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A simple and efficient Mamba implementation in pure PyTorch and MLX.
GENA-LM is a transformer masked language model trained on human DNA sequence.
✂️ Deep learning-based splice site predictor that improves spliced alignments
https://www.biorxiv.org/content/10.1101/2023.07.03.547592v2
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
Interpretation by Deep Generative Masking for Biological Sequences
Implementation of 1D, 2D, and 3D FFT convolutions in PyTorch. Much faster than direct convolutions for large kernel sizes.
Biological sequence analysis for the modern age.
displays multiple genomic sequences in the form of a tube map
Benchmarking DNA Language Models on Biologically Meaningful Tasks
Repository for mRNA Paper and CodonBERT publication.
Polygraph evaluates and compares groups of nucleic acid sequences based on their sequence and functional content for effective design of regulatory elements.
Ledidi turns any machine learning model into a biological sequence editor, allowing you to design sequences with desired properties.
A concise but complete full-attention transformer with a set of promising experimental features from various papers
RNA-seq prediction with deep convolutional neural networks.
Fast and memory-efficient exact attention
Get down and dirty with FlashAttention2.0 in pytorch, plug in and play no complex CUDA kernels
Using Transformer protein embeddings with a linear attention mechanism to make SOTA de-novo predictions for the subcellular location of proteins 🔬
🧬 Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
bio-transformers is a wrapper on top of the ESM/Protbert model, trained on millions on proteins and used to predict embeddings.
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
"Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks" by Koo et al.