Source code associated with "Leveraging global gene expression patterns to predict expression of unmeasured genes"
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Updated
Dec 9, 2015 - R
Source code associated with "Leveraging global gene expression patterns to predict expression of unmeasured genes"
A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.
Consultoria em análise de dados para Rodrigo Cardoso (Mestrado Profissional INTO, 2016)
Project to study proliferation and metabolic liabilities across several thousand cancer samples.
Prospective Lynch Syndrome Database (PLSD) - cumulative risk for cancer by age, genetic variant, and gender
Breast Cancer Detection via Machine Learning
Example workflow for cancer genomics analysis using TCGA data
Code for the Cancers paper (Functional Linkage of RKIP to the Epithelial to Mesenchymal Transition and Autophagy during the Development of Prostate Cancer)
Code for the (unpublished) paper ( Regulation of autophagy in response to oxidative stress)
EpiMethEx (Epigenetic Methylation and Expression), a R package to perform a large-scale integrated analysis by cyclic correlation analyses between methylation and gene expression data.
Two or three subtypes of high grade serous ovarian cancer subtypes fit data from different populations better than four
Code for the PeerJ paper (cRegulome: an R package for accessing microRNA and transcription factor-gene expression correlations in cancer)
Code for the book chapter (Anti-cancer effect of RKIP via modulating autophagy during metastasis)
An R package to access, manage and visualize regulome (microRNA/transcription factors)-gene correlations in cancer
Code for processing and analysis of TAGseq data in "The Effects of IKKβ Inhibition on Early NF-κB Activation and Transcription of Downstream Genes". Modified repository from https://github.com/z0on/tag-based_RNAseq.
Code for figure generation and analysis in Finding driver mutations in cancer: Elucidating the role of background mutational processes https://doi.org/10.1371/journal.pcbi.1006981
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