https://www.pnas.org/content/115/18/E4304 - Deep learning improves prediction of drug–drug and drug–food interactions
https://www.ncbi.nlm.nih.gov/pubmed/27153606 - Drug-induced adverse events prediction with the LINCS L1000 data.
https://science.sciencemag.org/content/313/5795/1929.full - The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease
https://ehp.niehs.nih.gov/doi/10.1289/EHP3986 - The Carcinogenome Project: In Vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173180/ - Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004220&type=printable - Sharing and Specificity of Co-expression Networks across 35 Human Tissues
Danaher P., Wang P., Witten D.M.: The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B
Mardia K.: Multi-dimensional multivariate Gaussian Markov random fields with application to image processing. Journal of Multivariate Analysis 24(2), 265–284 (1988) doi: 10.1016/0047-259X(88)90040-1
Markov and Hidden Markov Models of Genomic and Protein Features - https://www.youtube.com/watch?v=d5NMrA2HkG4
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To study and utilise the LINCS1000 database. - Understanding the data - Extracting the data - Automating the extraction - Converting the data into usable format for any biologist
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To utilise gene expression data to predict gene expression from composite variation
Developed small scale Hidden Markov Model to predict gene expression based on eQTL expression values.
Apply the model to the whole dataset of eQTLs from the GTEx consortium