machine-learning-resource

• Avoiding common pitfalls in machine learning omic data science. see PDF in the repo.
• Navigating the pitfalls of applying machine learning in genomics. Nature Review Genetics.
• A guide to machine learning for biologists
• paper: A pitfall for machine learning methods aiming to predict across cell types
• Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
• Neural Networks and Deep Learning A free online book by Michael Nielsen.
• supervised machine learning case study in R A Free, Interactive Course Using Tidy Tools
• Machine Learning for Everyone
• An Introduction to Statistical Learning
• Hands-on Machine Learning with R
• Practical Deep Learning For Coders from fast ai
• r2d3: A visual introduction to machine learning
• parsnipA tidy unified interface to models https://tidymodels.github.io/parsnip/
• Descriptive mAchine Learning EXplanations: DALEX
• machine learning MIT OCW

deep learning

• Yann LeCun’s Deep Learning Course at CDS
• Dive into Deep Learning A free interactive book. really nice!
• http://introtodeeplearning.com/ MIT course
• Opportunities and obstacles for deep learning in biology and medicine: 2019 update
• Machine Learning for Beginners: An Introduction to Neural Networks A blog post. see how linear algebra is playing here!
• Practical Deep Learning for Coders, 2019 edition course by fast.ai
• Examples of using deep learning in Bioinformatics
• Getting started with deep learning in R blog post from Rstudio.
• deep learning biology
• Bayesian deep learning for single-cell analysis
• A primer on deep learning in genomics
• Selene: a PyTorch-based deep learning library for biological sequence-level data
• Janggu - Deep learning for Genomics
• Dive into Deep Learning An interactive deep learning book for students, engineers, and researchers.
• Using Nucleus and TensorFlow for DNA Sequencing Error Correction tutorial
• SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species
• ONNX is a open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
• Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
• Deep learning applications in single-cell omics data analysis " Through a systematic literature review, we have found that DL has not yet revolutionized or addressed the most pressing challenges of the SC omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many cases."

machine learning in life science

• A book Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
• A call for deep-learning healthcare
• High-performance medicine: the convergence of human and artificial intelligence
• Privacy in the age of medical big data
• Automated identification of Cell Types in Single Cell RNA Sequencing
• Deep learning: new computational modelling techniques for genomics
• immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires
• Machine learning for deciphering cell heterogeneity and gene regulation