machine-learning-resource

courses, tools and reviews

• Big data and deep learning for RNA biology https://www.nature.com/articles/s12276-024-01243-w
• google machine learning free classes: https://developers.google.com/machine-learning
• https://vas3k.com/blog/machine_learning/
• statquest https://app.learney.me/maps/StatQuest
• Scientific discovery in the age of artificial intelligence
• Avoiding common pitfalls in machine learning omic data science. see PDF in the repo.
• Incorporating Machine Learning into Established Bioinformatics Frameworks
• Ten quick tips for deep learning in biology
• How to avoid machine learning pitfalls:a guide for academic researchers
• Artificial intelligence for multimodal data integration in oncology
• Steps to avoid overuse and misuse of machine learning in clinical research
• Navigating the pitfalls of applying machine learning in genomics. Nature Review Genetics.
• A guide to machine learning for biologists
• Deep learning for computational biology
• Current progress and open challenges for applying deep learning across the biosciences
• 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.

deep learning

• nice blog posts on deep NN
• Yann LeCun’s Deep Learning Course at CDS
• Dive into Deep Learning A free interactive book. really nice!
• neuralnetworks and deeplearning A free ebook
• 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

• Sensitive tumor detection, accurate quantification, and cancer subtype classification using low-pass whole methylome sequencing of plasma DNA https://www.biorxiv.org/content/10.1101/2024.06.10.598204v1
• Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models
• DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis
• 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
• Gene expression based inference of cancer drug sensitivity
• Learning the Drug-Target Interaction Lexicon accurately scan ~10 million drug-target pairs per minute
• AI in Drug Discovery 2022 - A Highly Opinionated Literature Review
• CodonBERT: Large Language Models for mRNA design and optimization
• How to represent a protein sequence

deep learning for single-cell

• awesome-deep-learning-single-cell-papers
• Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation” twitter thread to understand https://twitter.com/drklly/status/1698013780394041561
• Single-cell gene expression prediction from DNA sequence at large contexts https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1.full
• Transfer learning enables predictions in network biology https://pubmed.ncbi.nlm.nih.gov/37258680/
• Cell2Sentence: Teaching Large Language Models the Language of Biology
• CPA is a framework to learn the effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug responses across different cell types, doses, and combinations
• GET: a foundation model of transcription across human cell types
• Neural optimal transport predicts perturbation responses at the single-cell level
• CellPLM: Pre-training of Cell Language Model Beyond Single Cells
• GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations
• Learning single-cell perturbation responses using neural optimal transport
• Assessing the limits of zero-shot foundation models in single-cell biology
• Evaluating the Utilities of Large Language Models in Single-cell Data Analysis
• GENEPT: A SIMPLE BUT HARD-TO-BEAT FOUNDATION MODEL FOR GENES AND CELLS BUILT FROM CHATGPT

Large language models (LMM)

• A High-level Overview of Large Language Models
• Training and fine-tuning large language models
• A Hackers' Guide to Language Models
• SaProt: Protein Language Modeling with Structure-aware Vocabulary
• ProteinBERT: a universal deep-learning model of protein sequence and function
• Learning meaningful representations of protein sequences
• Evaluating the Impact of Sequence Convolutions & Embeddings on Protein Structure Prediction youtube video https://www.youtube.com/watch?v=w0N-N6J05gk
• Transformer protein language models are unsupervised structure learners
• A 5’ UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions

Graph Neural Nets (GNN)

• A Gentle Introduction to Graph Neural Networks
• Graph Neural Net works a blog post by Matt B.
• DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data. It uses GNN.
• Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
• GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype

datasets for practicing ML

• Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer https://zenodo.org/record/7625517#.ZDL9JOzMJsU