SPOKE: Related Papers

← Update Dec 5 2018

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Köhler S, Carmody L, Vasilevsky N, et al. Nucleic Acids Res. 2018 Nov 22. doi: 10.1093/nar/gky1105. [Epub ahead of print] PMID: 30476213
Website: www.human-phenotype-ontology.org

HumanNet v2: human gene networks for disease research. Hwang S, Kim CY, Yang S, Kim E, Hart T, Marcotte EM, Lee I. Nucleic Acids Res. 2018 Nov 10. doi: 10.1093/nar/gky1126. [Epub ahead of print] PMID: 30418591
Database: https://www.inetbio.org/humannet/

NTSHMDA: Prediction of Human Microbe-Disease Association based on Random Walk by Integrating Network Topological Similarity. Luo J, Long Y. IEEE/ACM Trans Comput Biol Bioinform. 2018 Nov 23. doi: 10.1109/TCBB.2018.2883041. [Epub ahead of print] PMID: 30489271

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network. Xiao X, Zhu W, Liao B, Xu J, Gu C, Ji B, Yao Y, Peng L, Yang J. Front Genet. 2018 Oct 16;9:411. doi: 10.3389/fgene.2018.00411. PMID: 30459803

PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations. Lei X, Fang Z, Chen L, Wu FX. Int J Mol Sci. 2018 Oct 31;19(11). pii: E3410. doi: 10.3390/ijms19113410. PMID: 30384427

SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Wu Y, Zhang F, Yang K, Fang S, Bu D, Li H, Sun L, Hu H, Gao K, Wang W, Zhou X, Zhao Y, Chen J. Nucleic Acids Res. 2018 Oct 31. doi: 10.1093/nar/gky1021. [Epub ahead of print] PMID: 30380087
Database: http://www.symmap.org/

Network-Based Methods for Prediction of Drug-Target Interactions. Wu Z, Li W, Liu G, Tang Y. Front Pharmacol. 2018 Oct 9;9:1134. doi: 10.3389/fphar.2018.01134. Review. PMID: 30356768

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes. Alshahrani M, Hoehndorf R. Bioinformatics. 2018 Sep 1;34(17):i901-i907. doi: 10.1093/bioinformatics/bty559. PMID: 30423077
https://github.com/bio-ontology-research-group/SmuDGE

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies. Prinz J, Koohi-Moghadam M, Sun H, Kocher JA, Wang J. Hum Hered. 2018 Oct 22;83(2):79-91. doi: 10.1159/000492574. [Epub ahead of print] PMID: 30347404

Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain. Alobaidi M, Malik KM, Hussain M. Comput Methods Programs Biomed. 2018 Oct;165:117-128. doi: 10.1016/j.cmpb.2018.08.010. PMID: 30337066

← Update Oct 17 2018

Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches. Shen F, Liu S, Wang Y, Wen A, Wang L, Liu H. JMIR Med Inform. 2018 Oct 10;6(4):e11301. doi: 10.2196/11301. PMID: 30305261

A new chemoinformatics approach with improved strategies for effective predictions of potential drugs. Hao M, Bryant SH, Wang Y. J Cheminform. 2018 Oct 11;10(1):50. doi: 10.1186/s13321-018-0303-x. PMID: 30311095

Drug Repositioning to Accelerate Drug Development Using Social Media Data: Computational Study on Parkinson Disease. Zhao M, Yang CC. J Med Internet Res. 2018 Oct 11;20(10):e271. doi: 10.2196/jmir.9646. PMID: 30309833

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis. Mortezaei Z, Cazier JB, Mehrabi AA, Cheng C, Masoudi-Nejad A. J Cell Biochem. 2018 Oct 9. doi: 10.1002/jcb.27825. [Epub ahead of print] PMID: 30302804

A novel approach for drug response prediction in cancer cell lines via network representation learning. Yang J, Li A, Li Y, Guo X, Wang M. Bioinformatics. 2018 Oct 10. doi: 10.1093/bioinformatics/bty848. [Epub ahead of print] PMID: 30304378
Code: https://github.com/USTC-HIlab/NRL2DRP
(these guys keep coming up with methods with unpronounceable acronyms, see earlier paper on HNMDRP)

Identifying communities from multiplex biological networks by randomized optimization of modularity. Didier G, Valdeolivas A, Baudot A. F1000Res. 2018 Jul 10;7:1042. doi: 10.12688/f1000research.15486.1. PMID: 30210790
Code: https://github.com/gilles-didier/MolTi-DREAM

Analyzing the disease module associated with osteosarcoma via a network- and pathway-based approach. Zhang Y, Yang F. Exp Ther Med. 2018 Sep;16(3):2584-2592. doi: 10.3892/etm.2018.6506. PMID: 30210606

Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine. Fiscon G, Conte F, Farina L, Paci P. Genes (Basel). 2018 Aug 31;9(9). pii: E437. doi: 10.3390/genes9090437. Review. PMID: 30200360

Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case-control approaches. Gomez-Rubio P, Piñero J, Molina-Montes E, Gutiérrez-Sacristán A, Marquez M, Rava M, Michalski CW, Farré A, Molero X, Löhr M, Perea J, Greenhalf W, O'Rorke M, Tardón A, Gress T, Barberà VM, Crnogorac-Jurcevic T, Muñoz-Bellvís L, Domínguez-Muñoz E, Balsells J, Costello E, Yu J, Iglesias M, Ilzarbe L, Kleeff J, Kong B, Mora J, Murray L, O'Driscoll D, Poves I, Lawlor RT, Ye W, Hidalgo M, Scarpa A, Sharp L, Carrato A, Real FX, Furlong LI, Malats N; PanGenEU Study Investigators. Int J Cancer. 2018 Sep 19. doi: 10.1002/ijc.31866. [Epub ahead of print] PMID: 30229903

Single-platform 'multi-omic' profiling: unified mass spectrometry and computational workflows for integrative proteomics-metabolomics analysis. Blum BC, Mousavi F, Emili A. Mol Omics. 2018 Sep 13. doi: 10.1039/c8mo00136g. [Epub ahead of print] Review. PMID: 30211418

HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1. [Epub ahead of print] PMID: 30197419
Website (NHLBI and CDC): https://phgkb.cdc.gov/PHGKB/specificPHGKB.action?topic=HLBS&query=home

← Update Aug 29 2018

Prediction of Drug-Gene Interaction by Using Metapath2vec. Zhu S, Bing J, Min X, Lin C, Zeng X. Front Genet. 2018 Jul 31;9:248. doi: 10.3389/fgene.2018.00248. PMID: 30108606

Network-based drug repositioning: A novel strategy for discovering potential antidepressants and their mode of action. Zhang TT, Xue R, Wang X, Zhao SW, An L, Li YF, Zhang YZ, Li S. Eur Neuropsychopharmacol. 2018 Aug 4. pii: S0924-977X(18)30267-0. doi: 10.1016/j.euroneuro.2018.07.096. [Epub ahead of print] PMID: 30087074

Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance. Yang CC, Yang H. Artif Intell Med. 2018 Aug 6. pii: S0933-3657(17)30037-4. doi: 10.1016/j.artmed.2018.07.002. [Epub ahead of print] PMID: 30093253

Computational drug repurposing to predict approved and novel drug-disease associations. Khalid Z, Sezerman OU. J Mol Graph Model. 2018 Aug 14;85:91-96. doi: 10.1016/j.jmgm.2018.08.005. [Epub ahead of print] PMID: 30130693

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework. Dragomir A, Vrahatis A, Bezerianos A. IEEE J Biomed Health Inform. 2018 Aug 3. doi: 10.1109/JBHI.2018.2863202. [Epub ahead of print] PMID: 30080151

Network Propagation Predicts Drug Synergy in Cancers. Li H, Li T, Quang D, Guan Y. Cancer Res. 2018 Jul 27. pii: canres.0740.2018. doi: 10.1158/0008-5472.CAN-18-0740. [Epub ahead of print] PMID: 30054332

Random Walk with Restart on Multiplex and Heterogeneous Biological Networks. Valdeolivas A, Tichit L, Navarro C, Perrin S, Odelin G, Levy N, Cau P, Remy E, Baudot A. Bioinformatics. 2018 Jul 18. doi: 10.1093/bioinformatics/bty637. [Epub ahead of print] PMID: 30020411
R package: http://bioconductor.org/packages/release/bioc/html/RandomWalkRestartMH.html
Code: https://github.com/alberto-valdeolivas/RWR-MH

Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis. Garcia-Vaquero ML, Gama-Carvalho M, Rivas JL, Pinto FR. Sci Rep. 2018 Aug 1;8(1):11555. doi: 10.1038/s41598-018-29990-7. PMID: 30068933
R package: https://github.com/frpinto/S2B

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction. Chen X, Qu J, Yin J. Front Genet. 2018 Jul 3;9:234. doi: 10.3389/fgene.2018.00234. PMID: 30018632

Disease Gene Classification with Metagraph Representations. Ata SK, Fang Y, Wu M, Li XL, Xiao X. Methods Mol Biol. 2018;1807:211-224. doi: 10.1007/978-1-4939-8561-6_16. PMID: 30030814

Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge. Zhou H, Ning S, Yang Y, Liu Z, Lang C, Lin Y. J Biomed Inform. 2018 Jul 11. pii: S1532-0464(18)30133-3. doi: 10.1016/j.jbi.2018.07.007. [Epub ahead of print] PMID: 30017973

← Update Jul 18 2018

Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological and Network Knowledge. Ding P, Yin R, Luo J, Kwoh CK. IEEE J Biomed Health Inform. 2018 Jul 2. doi: 10.1109/JBHI.2018.2852274. [Epub ahead of print] PMID: 29994408
Code: https://github.com/KDDing/EPSDC

Drug Response Prediction by Globally Capturing Drug and Cell Line Information in a Heterogeneous Network. Le DH, Pham VH. J Mol Biol. 2018 Jun 29. pii: S0022-2836(18)30697-1. doi: 10.1016/j.jmb.2018.06.041. [Epub ahead of print] PMID: 29966608

Disease classification: from phenotypic similarity to integrative genomics and beyond. Dozmorov MG. Brief Bioinform. 2018 Jun 22. doi: 10.1093/bib/bby049. [Epub ahead of print] PMID: 29939197

Identifying "Many-to-Many" Relationships Between Gene-Expression Data and Drug-Response Data Via Sparse Binary Matching. Cai J, Cai H, Chen J, Yang X. IEEE/ACM Trans Comput Biol Bioinform. 2018 Jun 22. doi: 10.1109/TCBB.2018.2849708. [Epub ahead of print] PMID: 29994482

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors. Alvarez MJ, Subramaniam PS, Tang LH, Grunn A, Aburi M, et al. Nat Genet. 2018 Jun 18. doi: 10.1038/s41588-018-0138-4. [Epub ahead of print] PMID: 29915428

Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed. Kveler K, Starosvetsky E, Ziv-Kenet A, Kalugny Y, Gorelik Y, Shalev-Malul G, Aizenbud-Reshef N, Dubovik T, Briller M, Campbell J, Rieckmann JC, Asbeh N, Rimar D, Meissner F, Wiser J, Shen-Orr SS. Nat Biotechnol. 2018 Jun 18. doi: 10.1038/nbt.4152. [Epub ahead of print] PMID: 29912209
ImmuneXpresso Knowledgebase: http://www.immunexpresso.org

Network-based approach to prediction and population-based validation of in silico drug repurposing. Cheng F, Desai RJ, Handy DE, Wang R, Schneeweiss S, Barabási AL, Loscalzo J. Nat Commun. 2018 Jul 12;9(1):2691. doi: 10.1038/s41467-018-05116-5. PMID: 30002366

Predicting perturbation patterns from the topology of biological networks. Santolini M, Barabási AL. Proc Natl Acad Sci U S A. 2018 Jul 3;115(27):E6375-E6383. doi: 10.1073/pnas.1720589115. PMID: 29925605

Inferring potential small molecule-miRNA association based on triple layer heterogeneous network. Qu J, Chen X, Sun YZ, Li JQ, Ming Z. J Cheminform. 2018 Jun 26;10(1):30. doi: 10.1186/s13321-018-0284-9. PMID: 29943160

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods. Iuliano A, Occhipinti A, Angelini C, De Feis I, Liò P. Front Genet. 2018 Jun 14;9:206. doi: 10.3389/fgene.2018.00206. PMID: 29963073

Predictive Systems Toxicology. Kiani NA, Shang MM, Zenil H, Tegner J. Methods Mol Biol. 2018;1800:535-557. doi: 10.1007/978-1-4939-7899-1_25. PMID: 29934910

Identification of drug-target interaction by a random walk with restart method on an interactome network. Lee I, Nam H. BMC Bioinformatics. 2018 Jun 13;19(Suppl 8):208. doi: 10.1186/s12859-018-2199-x. PMID: 29897326

Stratification of candidate genes for Parkinson's disease using weighted protein-protein interaction network analysis. Ferrari R, Kia DA, Tomkins JE, Hardy J, Wood NW, Lovering RC, Lewis PA, Manzoni C. BMC Genomics. 2018 Jun 13;19(1):452. doi: 10.1186/s12864-018-4804-9. PMID: 29898659

Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity. Yu L, Zhao J, Gao L. Int J Biol Sci. 2018 May 22;14(8):971-982. doi: 10.7150/ijbs.23350. PMID: 29989066

Constructing Disease Similarity Networks Based on Disease Module Theory. Ni P, Wang J, Zhong P, Li Y, Wu F, Pan Y. IEEE/ACM Trans Comput Biol Bioinform. 2018 Mar 21. doi: 10.1109/TCBB.2018.2817624. [Epub ahead of print] PMID: 29993782

Human pathway-based disease network. Yu L, Gao L. IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov 17. doi: 10.1109/TCBB.2017.2774802. [Epub ahead of print] PMID: 29990107

Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data. Luo P, Tian LP, Ruan J, Wu F. IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov 7. doi: 10.1109/TCBB.2017.2770120. [Epub ahead of print] PMID: 29990218

← Update Jun 13 2018

Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning. Wu M, Zeng W, Liu W, Lv H, Chen T, Jiang R. Methods. 2018 Jun 3. pii: S1046-2023(17)30494-2. doi: 10.1016/j.ymeth.2018.06.002. [Epub ahead of print] PMID: 29874547
Code: https://github.com/wmmthu/REGENT

Chemical-induced phenotypes at CTD help inform the pre-disease state and construct adverse outcome pathways. Davis AP, Wiegers TC, Wiegers J, Johnson RJ, Sciaky D, Grondin CJ, Mattingly CJ. Toxicol Sci. 2018 May 28. doi: 10.1093/toxsci/kfy131. [Epub ahead of print] PMID: 29846728
Chemical Toxogenomics Database (CTD): http://ctdbase.org
data source? includes >165,000 interactions from >90,000 publications, connecting >6,400 chemicals to 3,900 phenotypes for 760 anatomical terms in 215 species

Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications. Wang RS, Loscalzo J. J Mol Biol. 2018 May 20. pii: S0022-2836(18)30427-3. doi: 10.1016/j.jmb.2018.05.016. [Epub ahead of print] PMID: 29791871

Detection of multiple perturbations in multi-omics biological networks. Griffin PJ, Zhang Y, Johnson WE, Kolaczyk ED. Biometrics. 2018 May 17. doi: 10.1111/biom.12893. [Epub ahead of print] PMID: 29772079

RepTB: a gene ontology based drug repurposing approach for tuberculosis. Passi A, Rajput NK, Wild DJ, Bhardwaj A. J Cheminform. 2018 May 21;10(1):24. doi: 10.1186/s13321-018-0276-9. PMID: 29785561

Kernelized rank learning for personalized drug recommendation. He X, Folkman L, Borgwardt K. Bioinformatics. 2018 Mar 8. doi: 10.1093/bioinformatics/bty132. [Epub ahead of print] PMID: 29528376
Code: https://github.com/BorgwardtLab/Kernelized-Rank-Learning

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs. Hur J, Özgür A, He Y. J Biomed Semantics. 2018 Jun 7;9(1):17. doi: 10.1186/s13326-018-0185-x. PMID: 29880031

Exploring Landscape of Drug-Target-Pathway-Side Effect Associations. Lim H, Poleksic A, Xie L. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:132-141. PMID: 29888057
[PDF]

SemaTyP: a knowledge graph based literature mining method for drug discovery. Sang S, Yang Z, Wang L, Liu X, Lin H, Wang J. BMC Bioinformatics. 2018 May 30;19(1):193. doi: 10.1186/s12859-018-2167-5. PMID: 29843590

An anatomic transcriptional atlas of human glioblastoma. Puchalski RB, Shah N, et al. Science. 2018 May 11;360(6389):660-663. doi: 10.1126/science.aaf2666. PMID: 29748285
Ivy Glioblastoma Atlas: http://glioblastoma.alleninstitute.org/

Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud. Kamdar MR, Musen MA. AMIA Annu Symp Proc. 2018 Apr 16;2017:1014-1023. PMID: 29854169

Drug repositioning for prostate cancer: using a data-driven approach to gain new insights. Wang Q, Xu R. AMIA Annu Symp Proc. 2018 Apr 16;2017:1724-1733. PMID: 29854243
Data: http://nlp.case.edu/public/data/PC_GenoPredict/ and http://nlp.case.edu/public/data/treatKB/

Network-based machine learning and graph theory algorithms for precision oncology. Zhang W, Chien J, Yong J, Kuang R. NPJ Precis Oncol. 2017 Aug 8;1(1):25. doi: 10.1038/s41698-017-0029-7. Review. PMID: 29872707

Ontology-based disease similarity network for disease gene prediction. Le DH, Dang VT. Vietnam Journal of Computer Science. 2016 Aug;3(3):197–205.
uses Human Phenotype Ontology

A Landscape of Pharmacogenomic Interactions in Cancer. Iorio F, Knijnenburg TA, et al. Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. PMID: 27397505
Genomics of Drug Sensitivity in Cancer database: https://www.cancerrxgene.org/

← Update May 9 2018

Application of Atlas of Cancer Signalling Network in preclinical studies. Monraz Gomez LC, Kondratova M, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Brief Bioinform. 2018 May 3. doi: 10.1093/bib/bby031. [Epub ahead of print] PMID: 29726961
Multi-omics data visualization using NaviCell Web Service: https://navicell.curie.fr/pages/nav_web_service.html

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model. Le DH, Nguyen-Ngoc D. Acta Biotheor. 2018 Apr 26. doi: 10.1007/s10441-018-9325-z. [Epub ahead of print] PMID: 29700660

Community-driven roadmap for integrated disease maps. Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R. Brief Bioinform. 2018 Apr 23. doi: 10.1093/bib/bby024. [Epub ahead of print] PMID: 29688273
Disease Maps Community: http://disease-maps.org/

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning. Zhao C, Jiang J, Guan Y, Guo X, He B. Artif Intell Med. 2018 May;87:49-59. doi: 10.1016/j.artmed.2018.03.005. PMID: 29691122

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Brief Bioinform. 2018 Apr 19. doi: 10.1093/bib/bby025. [Epub ahead of print] Review. PMID: 29684165

pyNBS: A Python implementation for network-based stratification of tumor mutations. Huang JK, Jia T, Carlin DE, Ideker T. Bioinformatics. 2018 Mar 28. doi: 10.1093/bioinformatics/bty186. [Epub ahead of print] PMID: 29608663
Code and data: https://github.com/idekerlab/pyNBS

Prediction of microRNA-disease associations based on distance correlation set. Zhao H, Kuang L, Wang L, Ping P, Xuan Z, Pei T, Wu Z. BMC Bioinformatics. 2018 Apr 17;19(1):141. doi: 10.1186/s12859-018-2146-x. PMID: 29665774

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Way GP, Sanchez-Vega F, La K, Armenia J, Chatila WK, Luna A, Sander C, Cherniack AD, Mina M, Ciriello G, Schultz N; Cancer Genome Atlas Research Network, Sanchez Y, Greene CS. Cell Rep. 2018 Apr 3;23(1):172-180.e3. doi: 10.1016/j.celrep.2018.03.046. PMID: 29617658

MTGO: PPI Network Analysis Via Topological and Functional Module Identification. Vella D, Marini S, Vitali F, Di Silvestre D, Mauri G, Bellazzi R. Sci Rep. 2018 Apr 3;8(1):5499. doi: 10.1038/s41598-018-23672-0. PMID: 29615773
Code: https://gitlab.com/d1vella/MTGO

Recon3D enables a three-dimensional view of gene variation in human metabolism. Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Dräger A, Mih N, Gatto F, Nilsson A, Preciat Gonzalez GA, Aurich MK, Prlić A, Sastry A, Danielsdottir AD, Heinken A, Noronha A, Rose PW, Burley SK, Fleming RMT, Nielsen J, Thiele I, Palsson BO. Nat Biotechnol. 2018 Mar;36(3):272-281. doi: 10.1038/nbt.4072. PMID: 29457794
Virtual Metabolic Human database: https://vmh.uni.lu/

← Update Apr 4 2018

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Huang JK, Carlin DE, Yu MK, Zhang W, Kreisberg JF, Tamayo P, Ideker T. Cell Syst. 2018 Apr 25;6(4):484-495.e5. doi: 10.1016/j.cels.2018.03.001. PMID: 29605183
Presentation: HTML slides

Microarray analysis of obese women with polycystic ovary syndrome for key gene screening, key pathway identification and drug prediction. Wei L, Xin C, Wang W, Hao C. Gene. 2018 Jun 30;661:85-94. doi: 10.1016/j.gene.2018.03.079. PMID: 29601948

Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation. Rios A, Kavuluru R, Lu Z. Bioinformatics. 2018 Mar 26. doi: 10.1093/bioinformatics/bty190. [Epub ahead of print] PMID: 29590309
Experimental code: http://anthonyrios.net/other/adv_code.zip

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Lisa Bastarache, Jacob J. Hughey, et al. Science. 2018 Mar 16;359(6381):1233-1239. doi: 10.1126/science.aal4043. PMID: 29590070
phenotypes from electronic health records linked to genetic data

Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming. Chebouba L, Miannay B, Boughaci D, Guziolowski C. BMC Bioinformatics. 2018 Mar 8;19(Suppl 2):59. doi: 10.1186/s12859-018-2034-4. PMID: 29536824

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia. Kauppi K, Rosenthal SB, Lo MT, Sanyal N, Jiang M, Abagyan R, McEvoy LK, Andreassen OA, Chen CH. Am J Psychiatry. 2018 Jul 1;175(7):674-682. doi: 10.1176/appi.ajp.2017.17040410. PMID: 29495895

Patient-customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients. He L, Tang J, Andersson EI, Timonen S, Koschmieder S, Wennerberg K, Mustjoki S, Aittokallio T. Cancer Res. 2018 May 1;78(9):2407-2418. doi: 10.1158/0008-5472.CAN-17-3644. PMID: 29483097

A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network. Zhang X, Yin J, Zhang X. Genes (Basel). 2018 Mar 2;9(3). pii: E139. doi: 10.3390/genes9030139. PMID: 29498680
Web server: http://39.107.230.144/NLPMMDA
“heterogeneous network composed of disease similarity homo-network, miRNA similarity homo-network and miRNA-disease association hetero-network,” see Fig. 1

The target landscape of clinical kinase drugs. Klaeger S, Heinzlmeir S, Wilhelm M, Polzer H, Vick B, Koenig PA, Reinecke M, Ruprecht B, Petzoldt S, Meng C, Zecha J, Reiter K, Qiao H, Helm D, Koch H, Schoof M, Canevari G, Casale E, Depaolini SR, Feuchtinger A, Wu Z, Schmidt T, Rueckert L, Becker W, Huenges J, Garz AK, Gohlke BO, Zolg DP, Kayser G, Vooder T, Preissner R, Hahne H, Tõnisson N, Kramer K, Götze K, Bassermann F, Schlegl J, Ehrlich HC, Aiche S, Walch A, Greif PA, Schneider S, Felder ER, Ruland J, Médard G, Jeremias I, Spiekermann K, Kuster B. Science. 2017 Dec 1;358(6367). pii: eaan4368. doi: 10.1126/science.aan4368. PMID: 29191878
data in ProteomicsDB; cellular targets of hundreds of kinase inhibitors

Drug target ontology to classify and integrate drug discovery data. Lin Y, Mehta S, Küçük-McGinty H, Turner JP, Vidovic D, Forlin M, Koleti A, Nguyen DT, Jensen LJ, Guha R, Mathias SL, Ursu O, Stathias V, Duan J, Nabizadeh N, Chung C, Mader C, Visser U, Yang JJ, Bologa CG, Oprea TI, Schürer SC. J Biomed Semantics. 2017 Nov 9;8(1):50. PMID: 29122012
Web app, download links: http://drugtargetontology.org/

← Update Mar 1 2018

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network. Zhang Y, Li X, Zhou D, Zhi H, Wang P, Gao Y, Guo M, Yue M, Wang Y, Shen W, Ning S, Li Y, Li X. Mol Oncol. 2018 Feb 21. doi: 10.1002/1878-0261.12181. [Epub ahead of print] PMID: 29464864

Inborn errors of metabolism and the human interactome: a systems medicine approach. Woidy M, Muntau AC, Gersting SW. J Inherit Metab Dis. 2018 May;41(3):285-296. doi: 10.1007/s10545-018-0140-0. PMID: 29404805
disease modules within protein-protein networks

PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data. Kamdar MR, Musen MA. Proc Int World Wide Web Conf. 2017 Apr;2017:321-329. doi: 10.1145/3038912.3052692. PMID: 29479581

Drug repurposing: An approach to tackle drug resistance in S. typhimurium. Balasundaram P, Veerappapillai S, Krishnamurthy S, Karuppasamy R. J Cell Biochem. 2018 Mar;119(3):2818-2831. doi: 10.1002/jcb.26457. PMID: 2905878

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites. Zhang B, Hu S, Baskin E, Patt A, Siddiqui JK, Mathé EA. Metabolites. 2018 Feb 22;8(1). pii: E16. doi: 10.3390/metabo8010016. PMID: 29470400
R package with code for “user-friendly RShiny web app”: https://github.com/Mathelab/RaMP-DB
Backend code: https://github.com/Mathelab/RaMP-BackEnd

A novel heterogeneous network-based method for drug response prediction in cancer cell lines. Zhang F, Wang M, Xi J, Yang J, Li A. Sci Rep. 2018 Feb 20;8(1):3355. doi: 10.1038/s41598-018-21622-4. PMID: 29463808
Code: https://github.com/USTC-HIlab/HNMDRP

Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, Schork AJ, Appadurai V, Buil A, Werge TM, Liu C, White KP; CommonMind Consortium; PsychENCODE Consortium; iPSYCH-BROAD Working Group, Horvath S, Geschwind DH. Science. 2018 Feb 9;359(6376):693-697. doi: 10.1126/science.aad6469. PMID: 29439242

The research on gene-disease association based on text-mining of PubMed. Zhou J, Fu BQ. BMC Bioinformatics. 2018 Feb 7;19(1):37. doi: 10.1186/s12859-018-2048-y. PMID: 29415654

A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection. Langhauser F, Casas AI, Dao VT, Guney E, Menche J, Geuss E, Kleikers PWM, López MG, Barabási AL, Kleinschnitz C, Schmidt HHHW. NPJ Syst Biol Appl. 2018 Feb 5;4:8. doi: 10.1038/s41540-017-0039-7. PMID: 29423274

LncNetP, a systematical lncRNA prioritization approach based on ceRNA and disease phenotype association assumptions. Xu C, Ping Y, Zhao H, Ning S, Xia P, Wang W, Wan L, Li J, Zhang L, Yu L, Xiao Y. Oncotarget. 2017 Dec 8;8(70):114603-114612. doi: 10.18632/oncotarget.23059. PMID: 29383105

← Update Jan 30 2018

Computational Drug Repositioning using Low-Rank Matrix Approximation and Randomized Algorithms. Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Bioinformatics. 2018 Jun 1;34(11):1904-1912. doi: 10.1093/bioinformatics/bty013. PMID: 29365057
Data and software: http://bioinformatics.csu.edu.cn/resources/softs/DrugRepositioning/DRRS/index.html

ADEPTUS: A discovery tool for disease prediction, enrichment and network analysis based on profiles from many diseases. Amar D, Vizel A, Levy C, Shamir R. Bioinformatics. 2018 Jun 1;34(11):1959-1961. doi: 10.1093/bioinformatics/bty027. PMID: 29360930
Website: http://adeptus.cs.tau.ac.il/home

Implementing genome-driven personalized cardiology in clinical practice. Pasipoularides A. J Mol Cell Cardiol. 2018 Feb;115:142-157. doi: 10.1016/j.yjmcc.2018.01.008. Review. PMID: 29343412
not as useful as it sounds; reviews basic science, says little about implementation

Probability-based collaborative filtering model for predicting gene-disease associations. Zeng X, Ding N, Rodríguez-Patón A, Zou Q. BMC Med Genomics. 2017 Dec 28;10(Suppl 5):76. doi: 10.1186/s12920-017-0313-y. PMID: 29297351

ProphTools: general prioritization tools for heterogeneous biological networks. Navarro C, Martínez V, Blanco A, Cano C. Gigascience. 2017 Dec 1;6(12):1-8. doi: 10.1093/gigascience/gix111. PMID: 29186475
Project home page: https://github.com/cnluzon/prophtools, https://hub.docker.com/r/cnluzon/prophtools/

DDR: Efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Olayan RS, Ashoor H, Bajic VB. Bioinformatics. 2018 Apr 1;34(7):1164-1173. doi: 10.1093/bioinformatics/btx731. PMID: 29186331

Systematic identification of latent disease-gene associations from PubMed articles. Zhang Y, Shen F, Mojarad MR, Li D, Liu S, Tao C, Yu Y, Liu H. PLoS One. 2018 Jan 26;13(1):e0191568. doi: 10.1371/journal.pone.0191568. PMID: 29373609

Deep learning of mutation-gene-drug relations from the literature. Lee K, Kim B, Choi Y, Kim S, Shin W, Lee S, Park S, Kim S, Tan AC, Kang J. BMC Bioinformatics. 2018 Jan 25;19(1):21. doi: 10.1186/s12859-018-2029-1. PMID: 29368597

miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships. Chen H, Zhang Z, Peng W. Sci Rep. 2017 Nov 21;7(1):15921. doi: 10.1038/s41598-017-15716-8. PMID: 29162848

Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles. Li X, Xu Y, Cui H, Huang T, Wang D, Lian B, Li W, Qin G, Chen L, Xie L. Artif Intell Med. 2017 Nov;83:35-43. doi: 10.1016/j.artmed.2017.05.008. PMID: 28583437

PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways. Deng Z, Tu W, Deng Z, Hu QN. J Chem Inf Model. 2017 Oct 23;57(10):2395-2400. doi: 10.1021/acs.jcim.7b00175. PMID: 28906116
Database website: http://phid.ditad.org/

MD-Miner: a network-based approach for personalized drug repositioning. Wu H, Miller E, Wijegunawardana D, Regan K, Payne PRO, Li F. BMC Syst Biol. 2017 Oct 3;11(Suppl 5):86. doi: 10.1186/s12918-017-0462-9. PMID: 28984195

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J. Nat Commun. 2017 Sep 18;8(1):573. doi: 10.1038/s41467-017-00680-8. PMID: 28924171
Data and source code: https://github.com/luoyunan/DTINet

Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model. Hwang Y, Oh M, Jang G, Lee T, Park C, Ahn J, Yoon Y. Mol Biosyst. 2017 Aug 22;13(9):1788-1796. doi: 10.1039/c7mb00059f. PMID: 28702565

Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Zong N, Kim H, Ngo V, Harismendy O. Bioinformatics. 2017 Aug 1;33(15):2337-2344. doi: 10.1093/bioinformatics/btx160. PMID: 28430977
Data: https://github.com/zongnansu1982/drug-target-prediction

Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma. Regan KE, Payne PRO, Li F. AMIA Jt Summits Transl Sci Proc. 2017 Jul 26;2017:247-256. PMID: 28815138

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy. El-Hachem N, Gendoo DMA, Ghoraie LS, Safikhani Z, Smirnov P, Chung C, Deng K, Fang A, Birkwood E, Ho C, Isserlin R, Bader GD, Goldenberg A, Haibe-Kains B. Cancer Res. 2017 Jun 1;77(11):3057-3069. doi: 10.1158/0008-5472.CAN-17-0096. PMID: 28314784
Web application: http://dnf.pmgenomics.ca
Code: https://github.com/bhklab/DrugNetworkFusion
Presentation: HTML slides

Network mirroring for drug repositioning. Park S, Lee DG, Shin H. BMC Med Inform Decis Mak. 2017 May 18;17(Suppl 1):55. doi: 10.1186/s12911-017-0449-x. PMID: 28539121

Learning from biomedical linked data to suggest valid pharmacogenes. Dalleau K, Marzougui Y, Da Silva S, Ringot P, Ndiaye NC, Coulet A. J Biomed Semantics. 2017 Apr 20;8(1):16. doi: 10.1186/s13326-017-0125-1. PMID: 28427468

Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. Takeda T, Hao M, Cheng T, Bryant SH, Wang Y. J Cheminform. 2017 Mar 7;9:16. doi: 10.1186/s13321-017-0200-8. PMID: 28316654

Use of Graph Database for the Integration of Heterogeneous Biological Data. Yoon BH, Kim SK, Kim SY. Genomics Inform. 2017 Mar;15(1):19-27. doi: 10.5808/GI.2017.15.1.19. PMID: 28416946

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. BMC Bioinformatics. 2017 Jan 5;18(1):18. doi: 10.1186/s12859-016-1415-9. PMID: 28056782
Data and source code: https://github.com/zw9977129/drug-drug-interaction/

Constructing a Gene-Drug-Adverse Reactions Network and Inferring Potential Gene-Adverse Reactions Associations Using a Text Mining Approach. Sui M, Cui L. Stud Health Technol Inform. 2017;245:531-535. PMID: 29295151

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning. Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. J Biomed Inform. 2017 Apr;68:167-183. doi: 10.1016/j.jbi.2017.03.006. PMID: 28300647

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks. Liu H, Song Y, Guan J, Luo L, Zhuang Z. BMC Bioinformatics. 2016 Dec 23;17(Suppl 17):539. doi: 10.1186/s12859-016-1336-7. PMID: 28155639

Prediction of new drug indications based on clinical data and network modularity. Yu L, Ma X, Zhang L, Zhang J, Gao L. Sci Rep. 2016 Sep 28;6:32530. doi: 10.1038/srep32530. PMID: 27678071

← Initial Set (Jan 23 2018 from Sergio Baranzini)

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Bean DM, Wu H, Iqbal E, Dzahini O, Ibrahim ZM, Broadbent M, Stewart R, Dobson RJB. Sci Rep. 2017 Nov 27;7(1):16416. doi: 10.1038/s41598-017-16674-x. PMID: 29180758

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. Alghamdi M, Al-Mallah M, Keteyian S, Brawner C, Ehrman J, Sakr S. PLoS One. 2017 Jul 24;12(7):e0179805. doi: 10.1371/journal.pone.0179805. PMID: 28738059

Learning a Health Knowledge Graph from Electronic Medical Records. Rotmensch M, Halpern Y, Tlimat A, Horng S, Sontag D. Sci Rep. 2017 Jul 20;7(1):5994. doi: 10.1038/s41598-017-05778-z. PMID: 28729710

A study of EMR-based medical knowledge network and its applications. Zhao C, Jiang J, Xu Z, Guan Y. Comput Methods Programs Biomed. 2017 May;143:13-23. doi: 10.1016/j.cmpb.2017.02.016. PMID: 28391811

Integrating personalized gene expression profiles into predictive disease-associated gene pools. Menche J, Guney E, Sharma A, Branigan PJ, Loza MJ, Baribaud F, Dobrin R, Barabási AL. NPJ Syst Biol Appl. 2017 Mar 13;3:10. doi: 10.1038/s41540-017-0009-0. PMID: 28649437

Link prediction in drug-target interactions network using similarity indices. Lu Y, Guo Y, Korhonen A. BMC Bioinformatics. 2017 Jan 17;18(1):39. doi: 10.1186/s12859-017-1460-z. PMID: 28095781

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. Shi L, Li S, Yang X, Qi J, Pan G, Zhou B. Biomed Res Int. 2017;2017:2858423. doi: 10.1155/2017/2858423. PMID: 28299322

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing. Li C, Zhao P, Sheng VS, Xian X, Wu J, Cui Z. Comput Intell Neurosci. 2017;2017:4092135. doi: 10.1155/2017/4092135. PMID: 28588611

From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration. Gomez-Cabrero D, Menche J, Vargas C, Cano I, Maier D, Barabási AL, Tegnér J, Roca J; Synergy-COPD Consortia. BMC Bioinformatics. 2016 Nov 22;17(Suppl 15):441. doi: 10.1186/s12859-016-1291-3. PMID: 28185567

Tissue Specificity of Human Disease Module. Kitsak M, Sharma A, Menche J, Guney E, Ghiassian SD, Loscalzo J, Barabási AL. Sci Rep. 2016 Oct 17;6:35241. doi: 10.1038/srep35241. PMID: 27748412

Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams. Eshleman R, Singh R. BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):335. PMID: 27766937

Scoring multiple features to predict drug disease associations using information fusion and aggregation. Moghadam H, Rahgozar M, Gharaghani S. SAR QSAR Environ Res. 2016 Aug;27(8):609-28. doi: 10.1080/1062936X.2016.1209241. PMID: 27455069

Endophenotype Network Models: Common Core of Complex Diseases. Ghiassian SD, Menche J, Chasman DI, Giulianini F, Wang R, Ricchiuto P, Aikawa M, Iwata H, Müller C, Zeller T, Sharma A, Wild P, Lackner K, Singh S, Ridker PM, Blankenberg S, Barabási AL, Loscalzo J. Sci Rep. 2016 Jun 9;6:27414. doi: 10.1038/srep27414. PMID: 27278246

Network-based in silico drug efficacy screening. Guney E, Menche J, Vidal M, Barábasi AL. Nat Commun. 2016 Feb 1;7:10331. doi: 10.1038/ncomms10331. PMID: 26831545

ARWAR: A network approach for predicting Adverse Drug Reactions. Rahmani H, Weiss G, Méndez-Lucio O, Bender A. Comput Biol Med. 2016 Jan 1;68:101-8. doi: 10.1016/j.compbiomed.2015.11.005. PMID: 26638149

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Sharma A, Menche J, Huang CC, Ort T, Zhou X, Kitsak M, Sahni N, Thibault D, Voung L, Guo F, Ghiassian SD, Gulbahce N, Baribaud F, Tocker J, Dobrin R, Barnathan E, Liu H, Panettieri RA Jr, Tantisira KG, Qiu W, Raby BA, Silverman EK, Vidal M, Weiss ST, Barabási AL. Hum Mol Genet. 2015 Jun 1;24(11):3005-20. doi: 10.1093/hmg/ddv001. PMID: 25586491

A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. Ghiassian SD, Menche J, Barabási AL. PLoS Comput Biol. 2015 Apr 8;11(4):e1004120. doi: 10.1371/journal.pcbi.1004120. PMID: 25853560

Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. Science. 2015 Feb 20;347(6224):1257601. doi: 10.1126/science.1257601. PMID: 25700523