machine-learning-resource
courses, tools and reviews
- google machine learning free classes: https://developers.google.com/machine-learning
- 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.
- 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
- 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