02},
journal = {Drug Discovery Today},
author = {Liu, Zhichao and Fang, Hong and Reagan, Kelly and Xu, Xiaowei and Mendrick, Donna L. and Slikker, William and Tong, Weida},
month = feb,
year = {2013},
pages = {110--115}
}
@article{lu_identification_2016-1,
title = {Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a {K}-means clustering algorithm},
volume = {34},
issn = {1538-0254},
doi = {10.1080/07391102.2015.1060161},
abstract = {Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.},
language = {eng},
number = {4},
journal = {Journal of Biomolecular Structure \& Dynamics},
author = {Lu, Jing and Chen, Lei and Yin, Jun and Huang, Tao and Bi, Yi and Kong, Xiangyin and Zheng, Mingyue and Cai, Yu-Dong},
year = {2016},
pmid = {26849843},
keywords = {Humans, Algorithms, Antineoplastic Agents, chemical–chemical interaction, chemical–protein interaction, Cluster Analysis, Computational Biology, Databases, Pharmaceutical, Drug Discovery, K-means clustering algorithm, Ligands, lung cancer, Lung Neoplasms, Mutation, Proteins, Structure-Activity Relationship},
pages = {906--917}
}
@misc{pathway_alexander_2015,
title = {Alexander {Pico}, {Daniel} {Himmelstein} (2015) {Adding} pathway resources to your network. {Thinklab}. doi:10.15363/thinklab.d72}
}
@misc{himmelstein_stargeo_2015,
title = {Daniel {Himmelstein}, {Frederic} {Bastian}, {Dexter} {Hadley}, {Casey} {Greene} (2015) {STARGEO}: expression signatures for disease using crowdsourced {GEO} annotation. {Thinklab}. doi:10.15363/thinklab.d96}
}
@misc{himmelstein_entrez_2015,
title = {Daniel {Himmelstein}, {Casey} {Greene}, {Alexander} {Pico} (2015) {Using} {Entrez} {Gene} as our gene vocabulary. {Thinklab}. doi:10.15363/thinklab.d34},
annote = {thinklab for entrez gene.}
}
@misc{himmelstein_calculating_2015,
title = {Daniel {Himmelstein}, {Alex} {Pankov} (2015) {Mining} knowledge from {MEDLINE} articles and their indexed {MeSH} terms. {Thinklab}. doi:10.15363/thinklab.d67}
}
@misc{himmelstein_mining_2015,
title = {Daniel {Himmelstein}, {Alex} {Pankov} (2015) {Mining} knowledge from {MEDLINE} articles and their indexed {MeSH} terms. {Thinklab}. doi:10.15363/thinklab.d67}
}
@misc{himmelstein_computing_2015,
title = {Daniel {Himmelstein}, {Caty} {Chung} (2015) {Computing} consensus transcriptional profiles for {LINCS} {L}1000 perturbations. {Thinklab}. doi:10.15363/thinklab.d43}
}
@article{yu_next-generation_2011,
title = {Next-generation sequencing to generate interactome datasets},
volume = {8},
issn = {1548-7105},
doi = {10.1038/nmeth.1597},
abstract = {Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping.},
language = {eng},
number = {6},
journal = {Nature Methods},
author = {Yu, Haiyuan and Tardivo, Leah and Tam, Stanley and Weiner, Evan and Gebreab, Fana and Fan, Changyu and Svrzikapa, Nenad and Hirozane-Kishikawa, Tomoko and Rietman, Edward and Yang, Xinping and Sahalie, Julie and Salehi-Ashtiani, Kourosh and Hao, Tong and Cusick, Michael E. and Hill, David E. and Roth, Frederick P. and Braun, Pascal and Vidal, Marc},
month = jun,
year = {2011},
pmid = {21516116},
pmcid = {PMC3188388},
keywords = {Humans, Databases, Protein, Open Reading Frames, Polymerase Chain Reaction, Protein Interaction Mapping, Sequence Analysis, DNA, Two-Hybrid System Techniques},
pages = {478--480},
file = {Accepted Version:/home/lxu/Zotero/storage/8CWKAFKQ/Yu et al. - 2011 - Next-generation sequencing to generate interactome.pdf:application/pdf}
}
@article{schaefer_pid:_2009,
title = {{PID}: the {Pathway} {Interaction} {Database}},
volume = {37},
issn = {1362-4962},
shorttitle = {{PID}},
doi = {10.1093/nar/gkn653},
abstract = {The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.},
language = {eng},
number = {Database issue},
journal = {Nucleic Acids Research},
author = {Schaefer, Carl F. and Anthony, Kira and Krupa, Shiva and Buchoff, Jeffrey and Day, Matthew and Hannay, Timo and Buetow, Kenneth H.},
month = jan,
year = {2009},
pmid = {18832364},
pmcid = {PMC2686461},
keywords = {Humans, Proteins, Protein Interaction Mapping, Biological Transport, Cell Physiological Phenomena, Databases, Factual, Gene Expression Regulation, Internet, RNA, Signal Transduction, User-Computer Interface},
pages = {D674--679},
file = {Full Text:/home/lxu/Zotero/storage/QDZDTWCX/Schaefer et al. - 2009 - PID the Pathway Interaction Database.pdf:application/pdf}
}
@article{payton_drug_2003,
title = {Drug {Discovery} {Targeted} to the {Alzheimer}'s {APP} {mRNA} 5'-{Untranslated} {Region}: {The} {Action} of {Paroxetine} and {Dimercaptopropanol}},
volume = {20},
issn = {0895-8696},
shorttitle = {Drug {Discovery} {Targeted} to the {Alzheimer}'s {APP} {mRNA} 5'-{Untranslated} {Region}},
url = {http://link.springer.com/10.1385/JMN:20:3:267},
doi = {10.1385/JMN:20:3:267},
language = {en},
number = {3},
urldate = {2019-09-30},
journal = {Journal of Molecular Neuroscience},
author = {Payton, Sandra and Cahill, Catherine M. and Randall, Jeffrey D. and Gullans, Steven R. and Rogers, Jack T.},
year = {2003},
pages = {267--276}
}
@article{frederick_rapamycin_2015,
title = {Rapamycin {Ester} {Analog} {CCI}-779/{Temsirolimus} {Alleviates} {Tau} {Pathology} and {Improves} {Motor} {Deficit} in {Mutant} {Tau} {Transgenic} {Mice}},
volume = {44},
issn = {18758908, 13872877},
url = {http://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-142097},
doi = {10.3233/JAD-142097},
number = {4},
urldate = {2019-09-30},
journal = {Journal of Alzheimer's Disease},
author = {Frederick, Christelle and Ando, Kunie and Leroy, Karelle and Héraud, Céline and Suain, Valérie and Buée, Luc and Brion, Jean-Pierre},
month = feb,
year = {2015},
pages = {1145--1156}
}
@article{jiang_temsirolimus_2014,
title = {Temsirolimus promotes autophagic clearance of amyloid-β and provides protective effects in cellular and animal models of {Alzheimer}'s disease},
volume = {81},
issn = {10436618},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1043661814000218},
doi = {10.1016/j.phrs.2014.02.008},
language = {en},
urldate = {2019-09-30},
journal = {Pharmacological Research},
author = {Jiang, Teng and Yu, Jin-Tai and Zhu, Xi-Chen and Tan, Meng-Shan and Wang, Hui-Fu and Cao, Lei and Zhang, Qiao-Quan and Shi, Jian-Quan and Gao, Li and Qin, Hao and Zhang, Ying-Dong and Tan, Lan},
month = mar,
year = {2014},
pages = {54--63}
}
@article{hudes_temsirolimus_2007,
title = {Temsirolimus, {Interferon} {Alfa}, or {Both} for {Advanced} {Renal}-{Cell} {Carcinoma}},
volume = {356},
issn = {0028-4793, 1533-4406},
url = {http://www.nejm.org/doi/abs/10.1056/NEJMoa066838},
doi = {10.1056/NEJMoa066838},
language = {en},
number = {22},
urldate = {2019-09-30},
journal = {New England Journal of Medicine},
author = {Hudes, Gary and Carducci, Michael and Tomczak, Piotr and Dutcher, Janice and Figlin, Robert and Kapoor, Anil and Staroslawska, Elzbieta and Sosman, Jeffrey and McDermott, David and Bodrogi, István and Kovacevic, Zoran and Lesovoy, Vladimir and Schmidt-Wolf, Ingo G.H. and Barbarash, Olga and Gokmen, Erhan and O'Toole, Timothy and Lustgarten, Stephanie and Moore, Laurence and Motzer, Robert J.},
month = may,
year = {2007},
pages = {2271--2281}
}
@article{schmelzle_tor_2000,
title = {{TOR}, a central controller of cell growth},
volume = {103},
issn = {0092-8674},
doi = {10.1016/s0092-8674(00)00117-3},
abstract = {Cell growth (increase in cell mass) and cell proliferation (increase in cell number) are distinct yet coupled processes that go hand-in-hand to give rise to an organ, organism, or tumor. Cyclin-dependent kinase(s) is the central regulator of cell proliferation. Is there an equivalent regulator for cell growth? Recent findings reveal that the target of rapamycin TOR controls an unusually abundant and diverse set of readouts all of which are important for cell growth, suggesting that this conserved kinase is such a central regulator.},
language = {eng},
number = {2},
journal = {Cell},
author = {Schmelzle, T. and Hall, M. N.},
month = oct,
year = {2000},
pmid = {11057898},
keywords = {Signal Transduction, Cell Cycle Proteins, Cell Division, Fungal Proteins, Models, Biological, Phosphatidylinositol 3-Kinases, Phosphotransferases (Alcohol Group Acceptor), Protein Kinases, Saccharomyces cerevisiae Proteins, TOR Serine-Threonine Kinases},
pages = {253--262}
}
@article{jiang_temsirolimus_2014-1,
title = {Temsirolimus promotes autophagic clearance of amyloid-β and provides protective effects in cellular and animal models of {Alzheimer}'s disease},
volume = {81},
issn = {1096-1186},
doi = {10.1016/j.phrs.2014.02.008},
abstract = {Accumulation of amyloid-β peptides (Aβ) within brain is a major pathogenic hallmark of Alzheimer's disease (AD). Emerging evidence suggests that autophagy, an important intracellular catabolic process, is involved in Aβ clearance. Here, we investigated whether temsirolimus, a newly developed compound approved by Food and Drug Administration and European Medicines Agency for renal cell carcinoma treatment, would promote autophagic clearance of Aβ and thus provide protective effects in cellular and animal models of AD. HEK293 cells expressing the Swedish mutant of APP695 (HEK293-APP695) were treated with vehicle or 100nM temsirolimus for 24h in the presence or absence of 3-methyladenine (5mM) or Atg5-siRNA, and intracellular Aβ levels as well as autophagy biomarkers were measured. Meanwhile, APP/PS1 mice received intraperitoneal injection of temsirolimus (20mg/kg) every 2 days for 60 days, and brain Aβ burden, autophagy biomarkers, cellular apoptosis in hippocampus, and spatial cognitive functions were assessed. Our results showed that temsirolimus enhanced Aβ clearance in HEK293-APP695 cells and in brain of APP/PS1 mice in an autophagy-dependent manner. Meanwhile, temsirolimus attenuated cellular apoptosis in hippocampus of APP/PS1 mice, which was accompanied by an improvement in spatial learning and memory abilities. In conclusion, our study provides the first evidence that temsirolimus promotes autophagic Aβ clearance and exerts protective effects in cellular and animal models of AD, suggesting that temsirolimus administration may represent a new therapeutic strategy for AD treatment. Meanwhile, these findings emphasize the notion that many therapeutic agents possess pleiotropic actions aside from their main applications.},
language = {eng},
journal = {Pharmacological Research},
author = {Jiang, Teng and Yu, Jin-Tai and Zhu, Xi-Chen and Tan, Meng-Shan and Wang, Hui-Fu and Cao, Lei and Zhang, Qiao-Quan and Shi, Jian-Quan and Gao, Li and Qin, Hao and Zhang, Ying-Dong and Tan, Lan},
month = mar,
year = {2014},
pmid = {24602800},
keywords = {Humans, Antineoplastic Agents, TOR Serine-Threonine Kinases, 3-Methyladenine (PubChem CID: 1673), Alzheimer Disease, Alzheimer's disease, Amyloid beta-Peptides, Amyloid-β, Animals, Autophagy, Brain, Disease Models, Animal, HEK293 Cells, Male, Maze Learning, Memory, Mice, Transgenic, Neuroprotective Agents, Sirolimus, Spatial cognitive deficits, Temsirolimus, Temsirolimus (PubChem CID: 6918289)},
pages = {54--63}
}
@misc{lingling_xu_comaprison_workflow.png_2019,
title = {comaprison\_workflow.png},
copyright = {CC BY 4.0},
url = {https://figshare.com/articles/comaprison_workflow_png/9918533},
abstract = {This figure illustrates the comparison workflow to compare learned features and egineered features.{\textless}br{\textgreater}},
urldate = {2019-09-30},
author = {Lingling Xu},
year = {2019},
doi = {10.6084/m9.figshare.9918533},
keywords = {60102 Bioinformatics}
}
@misc{himmelstein_protein_2015,
title = {Daniel {Himmelstein}, {Sabrina} {Chen} (2015) {Protein} (target, carrier, transporter, and enzyme) interactions in {DrugBank}. {Thinklab}. doi:10.15363/thinklab.d65}
}
@incollection{kotz_spearman_2006,
address = {Hoboken, NJ, USA},
title = {Spearman {Correlation} {Coefficients}, {Differences} between},
isbn = {978-0-471-66719-3},
url = {http://doi.wiley.com/10.1002/0471667196.ess5050.pub2},
language = {en},
urldate = {2019-09-30},
booktitle = {Encyclopedia of {Statistical} {Sciences}},
publisher = {John Wiley \& Sons, Inc.},
author = {Myers, Leann and Sirois, Maria J.},
editor = {Kotz, Samuel and Read, Campbell B. and Balakrishnan, N. and Vidakovic, Brani and Johnson, Norman L.},
month = aug,
year = {2006},
doi = {10.1002/0471667196.ess5050.pub2},
pages = {ess5050.pub2}
}
@incollection{dagostino_wilcoxon_2008,
address = {Hoboken, NJ, USA},
title = {Wilcoxon {Signed}-{Rank} {Test}},
isbn = {978-0-471-46242-2},
url = {http://doi.wiley.com/10.1002/9780471462422.eoct979},
language = {en},
urldate = {2019-09-30},
booktitle = {Wiley {Encyclopedia} of {Clinical} {Trials}},
publisher = {John Wiley \& Sons, Inc.},
author = {Woolson, R. F.},
editor = {D'Agostino, Ralph B. and Sullivan, Lisa and Massaro, Joseph},
month = sep,
year = {2008},
doi = {10.1002/9780471462422.eoct979},
pages = {eoct979}
}
@article{plackett_karl_1983,
title = {Karl {Pearson} and the {Chi}-{Squared} {Test}},
volume = {51},
issn = {03067734},
url = {https://www.jstor.org/stable/1402731?origin=crossref},
doi = {10.2307/1402731},
number = {1},
urldate = {2019-09-29},
journal = {International Statistical Review / Revue Internationale de Statistique},
author = {Plackett, R. L.},
month = apr,
year = {1983},
pages = {59}
}
@book{acm_international_conference_on_information_and_knowledge_management_cikm_2009,
address = {New York, N.Y.},
title = {{CIKM} '09: proceedings of the {ACM} {Eighteenth} {Conference} on {Information} and {Knowledge} {Management}.},
isbn = {978-1-60558-512-3},
shorttitle = {{CIKM} '09},
language = {English},
publisher = {Association for Computing Machinery},
editor = {{ACM International Conference on Information and Knowledge Management} and {Association for Computing Machinery} and {Special Interest Group on Information Retrieval} and {Association for Computing Machinery} and Special Interest Group on Hypertext, Hypermedia {and} Web},
year = {2009},
note = {OCLC: 614185996}
}
@book{international_conference_on_knowledge_discovery_and_data_mining._<15_kdd09_2009,
address = {New York, NY},
title = {{KDD}'09 proceedings of the 15th {ACMKDD} {International} {Conference} on {Knowledge} {Discovery} \& {Data} {Mining}; {June} 28 - {July} 1, 2009, {Paris}, {France}.},
isbn = {978-1-60558-495-9},
language = {English},
publisher = {ACM},
editor = {International Conference on Knowledge Discovery {and} Data Mining. {\textless}15, Paris{\textgreater}, 2009 and {Association for Computing Machinery} and {Special Interest Group on Knowledge Discovery and Data Mining}},
year = {2009},
note = {OCLC: 699727169}
}
@article{von_luxburg_tutorial_2007,
title = {A tutorial on spectral clustering},
volume = {17},
issn = {0960-3174, 1573-1375},
url = {http://link.springer.com/10.1007/s11222-007-9033-z},
doi = {10.1007/s11222-007-9033-z},
language = {en},
number = {4},
urldate = {2019-09-29},
journal = {Statistics and Computing},
author = {von Luxburg, Ulrike},
month = dec,
year = {2007},
pages = {395--416},
file = {Submitted Version:/home/lxu/Zotero/storage/2DTZV3QZ/von Luxburg - 2007 - A tutorial on spectral clustering.pdf:application/pdf}
}
@article{rustenhoven_pu.1_2018,
title = {{PU}.1 regulates {Alzheimer}’s disease-associated genes in primary human microglia},
volume = {13},
issn = {1750-1326},
url = {https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-018-0277-1},
doi = {10.1186/s13024-018-0277-1},
language = {en},
number = {1},
urldate = {2019-09-29},
journal = {Molecular Neurodegeneration},
author = {Rustenhoven, Justin and Smith, Amy M. and Smyth, Leon C. and Jansson, Deidre and Scotter, Emma L. and Swanson, Molly E. V. and Aalderink, Miranda and Coppieters, Natacha and Narayan, Pritika and Handley, Renee and Overall, Chris and Park, Thomas I. H. and Schweder, Patrick and Heppner, Peter and Curtis, Maurice A. and Faull, Richard L. M. and Dragunow, Mike},
month = dec,
year = {2018},
pages = {44},
file = {Full Text:/home/lxu/Zotero/storage/KNX769JV/Rustenhoven et al. - 2018 - PU.1 regulates Alzheimer’s disease-associated gene.pdf:application/pdf}
}
@article{desimone_histone_2019,
title = {Histone {Deacetylase} {Inhibitors} as {Multitarget} {Ligands}: {New} {Players} in {Alzheimer}'s {Disease} {Drug} {Discovery}?},
volume = {14},
issn = {1860-7179, 1860-7187},
shorttitle = {Histone {Deacetylase} {Inhibitors} as {Multitarget} {Ligands}},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cmdc.201900174},
doi = {10.1002/cmdc.201900174},
language = {en},
number = {11},
urldate = {2019-09-29},
journal = {ChemMedChem},
author = {De Simone, Angela and Milelli, Andrea},
month = jun,
year = {2019},
pages = {1067--1073}
}
@article{bubna_vorinostat-overview_2015,
title = {Vorinostat-{An} {Overview}},
volume = {60},
issn = {1998-3611},
doi = {10.4103/0019-5154.160511},
abstract = {Vorinostat is a new drug used in the management of cutaneous T cell lymphoma when the disease persists, gets worse or comes back during or after treatment with other medicines. It is an efficacious and well tolerated drug and has been considered a novel drug in the treatment of this condition. Currently apart from cutaneous T cell lymphoma the role of Vorinostat for other types of cancers is being investigated both as mono-therapy and combination therapy.},
language = {eng},
number = {4},
journal = {Indian Journal of Dermatology},
author = {Bubna, Aditya Kumar},
month = aug,
year = {2015},
pmid = {26288427},
pmcid = {PMC4533557},
keywords = {Cutaneous T cell lymphoma, histone deacytelase inhibitor, Vorinostat},
pages = {419}
}
@article{marniemi_radiochemical_1975,
title = {Radiochemical assay of glutathione {S}-epoxide transferase and its enhancement by phenobarbital in rat liver in vivo},
volume = {24},
issn = {0006-2952},
doi = {10.1016/0006-2952(75)90080-5},
language = {eng},
number = {17},
journal = {Biochemical Pharmacology},
author = {Marniemi, J. and Parkki, M. G.},
month = sep,
year = {1975},
pmid = {9},
keywords = {Animals, Male, Carrier Proteins, Epoxy Compounds, Glutathione, Glutathione Transferase, Hydrogen-Ion Concentration, Liver, Methylcholanthrene, Phenobarbital, Rats, Stimulation, Chemical, Styrenes},
pages = {1569--1572}
}
@inproceedings{tang_scalable_2009,
location = {Hong Kong, China},
title = {Scalable learning of collective behavior based on sparse social dimensions},
isbn = {978-1-60558-512-3},
url = {http://portal.acm.org/citation.cfm?doid=1645953.1646094},
doi = {10.1145/1645953.1646094},
eventtitle = {Proceeding of the 18th {ACM} conference},
pages = {1107},
booktitle = {Proceeding of the 18th {ACM} conference on Information and knowledge management - {CIKM} '09},
publisher = {{ACM} Press},
author = {Tang, Lei and Liu, Huan},
urldate = {2019-11-07},
date = {2009},
langid = {english}
}
@inproceedings{tang_relational_2009,
location = {Paris, France},
title = {Relational learning via latent social dimensions},
isbn = {978-1-60558-495-9},
url = {http://portal.acm.org/citation.cfm?doid=1557019.1557109},
doi = {10.1145/1557019.1557109},
eventtitle = {the 15th {ACM} {SIGKDD} international conference},
pages = {817},
booktitle = {Proceedings of the 15th {ACM} {SIGKDD} international conference on Knowledge discovery and data mining - {KDD} '09},
publisher = {{ACM} Press},
author = {Tang, Lei and Liu, Huan},
urldate = {2019-11-07},
date = {2009},
langid = {english}
}
@article{Macskassy_a_2003,
title = {A Simple Relational Classifier},
url = {https://apps.dtic.mil/dtic/tr/fulltext/u2/a452802.pdf},
author = {Sofus A. Macskassy and Foster Provost}
}
@incollection{gangemi_knowledge_2018,
address = {Cham},
title = {Knowledge {Graph} {Embeddings} with node2vec for {Item} {Recommendation}},
volume = {11155},
isbn = {978-3-319-98191-8 978-3-319-98192-5},
url = {http://link.springer.com/10.1007/978-3-319-98192-5_22},
urldate = {2019-09-29},
booktitle = {The {Semantic} {Web}: {ESWC} 2018 {Satellite} {Events}},
publisher = {Springer International Publishing},
author = {Palumbo, Enrico and Rizzo, Giuseppe and Troncy, Raphaël and Baralis, Elena and Osella, Michele and Ferro, Enrico},
editor = {Gangemi, Aldo and Gentile, Anna Lisa and Nuzzolese, Andrea Giovanni and Rudolph, Sebastian and Maleshkova, Maria and Paulheim, Heiko and Pan, Jeff Z and Alam, Mehwish},
year = {2018},
doi = {10.1007/978-3-319-98192-5_22},
pages = {117--120}
}
@article{kuhn_side_2010,
title = {A side effect resource to capture phenotypic effects of drugs},
volume = {6},
issn = {1744-4292, 1744-4292},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1038/msb.2009.98},
doi = {10.1038/msb.2009.98},
language = {en},
number = {1},
urldate = {2019-09-29},
journal = {Molecular Systems Biology},
author = {Kuhn, Michael and Campillos, Monica and Letunic, Ivica and Jensen, Lars Juhl and Bork, Peer},
month = jan,
year = {2010},
pages = {343},
file = {Full Text:/home/lxu/Zotero/storage/GG3TME4J/Kuhn et al. - 2010 - A side effect resource to capture phenotypic effec.pdf:application/pdf}
}
@article{gottlieb_predict:_2011,
title = {{PREDICT}: a method for inferring novel drug indications with application to personalized medicine},
volume = {7},
issn = {1744-4292, 1744-4292},
shorttitle = {{PREDICT}},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1038/msb.2011.26},
doi = {10.1038/msb.2011.26},
language = {en},
number = {1},
urldate = {2019-09-29},
journal = {Molecular Systems Biology},
author = {Gottlieb, Assaf and Stein, Gideon Y and Ruppin, Eytan and Sharan, Roded},
month = jan,
year = {2011},
pages = {496},
file = {Full Text:/home/lxu/Zotero/storage/BQI9JEUS/Gottlieb et al. - 2011 - PREDICT a method for inferring novel drug indicat.pdf:application/pdf}
}
@article{mccoy_development_2012,
title = {Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications},
volume = {19},
issn = {1067-5027, 1527-974X},
shorttitle = {Development and evaluation of a crowdsourcing methodology for knowledge base construction},
url = {https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2012-000852},
doi = {10.1136/amiajnl-2012-000852},
language = {en},
number = {5},
urldate = {2019-09-29},
journal = {Journal of the American Medical Informatics Association},
author = {McCoy, Allison B and Wright, Adam and Laxmisan, Archana and Ottosen, Madelene J and McCoy, Jacob A and Butten, David and Sittig, Dean F},
month = sep,
year = {2012},
pages = {713--718},
file = {Full Text:/home/lxu/Zotero/storage/ERC66HC2/McCoy et al. - 2012 - Development and evaluation of a crowdsourcing meth.pdf:application/pdf}
}
@article{wei_development_2013,
title = {Development and evaluation of an ensemble resource linking medications to their indications},
volume = {20},
issn = {1067-5027, 1527-974X},
url = {https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2012-001431},
doi = {10.1136/amiajnl-2012-001431},
language = {en},
number = {5},
urldate = {2019-09-29},
journal = {Journal of the American Medical Informatics Association},
author = {Wei, Wei-Qi and Cronin, Robert M and Xu, Hua and Lasko, Thomas A and Bastarache, Lisa and Denny, Joshua C},
month = sep,
year = {2013},
pages = {954--961},
file = {Full Text:/home/lxu/Zotero/storage/QJFS7S2M/Wei et al. - 2013 - Development and evaluation of an ensemble resource.pdf:application/pdf}
}
@article{khare_labeledin:_2014,
title = {{LabeledIn}: {Cataloging} labeled indications for human drugs},
volume = {52},
issn = {15320464},
shorttitle = {{LabeledIn}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1532046414001853},
doi = {10.1016/j.jbi.2014.08.004},
language = {en},
urldate = {2019-09-29},
journal = {Journal of Biomedical Informatics},
author = {Khare, Ritu and Li, Jiao and Lu, Zhiyong},
month = dec,
year = {2014},
pages = {448--456},
file = {Accepted Version:/home/lxu/Zotero/storage/LFW73U3Y/Khare et al. - 2014 - LabeledIn Cataloging labeled indications for huma.pdf:application/pdf}
}
@article{priedigkeit_evolutionary_2015,
title = {Evolutionary {Signatures} amongst {Disease} {Genes} {Permit} {Novel} {Methods} for {Gene} {Prioritization} and {Construction} of {Informative} {Gene}-{Based} {Networks}},
volume = {11},
issn = {1553-7404},
url = {https://dx.plos.org/10.1371/journal.pgen.1004967},
doi = {10.1371/journal.pgen.1004967},
language = {en},
number = {2},
urldate = {2019-09-29},
journal = {PLOS Genetics},
author = {Priedigkeit, Nolan and Wolfe, Nicholas and Clark, Nathan L.},
editor = {Akey, Joshua M.},
month = feb,
year = {2015},
pages = {e1004967},
file = {Full Text:/home/lxu/Zotero/storage/UHZJB6ZP/Priedigkeit et al. - 2015 - Evolutionary Signatures amongst Disease Genes Perm.pdf:application/pdf}
}
@article{himmelstein_heterogeneous_2015,
title = {Heterogeneous {Network} {Edge} {Prediction}: {A} {Data} {Integration} {Approach} to {Prioritize} {Disease}-{Associated} {Genes}},
volume = {11},
issn = {1553-7358},
shorttitle = {Heterogeneous {Network} {Edge} {Prediction}},
url = {https://dx.plos.org/10.1371/journal.pcbi.1004259},
doi = {10.1371/journal.pcbi.1004259},
language = {en},
number = {7},
urldate = {2019-09-29},
journal = {PLOS Computational Biology},
author = {Himmelstein, Daniel S. and Baranzini, Sergio E.},
editor = {Tang, Hua},
month = jul,
year = {2015},
pages = {e1004259},
file = {Full Text:/home/lxu/Zotero/storage/6ZIA468J/Himmelstein and Baranzini - 2015 - Heterogeneous Network Edge Prediction A Data Inte.pdf:application/pdf}
}
@article{menche_uncovering_2015,
title = {Uncovering disease-disease relationships through the incomplete interactome},
volume = {347},
issn = {0036-8075, 1095-9203},
url = {http://www.sciencemag.org/cgi/doi/10.1126/science.1257601},
doi = {10.1126/science.1257601},
language = {en},
number = {6224},
urldate = {2019-09-29},
journal = {Science},
author = {Menche, J. and Sharma, A. and Kitsak, M. and Ghiassian, S. D. and Vidal, M. and Loscalzo, J. and Barabasi, A.-L.},
month = feb,
year = {2015},
pages = {1257601--1257601},
file = {Accepted Version:/home/lxu/Zotero/storage/ARBFKIAW/Menche et al. - 2015 - Uncovering disease-disease relationships through t.pdf:application/pdf}
}
@incollection{bairoch_bgee:_2008,
address = {Berlin, Heidelberg},
title = {Bgee: {Integrating} and {Comparing} {Heterogeneous} {Transcriptome} {Data} {Among} {Species}},
volume = {5109},
isbn = {978-3-540-69827-2 978-3-540-69828-9},
shorttitle = {Bgee},
url = {http://link.springer.com/10.1007/978-3-540-69828-9_12},
language = {en},
urldate = {2019-09-29},
booktitle = {Data {Integration} in the {Life} {Sciences}},
publisher = {Springer Berlin Heidelberg},
author = {Bastian, Frederic and Parmentier, Gilles and Roux, Julien and Moretti, Sebastien and Laudet, Vincent and Robinson-Rechavi, Marc},
editor = {Bairoch, Amos and Cohen-Boulakia, Sarah and Froidevaux, Christine},
year = {2008},
doi = {10.1007/978-3-540-69828-9_12},
pages = {124--131},
file = {Submitted Version:/home/lxu/Zotero/storage/WYZ3CCXB/Bastian et al. - 2008 - Bgee Integrating and Comparing Heterogeneous Tran.pdf:application/pdf}
}
@article{pinero_disgenet:_2015,
title = {{DisGeNET}: a discovery platform for the dynamical exploration of human diseases and their genes},
volume = {2015},
issn = {1758-0463},
shorttitle = {{DisGeNET}},
doi = {10.1093/database/bav028},
abstract = {DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380,000 associations between {\textgreater}16,000 genes and 13,000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/},
language = {eng},
journal = {Database: The Journal of Biological Databases and Curation},
author = {Piñero, Janet and Queralt-Rosinach, Núria and Bravo, Àlex and Deu-Pons, Jordi and Bauer-Mehren, Anna and Baron, Martin and Sanz, Ferran and Furlong, Laura I.},
year = {2015},
pmid = {25877637},
pmcid = {PMC4397996},
keywords = {Humans, Internet, User-Computer Interface, Animals, Disease Models, Animal, Cloud Computing, Databases, Genetic, Gene Regulatory Networks, Genetic Diseases, Inborn, Genome, Human},
pages = {bav028},
file = {Full Text:/home/lxu/Zotero/storage/8I455KL8/Piñero et al. - 2015 - DisGeNET a discovery platform for the dynamical e.pdf:application/pdf}
}
@article{rogers_toward_1963,
title = {Toward a {Science} of the {Person}},
volume = {3},
issn = {0022-1678, 1552-650X},
url = {http://journals.sagepub.com/doi/10.1177/002216786300300208},
doi = {10.1177/002216786300300208},
language = {en},
number = {2},
urldate = {2019-09-29},
journal = {Journal of Humanistic Psychology},
author = {Rogers, Carl R.},
month = apr,
year = {1963},
pages = {72--92}
}
@article{ursu_drugcentral_2019,
title = {{DrugCentral} 2018: an update},
volume = {47},
issn = {0305-1048, 1362-4962},
shorttitle = {{DrugCentral} 2018},
url = {https://academic.oup.com/nar/article/47/D1/D963/5146206},
doi = {10.1093/nar/gky963},
language = {en},
number = {D1},
urldate = {2019-09-29},
journal = {Nucleic Acids Research},
author = {Ursu, Oleg and Holmes, Jayme and Bologa, Cristian G and Yang, Jeremy J and Mathias, Stephen L and Stathias, Vasileios and Nguyen, Dac-Trung and Schürer, Stephan and Oprea, Tudor},
month = jan,
year = {2019},
pages = {D963--D970},
file = {Full Text:/home/lxu/Zotero/storage/YGKFKJWS/Ursu et al. - 2019 - DrugCentral 2018 an update.pdf:application/pdf}
}
@misc{himmelstein_dhimmel/pathways_2016,
title = {Dhimmel/{Pathways} {V}2.0: {Compiling} {Human} {Pathway} {Gene} {Sets}},
copyright = {Open Access},
shorttitle = {Dhimmel/{Pathways} {V}2.0},
url = {https://zenodo.org/record/48810},
abstract = {This release compiles 1,862 pathway gene sets (1,341 from Reactome, 298 from WikiPathways, and 223 from the PID). Reactome and the PID were retrieved via Pathway Commons v7. Compared to the previous release (dhimmel/pathways v1.1), this release removes MSigDB pathways due to licensing issues.},
urldate = {2019-09-29},
publisher = {Zenodo},
author = {Himmelstein, Daniel S. and Pico, Alexander R.},
month = apr,
year = {2016},
doi = {10.5281/zenodo.48810},
keywords = {gene set, pathways, PID, Reactome, Rephetio, WikiPathways}
}
@misc{himmelstein_user-friendly_2016,
title = {User-{Friendly} {Extensions} {To} {Mesh} {V}1.0},
copyright = {Open Access},
url = {https://zenodo.org/record/45586},
abstract = {The Medical Subject Headings (MeSH) is a controled vocabulary produced by the NLM for cataloging biomedical information. The resource is structured as an ontology and is used for PubMed/MEDLINE annotation. Here we provide user-friendly datasets derived from MeSH. Currently, two record types are processed: Descriptors and Supplementary Concept Records. The datasets are built from the 2015 MeSH release retrieved on 2015-05-09 for Descriptors and 2015-05-25 for SCRs.},
urldate = {2019-09-29},
publisher = {Zenodo},
author = {Himmelstein, Daniel S.},
month = feb,
year = {2016},
doi = {10.5281/zenodo.45586},
keywords = {Rephetio, Medical Subject Headings, MeSH, Symptoms}
}
@article{mungall_uberon_2012,
title = {Uberon, an integrative multi-species anatomy ontology},
volume = {13},
issn = {1465-6906},
url = {http://genomebiology.biomedcentral.com/articles/10.1186/gb-2012-13-1-r5},
doi = {10.1186/gb-2012-13-1-r5},
language = {en},
number = {1},
urldate = {2019-09-29},
journal = {Genome Biology},
author = {Mungall, Christopher J and Torniai, Carlo and Gkoutos, Georgios V and Lewis, Suzanna E and Haendel, Melissa A},
year = {2012},
pages = {R5},
file = {Full Text:/home/lxu/Zotero/storage/CWSV5C9N/Mungall et al. - 2012 - Uberon, an integrative multi-species anatomy ontol.pdf:application/pdf}
}
@article{law_drugbank_2014,
title = {{DrugBank} 4.0: shedding new light on drug metabolism},
volume = {42},
issn = {0305-1048, 1362-4962},
shorttitle = {{DrugBank} 4.0},
url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkt1068},
doi = {10.1093/nar/gkt1068},
language = {en},
number = {D1},
urldate = {2019-09-29},
journal = {Nucleic Acids Research},
author = {Law, Vivian and Knox, Craig and Djoumbou, Yannick and Jewison, Tim and Guo, An Chi and Liu, Yifeng and Maciejewski, Adam and Arndt, David and Wilson, Michael and Neveu, Vanessa and Tang, Alexandra and Gabriel, Geraldine and Ly, Carol and Adamjee, Sakina and Dame, Zerihun T. and Han, Beomsoo and Zhou, You and Wishart, David S.},
month = jan,
year = {2014},
pages = {D1091--D1097},
file = {Full Text:/home/lxu/Zotero/storage/W8NRLQXE/Law et al. - 2014 - DrugBank 4.0 shedding new light on drug metabolis.pdf:application/pdf}
}
@article{schriml_disease_2012,
title = {Disease {Ontology}: a backbone for disease semantic integration},
volume = {40},
issn = {0305-1048, 1362-4962},
shorttitle = {Disease {Ontology}},
url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkr972},
doi = {10.1093/nar/gkr972},
language = {en},
number = {D1},
urldate = {2019-09-29},
journal = {Nucleic Acids Research},
author = {Schriml, L. M. and Arze, C. and Nadendla, S. and Chang, Y.-W. W. and Mazaitis, M. and Felix, V. and Feng, G. and Kibbe, W. A.},
month = jan,
year = {2012},
pages = {D940--D946},
file = {Full Text:/home/lxu/Zotero/storage/R6QCFB8R/Schriml et al. - 2012 - Disease Ontology a backbone for disease semantic .pdf:application/pdf}
}
@article{maglott_entrez_2011,
title = {Entrez {Gene}: gene-centered information at {NCBI}},
volume = {39},
issn = {1362-4962},
shorttitle = {Entrez {Gene}},
doi = {10.1093/nar/gkq1237},
abstract = {Entrez Gene (http://www.ncbi.nlm.nih.gov/gene) is National Center for Biotechnology Information (NCBI)'s database for gene-specific information. Entrez Gene maintains records from genomes which have been completely sequenced, which have an active research community to submit gene-specific information, or which are scheduled for intense sequence analysis. The content represents the integration of curation and automated processing from NCBI's Reference Sequence project (RefSeq), collaborating model organism databases, consortia such as Gene Ontology and other databases within NCBI. Records in Entrez Gene are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, genomic location, gene products and their attributes, markers, phenotypes and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is available via interactive browsing through NCBI's Entrez system, via NCBI's Entrez programming utilities (E-Utilities) and for bulk transfer by FTP.},
language = {eng},
number = {Database issue},
journal = {Nucleic Acids Research},
author = {Maglott, Donna and Ostell, Jim and Pruitt, Kim D. and Tatusova, Tatiana},
month = jan,
year = {2011},
pmid = {21115458},
pmcid = {PMC3013746},
keywords = {United States, Internet, User-Computer Interface, Databases, Genetic, Genes, Genomics, National Library of Medicine (U.S.)},
pages = {D52--57},
file = {Full Text:/home/lxu/Zotero/storage/VP9RCQ5X/Maglott et al. - 2011 - Entrez Gene gene-centered information at NCBI.pdf:application/pdf}
}
@techreport{muslu_guiltytargets:_2019,
type = {preprint},
title = {{GuiltyTargets}: {Prioritization} of {Novel} {Therapeutic} {Targets} with {Deep} {Network} {Representation} {Learning}},
shorttitle = {{GuiltyTargets}},
url = {http://biorxiv.org/lookup/doi/10.1101/521161},
abstract = {The majority of clinical trial failures are caused by low efficacy of investigated drugs, often due to a poor choice of target protein. Computational prioritization approaches aim to support target selection by ranking candidate targets in the context of a given disease. We propose a novel target prioritization approach, GuiltyTargets, which relies on deep network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled machine learning for candidate ranking. We evaluated our approach on six diseases of different types (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.94, significantly outperforming a previous approach, which relies on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. Applying GuiltyTargets to Alzheimer's disease resulted into a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning of our method. Availability: The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets.},
language = {en},
urldate = {2019-09-29},
institution = {Bioinformatics},
author = {Muslu, Oezlem and Hoyt, Charles Tapley and Hofmann-Apitius, Martin and Froehlich, Holger},
month = jan,
year = {2019},
doi = {10.1101/521161}
}
@article{ayers_snp_2010,
title = {{SNP} {Selection} in genome-wide and candidate gene studies via penalized logistic regression},
volume = {34},
issn = {07410395},
url = {http://doi.wiley.com/10.1002/gepi.20543},
doi = {10.1002/gepi.20543},
language = {en},
number = {8},
urldate = {2019-09-29},
journal = {Genetic Epidemiology},
author = {Ayers, Kristin L. and Cordell, Heather J.},
month = dec,
year = {2010},
pages = {879--891},
file = {Full Text:/home/lxu/Zotero/storage/55REDDMH/Ayers and Cordell - 2010 - SNP Selection in genome-wide and candidate gene st.pdf:application/pdf}
}
@book{acm_special_interest_group_on_knowledge_discovery_in_data_proceedings_2014,
title = {Proceedings of the 20th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining.},
isbn = {978-1-4503-2956-9},
language = {English},
editor = {{ACM Special Interest Group on Knowledge Discovery in Data} and {Association for Computing Machinery} and {Special Interest Group on Management of Data}},
year = {2014},
note = {OCLC: 994251388}
}
@article{pearson_problem_1905,
title = {The {Problem} of the {Random} {Walk}},
volume = {72},
issn = {0028-0836, 1476-4687},
url = {http://www.nature.com/articles/072294b0},
doi = {10.1038/072294b0},
language = {en},
number = {1865},
urldate = {2019-09-29},
journal = {Nature},
author = {Pearson, Karl},
month = jul,
year = {1905},
pages = {294--294}
}
@article{rong_word2vec_2014,
title = {word2vec {Parameter} {Learning} {Explained}},
url = {http://arxiv.org/abs/1411.2738},
abstract = {The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. As an increasing number of researchers would like to experiment with word2vec or similar techniques, I notice that there lacks a material that comprehensively explains the parameter learning process of word embedding models in details, thus preventing researchers that are non-experts in neural networks from understanding the working mechanism of such models. This note provides detailed derivations and explanations of the parameter update equations of the word2vec models, including the original continuous bag-of-word (CBOW) and skip-gram (SG) models, as well as advanced optimization techniques, including hierarchical softmax and negative sampling. Intuitive interpretations of the gradient equations are also provided alongside mathematical derivations. In the appendix, a review on the basics of neuron networks and backpropagation is provided. I also created an interactive demo, wevi, to facilitate the intuitive understanding of the model.},
urldate = {2019-09-29},
journal = {arXiv:1411.2738 [cs]},
author = {Rong, Xin},
month = nov,
year = {2014},
note = {arXiv: 1411.2738},
keywords = {Computer Science - Computation and Language},
file = {arXiv\:1411.2738 PDF:/home/lxu/Zotero/storage/QWMPDKXP/Rong - 2014 - word2vec Parameter Learning Explained.pdf:application/pdf;arXiv.org Snapshot:/home/lxu/Zotero/storage/N9EZZVVT/1411.html:text/html}
}
@book{matwin_proceedings_2017,
address = {New York, NY},
title = {Proceedings of the 23rd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
isbn = {978-1-4503-4887-4},
language = {English},
publisher = {ACM},
author = {Matwin, Stan and {Association for Computing Machinery-Digital Library} and {ACM Special Interest Group on Knowledge Discovery in Data} and {ACM Special Interest Group on Management of Data}},
year = {2017},
note = {OCLC: 1043860012}
}
@article{sheikh_gat2vec:_2019,
title = {gat2vec: representation learning for attributed graphs},
volume = {101},
issn = {0010-485X, 1436-5057},
shorttitle = {gat2vec},
url = {http://link.springer.com/10.1007/s00607-018-0622-9},
doi = {10.1007/s00607-018-0622-9},
language = {en},
number = {3},
urldate = {2019-09-29},
journal = {Computing},
author = {Sheikh, Nasrullah and Kefato, Zekarias and Montresor, Alberto},
month = mar,
year = {2019},
pages = {187--209}
}
@book{acm_special_interest_group_on_knowledge_discovery_in_data_proceedings_2014-1,
title = {Proceedings of the 20th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining.},
isbn = {978-1-4503-2956-9},
language = {English},
editor = {{ACM Special Interest Group on Knowledge Discovery in Data} and {Association for Computing Machinery} and {Special Interest Group on Management of Data}},
year = {2014},
note = {OCLC: 994251388}
}
@article{gao_edge2vec:_2018,
title = {edge2vec: {Representation} learning using edge semantics for biomedical knowledge discovery},
shorttitle = {edge2vec},
url = {http://arxiv.org/abs/1809.02269},
abstract = {Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, {\textbackslash}textbf\{edge2vec\}{\textbackslash} significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.},
urldate = {2019-09-29},
journal = {arXiv:1809.02269 [cs]},
author = {Gao, Zheng and Fu, Gang and Ouyang, Chunping and Tsutsui, Satoshi and Liu, Xiaozhong and Yang, Jeremy and Gessner, Christopher and Foote, Brian and Wild, David and Yu, Qi and Ding, Ying},
month = sep,
year = {2018},
note = {arXiv: 1809.02269},
keywords = {Computer Science - Information Retrieval},
annote = {Comment: 10 pages},
file = {arXiv\:1809.02269 PDF:/home/lxu/Zotero/storage/RLKYAPI4/Gao et al. - 2018 - edge2vec Representation learning using edge seman.pdf:application/pdf;arXiv.org Snapshot:/home/lxu/Zotero/storage/MMMQVNFX/1809.html:text/html}
}
@book{association_for_computing_machinery_proceedings_2016,
title = {Proceedings of the 22nd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}.},
isbn = {978-1-4503-4232-2},
language = {English},
editor = {{Association for Computing Machinery} and {Special Interest Group on Management of Data} and {ACM Special Interest Group on Knowledge Discovery in Data}},
year = {2016},
note = {OCLC: 994252363}
}
@article{mikolov_efficient_2013,
title = {Efficient {Estimation} of {Word} {Representations} in {Vector} {Space}},
url = {http://arxiv.org/abs/1301.3781},
abstract = {We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.},
urldate = {2019-09-29},
journal = {arXiv:1301.3781 [cs]},
author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
month = jan,
year = {2013},
note = {arXiv: 1301.3781},
keywords = {Computer Science - Computation and Language},
file = {arXiv\:1301.3781 PDF:/home/lxu/Zotero/storage/J2NMRMG2/Mikolov et al. - 2013 - Efficient Estimation of Word Representations in Ve.pdf:application/pdf;arXiv.org Snapshot:/home/lxu/Zotero/storage/49THESH3/1301.html:text/html}
}
@article{himmelstein_systematic_2017,
title = {Systematic integration of biomedical knowledge prioritizes drugs for repurposing},
volume = {6},
issn = {2050-084X},
url = {https://elifesciences.org/articles/26726},
doi = {10.7554/eLife.26726},
abstract = {The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
,
Of all the data in the world today, 90\% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context.
In biomedical research, data is largely fragmented and stored in databases that typically do not “talk” to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition.