diff --git a/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/AI.md b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/AI.md new file mode 100644 index 00000000..4c797f6f --- /dev/null +++ b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/AI.md @@ -0,0 +1,70 @@ +--- +title: "Tutorials" +task: "" +lineage_type: import +upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/AI.md +upstream_sha: d1364e08 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + + +# Tutorials +(***) [Machine Learning course at Coursera](coursera.org/learn/machine-learning) by Andrew Ng. +A required course for any people who are interested in machine learning. + +(***) [Blog posts by Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471), his code examples (tensorflow + jupyter) are amazing ([GitHub link](https://github.com/aymericdamien/TensorFlow-Examples)). +Step by step introduction to machine learning using tensorflow. + +(*) [Track deep learning related papers on Arxiv](http://www.arxiv-sanity.com/) + +(**) [nice blog posts from a stanford graduate Andrej Karpathy](http://karpathy.github.io/). +A few excellent posts to introduce deep learning from a software engineer perspective. + +(*) [learn important features from deep learning models](https://arxiv.org/abs/1605.01713) + +(**) [Berkeley CS294-112 Deep Reinforcement Learning Sp17](https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) + +(*) [Deep Learning 2016: The Year in Review](http://www.deeplearningweekly.com/blog/deep-learning-2016-the-year-in-review) + +(*) [stanford deep learning in genomics and biomedicine](https://canvas.stanford.edu/courses/51037/files/folder/LectureMaterial) + +(*) [google Udacity deep learning course](https://classroom.udacity.com/courses/ud730) + +# Code +(*) [Keras Documentation](https://keras.io/) + +(*) [deep learning for sequence data DragoNN](http://kundajelab.github.io/dragonn/) + +(*) [deep learning for chemical data: deepchem](http://deepchem.io/) + +(*) [mofan python (chinese)](https://morvanzhou.github.io). +Only provide a Chinese version, but a nice intro with code available in github. + +(*) add image and text in one model ([here](http://cbonnett.github.io/Insight.html)) + +# Hot topics +## Generative models +(*) [GAN code ](https://github.com/Newmu/dcgan_code) + +(*) [Open AI generative model post](https://blog.openai.com/generative-models/#contributions) + +(*) [introduction-generative-adversarial-networks-code-tensorflow with github code](http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/) + +(**)[tips and tricks training GAN](https://github.com/soumith/ganhacks). +If you have trouble debugging GAN, read this. + +(***) [CycleGAN: Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.](https://github.com/junyanz/CycleGAN). +Very inspiring application of GAN in domain knowledge translation. + +(***) [Deep learning with cats](https://github.com/AlexiaJM/Deep-learning-with-cats). +Using different GAN methods to generate cats. + + +# Applications +(*) install tensorflow +https://www.tensorflow.org/install/install_mac#ValidateYourInstallation + diff --git a/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/BigData.md b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/BigData.md new file mode 100644 index 00000000..407393bd --- /dev/null +++ b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/BigData.md @@ -0,0 +1,41 @@ +--- +title: "Disease models" +task: "" +lineage_type: import +upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/BigData.md +upstream_sha: d1364e08 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +Refer to . + +[Leveraging big data to transform target selection and drug discovery](https://www.ncbi.nlm.nih.gov/pubmed/26659699). +[Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma](https://www.nature.com/articles/s41575-019-0240-9). + +Data Types include Genomics (G), Epigenomics (E), Transcriptomics (T), Proteomics (P), Metabolomics (M), Phenomics (P), Imaging (I) . +Mainly list the big data repositories + +# Disease models +## Clinical models + +## Preclinical models + +# Perturbagen + +## OMICS +(***) [A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations](https://www.biorxiv.org/content/early/2017/09/07/185918). +profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays + +(*) [Orthotopic patient-derived xenografts of paediatric solid tumours](https://www.nature.com/nature/journal/v549/n7670/full/nature23647.html). +sensitivity data of 150 drugs across >20 pediatric cell lines + +(*) [NCI ALMANAC](https://dtp.cancer.gov/ncialmanac/initializePage.do) +5,000 pairs of FDA-approved drugs that were tested against the NCI-60 + +# Single cells +(***) Liver cancer single cells ([Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing](http://www.cell.com/cell/fulltext/S0092-8674(17)30596-2)). +5,063 single T cells isolated from peripheral blood, tumor, and adjacent normal tissues from six hepatocellular carcinoma patients. diff --git a/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/DrugDiscovery.md b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/DrugDiscovery.md new file mode 100644 index 00000000..1815c7af --- /dev/null +++ b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/DrugDiscovery.md @@ -0,0 +1,33 @@ +--- +title: "Outstanding discoveries" +task: "" +lineage_type: import +upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/DrugDiscovery.md +upstream_sha: d1364e08 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Outstanding discoveries + +# Successfully Stories +(*) The history of the [development of analogs of thalidomide](https://en.wikipedia.org/wiki/Development_of_analogs_of_thalidomide). Thalidomide is one of the most successful repurposed drug. + +# Business thoughts +(*) [Escaping Big Pharma’s Pricing With Patent-Free Drugs](https://www.nytimes.com/2017/07/18/opinion/escaping-big-pharmas-pricing-with-patent-free-drugs.html).. + +(*) [Cost of Developing a Single Cancer Drug: time 7.3 years, cost $0.6 billion, revenue 1.7 billion](https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2653012?utm_source=twitter&utm_campaign=content-shareicons&utm_content=article_engagement&utm_medium=social&utm_term=091117#.WbdeR9XhT0M.twitter) + +(***) [Axovant's story](https://www.forbes.com/sites/matthewherper/2017/08/09/biotech-wunderkind-raises-1-1-billion-to-fund-pharma-startups/#202ba88c3308). +lots of stories on Axovant founded by Vivek Ramaswamy. + + +# Blogs +(**) [Robert M. Plenge](https://www.plengegen.com/robert-plenge/), Celgene VP, Harvard faculty. Focus on genetics-based drug discovery + +(***) [In the Pipeline](http://blogs.sciencemag.org/pipeline/). +A must-read blog for anyone interesting in drug discovery. + diff --git a/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/README.md b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/README.md new file mode 100644 index 00000000..b71a89c8 --- /dev/null +++ b/upstream/Bin-Chen-Lab-Awesome_BigData_AI_DrugDiscovery/catalogue/README.md @@ -0,0 +1,308 @@ +--- +title: "Introduction to Bioinformatics/Cheminformatics" +task: "" +lineage_type: import +upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/README.md +upstream_sha: d1364e08 +imported_at: 2026-06-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Introduction to Bioinformatics/Cheminformatics +(***) [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) by Robert Tibshirani. +If you have a little statistical background, read this book first. + +(***) [Machine Learning course at Coursera](https://www.coursera.org/learn/machine-learning) by Andrew Ng. +A required course for any people who are interested in machine learning. + +(***) [R & Bioconductor Manual](http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual/). +Ensure you have run the code before taking any bioinformatics project. + +(***) [STATQUEST] (https://statquest.org/video-index/). +Video collection for statistics, machine learning and bioinformatics. + +(***) [HT Sequence Analysis with R and Bioconductor](http://manuals.bioinformatics.ucr.edu/home/ht-seq). +Ensure you have run the code before taking any NGS project. + +(***) [ChemmineR: Cheminformatics Toolkit for R](http://www.bioconductor.org/packages/devel/bioc/vignettes/ChemmineR/inst/doc/ChemmineR.html). +Suggest to run the code before taking any cheminformatics project. + +(**) [Step by Step to practice deep learning](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html). +PyTorch tutorial for deep learning. + +(***) [Introduction to Bioinformatics and Computational Biology](https://liulab-dfci.github.io/bioinfo-combio/). +Very comprenhensive video tutorials in computational biology by Shirley Liu. + +(***) [HarvardX Biomedical Data Science Open Online Training](http://rafalab.github.io/pages/harvardx.html). +comprehensive tutorial on data science with code from rafalab. + +(***) [Statistics for biologists from StatQuest](https://statquest.org/video-index/). +outstanding video tutorials by Josh Starmer. + +(**) [Data Science Cheat Sheet](https://github.com/Bin-Chen-Lab/BigData_AI_DrugDiscovery/blob/master/data_science_cheatsheet.pdf). +A quick check list of basics in data science (credit to Maverick Lin) + +(***) Single cell RNA-Seq analysis [osca](https://osca.bioconductor.org/introduction.html), [seurat](https://satijalab.org/seurat/vignettes.html). +outstanding framework for scRNA-Seq analysis. + +# Fundamental papers +**These papers I read at least 10 times, including supplementary materials! All of them are three stars!** + +## Field review +(***) [Hallmarks of Cancer: The Next Generation](http://www.cell.com/abstract/S0092-8674%2811%2900127-9), by Robert A. Weinberg. +Fundamental to understand cancer. + +(***) [Tumor Metastasis: Molecular Insights and Evolving Paradigms](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261217/), by Robert A. Weinberg. +Fundamental to understand cancer metastasis. + +(***) [Cancer genome landscapes](http://science.sciencemag.org/content/339/6127/1546.long), by Bert Vogelstein. +Fundamental to understand cancer genomics. + +(***) [Cancer transcriptome profiling at the juncture of clinical translation](https://www.nature.com/articles/nrg.2017.96), by Arul M. Chinnaiyan. +review on cancer transcriptomics. + +(***) [Ewing sarcoma: historical perspectives, current state-of-the-art, and opportunities for targeted therapy in the future.](https://www.ncbi.nlm.nih.gov/pubmed/18525337). +A typical review on the therapeutic discovery of one cancer. + +(***) [Opportunities and challenges in phenotypic drug discovery: an industry perspective](https://www.nature.com/nrd/journal/v16/n8/abs/nrd.2017.111.html). +Our drug discovery approach is one type of phenotypic screening. + +(***) [Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002375), by Purvesh Khatri. +A very nice summary of method development in pathway analysis. + +(***) [Deep learning](https://www.nature.com/nature/journal/v521/n7553/full/nature14539.html) by Yann LeCun, Yoshua Bengio & Geoffrey Hinton. +Deep learning review. + +(***) [High-performance medicine: the convergence of human and artificial intelligence](https://www.nature.com/articles/s41591-018-0300-7) by Eric Topol. +Current progress and challenges in applying DL into biomedical research. + +## Statistical method development +(***) [Significance analysis of microarrays applied to the ionizing radiation response](http://www.pnas.org/content/98/9/5116.full), by Robert Tibshirani. +Development of SAM, a popular method to perform differential expression analysis using microarray data. + +(***) [limma: Linear Models for Microarray Data](https://link.springer.com/chapter/10.1007/0-387-29362-0_23). +Development of LIMMA, another popular method to perform differential expression analysis using microarray data. + +(***) [Differential expression analysis for sequence count data](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106), by Simon Anders. +Development of DEseq, a popular method to perform differential expression analysis using RNA-SEQ data. + +(***) [Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles](http://www.pnas.org/content/102/43/15545.long), by Jill P. Mesirov. +Development of GSEA, the most popular gene set enrichment analysis method and the fundamental to understand our drug discovery method. + +(***) [Adjusting batch effects in microarray expression data using Empirical Bayes methods](https://academic.oup.com/biostatistics/article/8/1/118/252073/Adjusting-batch-effects-in-microarray-expression). +Development of Combat, a method to correct batch effects. + +(***) [Emergence of Scaling in Random Networks](http://science.sciencemag.org/content/286/5439/509.full) by Albert-László Barabási. +Discovery of scale-free networks. + +(***) [Pathsim: Meta path-based top-k similarity search in heterogeneous information networks](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.2455) by Jiawei Han. +A typical machine learning approach to mining heterogeneous networks. + +(***) [MuSiC: Identifying mutational significance in cancer genomes](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409272/), by Li Ding. +Development of MuSic, a popular method to identify mutations. + +## Informatics method development and application +(***) [The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.](http://science.sciencemag.org/content/313/5795/1929.long) by Justin Lamb. +The first paper to describe our drug discovery approach. + +(***) [Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data](http://stm.sciencemag.org/content/3/96/96ra77) from Atul's lab. +The basic of our drug discovery work, and a great demonstration of writing a computational paper (from method development to experimental validation). + +(***) [Relating protein pharmacology by ligand chemistry](http://www.nature.com/nbt/journal/v25/n2/full/nbt1284.html) by Michael J Keiser and Brian K Shoichet. +The development of SEA, a method to predict drug-target interactions, and another great demonstration of writing a computational paper. + +(***) [Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3658274/) by Peer Bork. +start with data analysis and end with a few biological experiments. + +(***) [Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy](http://www.cell.com/cancer-cell/fulltext/S1535-6108(14)00125-1) by Andrea Califano. +start with data analysis and end with a few biological experiments. + +(***) [Elucidating compound mechanism of action by network perturbation analysis](http://www.sciencedirect.com/science/article/pii/S0092867415006996) by Andrea Califano. +start with data analysis and end with a few biological experiments. + +(***) [Discovery of drug mode of action and drug repositioning from transcriptional responses](http://www.pnas.org/content/107/33/14621.long). +start with data analysis and end with a few biological experiments. + +(***) [Imagenet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). +Development of convolutional neural networks (CNN), the popular deep learning method. + +## Computational analysis +(***) [Drug-target network](https://www.nature.com/nbt/journal/v25/n10/full/nbt1338.html) by Barabási. +Network analysis of drug-target interactions. + +(***) [Comprehensive molecular portraits of human breast tumours](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465532/) from TCGA. +A typical genomic analysis paper from TCGA. + +(***) [Mutational landscape and significance across 12 major cancer types.](http://dx.doi.org/10.1038/nature12634), by Li Ding. +A phenomenal paper on pan-cancer genomic analysis. + +(***) [Comprehensive Characterization of Molecular Differences in Cancer between Male and Female Patients](http://www.cell.com/cancer-cell/fulltext/S1535-6108(16)30111-8), by Han Liang. +A phenomenal paper on pan-cancer genomic analysis. + +(***) [Genetics of rheumatoid arthritis contributes to biology and drug discovery.](https://www.nature.com/nature/journal/v506/n7488/full/nature12873.html) by Robert M. Plenge. +great work using genetics for drug discovery. + +(***) [The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity](https://www.nature.com/nature/journal/v483/n7391/full/nature11003.html). +phenomenal work using cell line data to discover biomarkers. + +(***) [A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set](http://stm.sciencemag.org/content/7/287/287ra71.short) by Purvesh Khatri. +Phenomenal work using public microarray data to discover biomarkers. + +(***) [Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases](http://pubs.acs.org/doi/10.1021/ci060003g) by Jeremy Jenkins. +A typical machine learning paper in cheminformatics. + +(***) [Do structurally similar molecules have similar biological activity](https://dx.doi.org/10.1021/jm020155c). +A typical data analysis paper in cheminformatics. + +## Deep-learning based drug discovery +(***) [Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells](https://pubmed.ncbi.nlm.nih.gov/33096023/) by Ideker. +Develop a model to predict drug activity based on a huge pharmacogenomics dataset, propose novel ways to model cells based on Gene Ontology, and experimentally validate some hits. + +(***) [A Deep Learning Approach to Antibiotic Discovery](https://pubmed.ncbi.nlm.nih.gov/32084340/) by Barzilay and Collins. +Develop a model to predict antibiotic activity based on chemical structure, screen millions of compounds and extensively validate one drug candidate. + +(***) [Deep reinforcement learning for de novo drug design](http://advances.sciencemag.org/content/4/7/eaap7885) by Tropsha. +Develop a model to generate targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties. + +(***) [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://arxiv.org/abs/1509.09292) by Ryan Adams. +Designed DL based fingerprints inspired by ECFP + +(***) [Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules](https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00572). +Used Variational autoencoder to encode SMILES and optimize compounds from the latent space. + +## Shape our future +(***) [Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma](http://science.sciencemag.org/content/344/6190/1396). +Application of single cell in a cancer study. + +(***) [Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia](https://www.nature.com/nm/journal/v23/n6/full/nm.4336.html). +Application of single cell analysis toward personalized cancer therapy. + +(***) [Brown Adipogenic Reprogramming Induced by a Small Molecule](http://www.sciencedirect.com/science/article/pii/S2211124716317697) by Sheng Ding. +Using small molecules to control cell development. + +(***) [Correlating chemical sensitivity and basal gene expression reveals mechanism of action.](https://www.nature.com/nchembio/journal/v12/n2/full/nchembio.1986.html) from Stuart Schreiber. +Usage of pharmacogenomics data to understand drug mechanisms. + +(***) [A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles](https://www.biorxiv.org/content/early/2017/05/10/136168) from Todd R. Golub. +LINCS, the dataset we primarily used for drug discovery. + +(***) [Integrative clinical genomics of metastatic cancer](http://www.nature.com/nature/journal/v548/n7667/full/nature23306.html). +We have lots of experience working on primary cancer, now it's time to place our interest to metastatic cancer, which the majority of patients die from. + +(***) [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593.pdf). +Using deep learning GAN to realize domain knowledge translation. + +# Outstanding tools and datasets for translational drug discovery + +**use liver cancer as an example, can be applied to other cancers, only list outstanding tools/datasets for liver cancer drug discovery** +two review articles from the lab: +[Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma](https://www.nature.com/articles/s41575-019-0240-9). +[Leveraging big data to transform target selection and drug discovery](http://www.ncbi.nlm.nih.gov/pubmed/26659699). + +## Diseases/Patients +(***) [ClinicalTrials.gov](https://clinicaltrials.gov). +Search drugs used in liver cancer clinical trials. + +(**) [Cancer Today (Globocan): Data visualization tools that present current national estimates of cancer incidence, mortality, and prevalence](http://gco.iarc.fr/today/home). +Search liver cancer incidence. + +(*) [UK Biobank](http://www.ukbiobank.ac.uk/). [UK Biobank Engine](https://biobankengine.stanford.edu/). +Search public clinical/molecular data for liver cancer and search genetic variants for liver cancer via Stanford Biobank Engine. + +(**) [COSMIC](http://cancer.sanger.ac.uk/cosmic). +Search somatic mutation for liver cancer. + +## Target Discovery +(***) [cBioPortal](http://www.cbioportal.org/). +Search molecular alterations for liver cancer from public datasets including TCGA. + +(***) [GTEx](http://www.gtexportal.org). +Search gene expression in normal liver tissues. + +(***) [The Human Protein Atlas](https://www.proteinatlas.org/). +Search protein expression and pathology for liver cancer. + +(***) [Cancer Cell Line Encyclopedia](https://portals.broadinstitute.org/ccle). +Search gene expression in liver cancer cell lines. + +(*) [Project Achilles](https://portals.broadinstitute.org/achilles). +Search essential genes in liver cancer cells. + +(***) [DepMap](https://depmap.org/portal/). +create a comprehensive preclinical reference map connecting tumor features with tumor dependencies to accelerate the development of precision treatments. + +(***) [GEO](https://www.ncbi.nlm.nih.gov/geo/). +Search functional genomics data for liver cancer, requiring additional computational analysis to create a liver cancer signature. + +(***) [Enrichr](http://amp.pharm.mssm.edu/Enrichr/). +Search enriched TS/pathways/biological processes/cell types given a list of genes. + +(***) [STRING DB](https://string-db.org/). +Visualize protein-protein interactions. + +## Drug Discovery +(***) [PubChem](https://pubchem.ncbi.nlm.nih.gov/). +Everything needed to know about a compound/drug. + +(**) [DrugBank](https://www.drugbank.ca/). +Search drug-target-indication. + +(**) [SEA](http://sea.bkslab.org/). +Predict targets of a given compound. + +(***) [LINCS](https://clue.io/). +Predict drugs given a liver cancer signature. + +(**) [ChemMine](http://chemmine.ucr.edu/). +very useful for chemical structure enrichment analysis. + +# NGS analysis +(***) [RNASEQ blog](http://www.rna-seqblog.com/). +A great collection of RNA-SEQ analysis methods/applications. + +(*) [RPKM, FPKM and TPM, clearly explained](http://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/) + +(*) [RNA-seq workflow: gene-level exploratory analysis and differential expression](http://www.bioconductor.org/help/workflows/rnaseqGene/) + +# Python packages +(***) [anaconda](https://anaconda.org/) +Suggest using anaconda to manage python packages. + +(***) [scikit: a popular python machine learning packages](http://scikit-learn.org/stable/). + +(**) [rdkit](http://www.rdkit.org/docs/index.html). +free python library to process chemical structures. + +(***) [PyTorch](http://pytorch.org/). +Deep learning framework. + +# R/Bioconductor packages +(**) [ggplot cheatsheet](http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/). A must read to visualize data using R. + +(**) [ChemmineR: Cheminformatics Toolkit for R](http://www.bioconductor.org/packages/devel/bioc/vignettes/ChemmineR/inst/doc/ChemmineR.html) + +(**) [biomaRt](http://bioconductor.org/packages/release/bioc/html/biomaRt.html). +A great package to map IDs. + +(***) [GEOquery](http://bioconductor.org/packages/release/bioc/html/GEOquery.html). +Search and download data from GEO. + +(**) [cgdsr](https://cran.r-project.org/web/packages/cgdsr/index.html). +API to access cBioportal data. + +(**) [pheatmap](https://cran.r-project.org/web/packages/pheatmap/index.html). +Visualize heatmap. + +(*) [Easy Way to Mix Multiple Graphs on The Same Page](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/). + + + + + + + + +