- https://vas3k.com/blog/machine_learning/
- statquest https://app.learney.me/maps/StatQuest
- 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
- CS229: Machine Learning - The Summer Edition free course from Stanford.
- 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."
- A book Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
- Deep learning shapes single-cell data analysis
- 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