Popular GitHub Repositories for Genome Sequencing

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

? An Artificial Inteligence to teach Google's Dinosaur to jump cactus

Using artificial intelligence and genetic algorithms to automatically write programs. Tutorial: http://www.primaryobjects.com/cms/article149

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

Code for Neural Motifs: Scene Graph Parsing with Global Context (CVPR 2018)

[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome

DANCE: a deep learning library and benchmark platform for single-cell analysis

Scientific Large Language Models: A Survey on Biological & Chemical Domains

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

Train Scene Graph Generation for Visual Genome and GQA in PyTorch >= 1.2 with improved zero and few-shot generalization.

DNABERT_S: Learning Species-Aware DNA Embedding with Genome Foundation Models

Explore a comprehensive collection of basic theories, applications, papers, and best practices about Large Language Models (LLMs) in genomes.

Learning to untangle genome assembly with graph neural networks.

Genetic learning algorithm implementation for simulations, games, or general machine learning problems

This is a python package for genomics study with a GCN framework.

基于ZooKeeper一致性协议的分布式序列生成器

Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer

A collection of publications on comparison of high-throughput sequencing technologies.

Deep learning for identifying important motifs in DNA sequences. Exploration of the structure of the human genome by training neural network architectures (CNN, LSTM, Attention).