BMBL Links:
- YouTube: BMBL OSU
- Twitter: @QinMaBMBL
- Slack: bmbl-team
- Graph Neural Network coding notebooks
Single-cell related websites
- Chan Zuckerberg Initiative Single-cell Grants
- scRNA-tools (List of software packages for single-cell data analysis)
- Awesome-single-cell (Another list of software packages for single-cell data analysis)
- Single-cell RNA-Seq data analysis introduction
- Single-cell dataset collection
Single-cell symposiums
Rmarkdown & Jupyter Notebooks
- Applications of Machine Learning and Artificial Intelligence in Biomedical Informatics (Jupyter)
- Introduction to Jupyter Notebooks
- Mechanisms of Action (MoA) Prediction using DNN models
- A simple LSTM-RNN for generating sequence consensus
- A tutorial of DNA motif finding using CNN
- A tutorial of predicting protein-protein interactions using GCN
- A tutorial of dimension reduction in single-cell RNA-seq dataset using Autoencoder
- RNA-seq analysis pipeline (Rmarkdown)
- Single-cell RNA-seq analysis pipeline (Rmarkdown)
- Spatial Transcriptomics Analysis pipeline (Rmarkdown)
- BMI8130 notebooks
Online courses
- Machine Learning for Single Cell Analysis workshop – Krishnaswamy Lab (January 2021)
- Algorithms for DNA Sequencing
- Statistics for Biologists
- STAT 115: Introduction to Computational Biology and Bioinformatics (Havard University by Xiaole (Shirley) Liu)
- CS224W: Machine Learning with Graphs (Stanford University)
Awesome labs
- Oliver Stegle Lab (German Cancer Research Center)
- Yosef Lab (UC Berkeley)
- Regev Lab (MIT)
- Bonnie Berger Lab (MIT)
- Amit Lab (Weizmann Institute of Science)
- Welch Lab (University of Michigan)
- Kharchenko Lab (Harvard University)
- Guy Wolf lab (Université de Montréal)
- The Krishnaswamy Lab (Yale University)
- Theis Lab (TU Munich)
- Michael Bronstein lab (Imperial College London)
- The Dana Pe’er Lab (Sloan Kettering Institute)
- REN lab (San Diego Supercomputer Center)
- Linnarsson Lab (Karolinska Institutet)
- BULYK Lab (Harvard University) — GENRE, PBM
- Trapnell Lab (University of Washington) — Monocle
- Sajita Lab (New York Genome Center) — Seurat
- M. Madan Babu Lab (Cambridge) — Regulatory genomics and systems biology
- Wong Lab (Stanford) — Machine learning and gene regulatory network
- Stain Aerts Lab — SCENIC, cisTopic
- Liu Lab (Harvard University) — Cancer epigenomics, CRISPR screens, Cistrome DB
- Xing Lab (Children’s Hospital of Philadelphia) — integrative genomics and medical research
- Huttenhower Lab (Harvard University) — HUMAnN2 for metagenomic research
- International Society for Computational Biology (ISCB)
- Communities of Special Interest (COSIs)
- [Google group] Education: Computational Biology and Bioinformatics Education and Training
- [Google group] MLCSB: Machine Learning in Computational and Systems Biology
- [Google group] Microbiome-cosi
- [Google group] SysMod: Computational Modeling of Biological Systems
- [Wiki] RegSys: Regulatory and Systems Genomics
- [Linkedin group] NetBio: Network Biology (Account loggin needed)
Machine learning & AI
- ML Course Notes
- Deep learning biology
- Machine learning course (Andrew Ng)
- Neural Networks and Deep Learning course (Andrew Ng)
- UFLDL: Stanford tutorial of Unsupervised Feature Learning and Deep Learning.
- MSU tutorial of data science using the Python programming language
- Scikit-learn: Simple and efficient tools for data mining, data analysis and Machine Learning in Python
- Scipy: A Python-based ecosystem of open-source software for mathematics, science, and engineering.
- Tensorflow: An end-to-end open source machine learning computing backend.
- Pythorch: An open source machine learning framework that accelerates the path from research prototyping to production deployment.
- Keras: A high-level neural networks API, written in Python and capable of running on top of multiple computing backends.
- NIPS: Conference on Neural Information Processing Systems.
- ICML: International Conference on Machine Learning.
- IJCAI: International Joint Conferences on Artificial Intelligence Organization
- AAAI: Library of the Association for the Advancement of Artificial Intelligence conference
- XuDong-Deep Learning-Italy-2019-4-lectures