1. ChemSpider - Royal Society of Chemistry [link] - [Data Source]

2. GSEA - Gene Sets from Community Contributors [link]

3. PAGER 2.0 - Pathway, Annotated-list and Gene-signature Electronic Repository for Human Network Biology [link]

4. Enrichr - Web server of interactive gene set enrichment analysis [link]

5. Datasets2Tools - Bioinformatics index of datasets, computational tools and databases [link] [Data Source]

6. BioToolBay - A database collection of bioinformatics tools and resources. [link] [Data Source]

7. OMICtools - an informative directory for multi-omic data analysis [link] - [Data Source]

8. VLS3D.com - a virtual ligand screening resource [link] - [Data Source]

1. Arifuzzaman S, Rahman MS, Pang MG. (2018). Research Update and Opportunity of Non-hormonal Male Contraception: Histone Demethylase KDM5B-based Targeting. Pharmacological Research.. https://doi.org/10.1016/j.phrs.2018.12.003. [PDF]

2. Kirchner SK, Ozkan S, Musil R, Spellmann I, Kannayian N, Falkai P, Rossner M, Papiol S. (2018). Polygenic analysis suggests the involvement of calcium signaling in executive function in schizophrenia patients. European Archives of Psychiatry and Clinical Neuroscience.. https://doi.org/10.1007/s00406-018-0961-8. [PDF]

3. Hassan M, Raza H, Abbasi MA, Moustafa AA, Seo SY. (2019). The exploration of novel Alzheimer’s therapeutic agents from the pool of FDA approved medicines using drug repositioning, enzyme inhibition and kinetic mechanism approaches. Biomedicine & Pharmacotherapy.. 109: 2513-2526. [PDF]

4. Hwang S, Kim CY, Yang S, Kim E, Hart T, Marcotte EM, Lee I. (2018). HumanNet v2: human gene networks for disease research. Nucleic Acids Research. doi: https://doi.org/10.1093/nar/gky1126. [PDF]

5. DeTomaso D, Jones M, Subramaniam M, Aschuach T, Ye CJ, Yosef N. (2018). Functional Interpretation of Single-Cell Similarity Maps. bioRxiv. doi: https://doi.org/10.1101/403055. [PDF]

6. Ochsner S, Abraham D, Martin K, Ding W, McOwiti A, Wang Z, Andreano K, Hamilton R, Chen Y, Hamilton A, Gantner M, Dehart M, Qu S, Hilsenbeck S, Becnel L, Bridges D, Maayan A, Huss J, Stossi F, Foulds C, Kralli A, McDonnell D, McKenna N. (2018). The Signaling Pathways Project: an integrated 'omics knowledgebase for mammalian cellular signaling pathways. bioRxiv. doi: https://doi.org/10.1101/401729. [PDF]

7. Gao S, Casey AE, Sargeant TJ, Makinen V-P. (2018). Genetic variation within endolysosomal system is associated with late-onset Alzheimer's disease. BRAIN. [PDF]

8. Wu Y, Wang G. (2018). Machine Learning Based Toxicity Prediction:From Chemical Structural Description toTranscriptome Analysis. International Journal of Molecular Sciences. 19(8): 2358. [PDF]

9. Stopsack KH, Ebot EM, Downer MK, Gerke TA, Rider JR, Kantoff PW, Mucci LA. (2018). Regular aspirin use and gene expression profiles in prostate cancer patients. Cancer Causes & Control. 29:775-784. [PDF]

10. Chujan S, Suriyo T, Ungtrakul T, Pomyen Y, Satayavivad J. (2018). Potential candidate treatment agents for targeteing of cholangiocarcinoma identified by gene expression profile analysis. Biomedical Reports. 9(1): 42-52. [PDF]

11. Srivastava A, George J, Karuturi RKM. (2018). Transcriptome Analysis. Reference Module in Life Sciences. http://doi.org/10.1016/B978-0-12-809633-8.20161-1. [PDF]

12. Silva RXC, Rocha SP, Souza DPS, Lima-Maximino MG, Maximino C. (2018). Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis. bioRxiv. doi: https://doi.org/10.1101/326017. [PDF]

13. Gaspar HA, Gerring Z, Hubel C, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Middeldorp CM, Derks EM, Breen G. (2018). Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. bioRxiv. doi:http://doi.org/10.1101/304113. [PDF]

14. Hintzsche JD, Yoo M, Kim J, Amato CM, Robinson WA, Tan AC. (2018). IMPACT Web Portal: Oncology Database of Integrating Molecular Profiles with Actionable Therapeutics. BMC Medical Genomics. 11 (Suppl 2): 26. [PDF]

15. Jung J, Kim GW, Lee W, Mok C, Chung SH, Jang W. (2018). Meta- and cross-species analyses of insulin resistance based on gene expression datasets in human white adipose tissues. Scientific Reports. Article number: 3747. [PDF]

16. Takata A, Miyake N, Tsurusaki Y, Fukai R, Miyatake S, Koshimizu E, Kushima I, Okada T, Morikawa M, Uno Y, Ishizuka K, Nakamura K, Tsujii M, Yoshikawa T, Toyota T, Okamoto N, Hiraki Y, Hashimoto R, Yasuda Y, Saitoh S, Ohashi K, Sakai Y, Ohga S, Hara T, Kato M, Nakamura K, Ito A, Seiwa C, Shirahata E, Osaka H, Matsumoto A, Takeshita S, Tohyama J, Saikusa T, Matsuishi T, Nakamura T, Tsuboi T, Kato T, Suzuki T, Saitsu H, Nakshima M, Mizuguchi T, Tanaka F, Mori N, Ozaki N, Matsumoto N. (2018). Integrative Analyses of De Novo Mutations Provide Deeper Biological Insights into Autism Spectrum Disorder. Cell Reports. 22(3): 734-747. [PDF]

17. Mehtonen J, Polonen P, Hayrynen S, Lin J, Liuksiala T, Granberg K, Lohi O, Hautamaki V, Nykter M, Heinaniemi M. (2018). Data-driven characterization of molecular phenotypes across heterogenous sample collections. bioRxiv. doi: https://doi.org/10.1101/248096. [PDF]

18. De Las Rivas J, Alonso-Lopez D, Arroyo MM. (2018). Human Interactomics: Comparative Analysis of Different Protein Interaction Resources and Construction of a Cancer Protein-Drug Bipartite Network. Advances in Protein Chemistry and Structural Biology.. 111: 263-282. doi.org/10.1016/bs.apcsb.2017.09.002. [PDF]

19. Powers R, Goodspeed A, Pielke-Lombardo H, Tan AC, Costello J. (2018). GSEA-InContext: Identifying novel and common patterns in expression experiments. Bioinformatics. 34(13): i555-i564. [PDF]

20. Cha Y, Erez T, Reynolds IJ, Kumar D, Ross J, Koytiger G, Kusko R, Zeskind B, Risso S, Kagan E, Papapetropoulos S, Grossman I, Laifenfeld D. (2018). Drug Repurposing from the perspective of pharmaceutical companies. British Journal of Pharmacology. 175(2): 168-180. [PDF].

21. Kim J, Yoo M, Shin J, Kim H, Kang J, Tan AC. (2018). Systems Pharmacology-based Approach of Connecting Disease Genes in Genome-wide Association Studies with Traditional Chinese Medicine. International Journal of Genomics. 2018: 7697356. [PDF]

22. Yue Z, Zheng Q, Neylon MT, Yoo M, Shin J, Zhao Z, Tan AC, Chen JY. (2018). PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology. Nucleic Acids Research. 46(D1): D668-D676. [PDF]

23. Ravikumar B, Aittokallio T. (2018). Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery. Expert Opinion on Drug Discovery. 13(2):179-192. doi: 10.1080/17460441.2018.1413089.[PDF]

24. Consoloni JL, Ibrahim EC, Lefebvre M-N, Zendjidjian X, Olie E, Mazzola-Pomietto P, Desmidt T, Samalin L, Llorca P-M, Abbar M, Lopez-Castroman J, Haffen E, Baumstarck K. (2018). Serotonin transporter gene expression predicts the worsening of suicidal ideation and suicide attempts along a long-term follow-up of a Major Depressive Episode. European Neuropsychopharmacology. 28(3): 401-414. doi.org/10.1016/j.euroneuro.2017.12.015. [PDF]

25. Laukkanen S, Gröonroos T, Pölönen P, Kuusanmäki H, Mehtonen J, Cloos J, Ossenkoppele G, Gjertsen B, Øystein B, Heckman C, Heinäniemi M, Kontro M, Lohi O. (2017). In silico and preclinical drug screening identifies dasatinib as a target therapy for T-ALL. Blood Cancer Journal. 7: e604. [PDF]

26. Bloom B. (2017). Computational biology: future challenges for the patenting of repurposed drugs. Pharmaceutical Patent Analyst. 6(5):201-203. [PDF]

27. Zolotareva O, Bragina E, Goncharova I, Freidin M, Hofestadt R. (2017). Identification of new potential targets for treatment of coinciding asthma and hypertension. Proceedings of the Moscow Conference on Computational Molecular Biology (MCCMB 2017).. [PDF]

28. So HC, Wong YH. (2017). Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders. bioRxiv. doi: https://doi.org/10.1101/173641. [PDF]

29. Liu X, Zeng P, Cui Q, Zhou Y. (2017). Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data. PLoS ONE. 12(6): e0179037. [PDF]

30. So HC, Lau A, Chau CKL, Wong SY. (2017). Translating GWAS findings into therapies for depression and anxiety disorders: Drug repositioning using gene-set analyses reveals enrichment of psychiatric drug classes. bioRxiv. doi: https://doi.org/10.1101/132563. [PDF]

31. Anafi RC, Francey LJ, Hogenesch JB, Kim J. (2017). CYCLOPS reveals human transcriptional rhythms in health and disease. Proc. Natl. Acad. Sci. U.S.A. 114(20): 5312-5317. [PDF]

32. Keum J, Nam H. (2017). SELF-BLM: Prediction of drug-target interactions via self-training SVM. PLoS ONE. 12(2): e0171839. [PDF]

33. Taroni JN, Martyanov V, Mahoney JM, Whitfield ML. (2017). A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy. Journal of Investigative Dermatology. 137(5): 1033-1041. https://doi.org/10.1101/087361. [PDF]

34. Verma N, Rai AK, Kaushik V, Brunnert D, Chahar KR, Pandey J, Goyal P. (2016). Identification of gefitinib off-targets using a structure-based systems biology approach; their validation with reverse docking and retrospective data mining. Scientific Reports. 6: 33949. [PDF]

35. Breen MS, White CH, Shekhtman T, Lin K, Looney D, Woelk CH, Kelsoe R. (2016). Lithium-responsive Genes and Gene Networks in Bipolar Disorder Patient-derived Lymphoblastoid Cell Lines. The Pharmacogenomics Journal. 16: 446-453. [PDF]

36. Hsu YC, Chiu YC, Chen Y, Hsiao TH, Chuang EY. (2016). A Simple Gene Set-based Method Accurately Predicts the Synergy of Drug Pairs. BMC Systems Biology. 10(Suppl 3): 66. [PDF]

37. Federer C*, Yoo M*, Tan AC. (2016). Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials. ASSAY and Drug Development Technologies. 14(10): 557-566. [PDF]

38. Hintzsche JD, Robinson WA, Tan AC. (2016). A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data. International Journal of Genomics. 2016:7983236. [PDF]

39. Hintzsche J, Kim J, Yadav V, Amato C, Robinson SE, Seelenfreund E, Shellman Y, Wisell J, Applegate A, McCarter M, Box N, Tentler J, De S, Robinson WA, Tan AC. (2016). IMPACT: Whole-Exome Sequencing Analysis Pipeline of Integrating Molecular Profiles with Actionable Therapeutics in Clinical Samples. Journal of the American Medical Informatics Association. 23(4): 721-730. [PDF]

40. Ryall KA, Shin J, Yoo M, Hinz TK, Kim J, Kang J, Heasley LE, Tan AC. (2015). Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data. Bioinformatics. 31(23):3799-3806. [PDF]

41. Ryall KA, Kim J, Klauck PJ, Shin J, Yoo M, Ionkina A, Pitts TM, Tentler JJ, Diamond JR, Eckhardt SG, Heasley LE, Kang J, Tan AC. (2015). An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer. BMC Genomics. 16 (Suppl 12):S2. [PDF]

1. Calvert N, Wu J, Sneddon S, Woodhouse J, Carey-Smith R, Wood D, Ingley E. (2018). The use of whole exome sequencing and murine patient derived xenografts as a method of chemosensitivity testing in sarcoma. Clinical Sarcoma Research. 8: 4. [PDF].

2. Dovrolis N, Kolios G, Spyrou G, Maroulakou I. (2017). Laying in silico pipelines for drug repositioning: a paradigm in ensemble analysis for neurodegenerative diseases. Drug Discovery Today. 22(5): 805-813. [PDF]

1. Gaspar HA, Hubel C,Breen G. (2017). Biological Pathways and Drug Gene-sets: Analysis and Visualization. European Neuropsychopharmacology. SA23. [PDF]

2. Laukkanen S, Polonen P, Gronroos T, Kuusanmaki H, Heckman CA, Kontro M, Heinaniemi M, Lohi O. (2016). In Silico and Ex Vivo Drug Screening Identifies Dasatinib as a Potential Targeted Therapy for T-ALL. Blood. 128: 4029. [PDF]

3. Gao S, Casey A, Sargeant T, Makinen V-P. (2016). Genetically Perturbed Pathways and Core Driver Genes in Late-Onset Alzheimer's Disease. Proceedings of The Australian Bioinformatics and Computational Biology Society (AB3ACBS). Poster ID: 47. [PDF]

4. Casey A, Gao S, Makinen V-P. (2016). Integrative genomics of circulating metabolites in human populations. Proceedings of The Australian Bioinformatics and Computational Biology Socitey (AB3ACBS). Poster ID: 66. [PDF]

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