Popular GitHub Repositories for Genome Mutation

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

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

A guided mutation-based fuzzer for ML-based Web Application Firewalls

MetaDE is a GPU-accelerated evolutionary framework that optimizes Differential Evolution (DE) strategies via meta-level evolution. Supporting both JAX and PyTorch, it dynamically adapts mutation and crossover strategies for efficient large-scale black-box optimization.

? Advanced hybrid language model for directed protein evolution. (NeurIPS 2024)

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.

Evolutionary algorithm that uses Large Language Models (LLMs) to automatically improve programs through iterative mutation and selection

A simple simulation in Unity, which uses genetic algorithm to optimize forces applied to cubes

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

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

This paper have been presented in the form of an oral presentation at the IJCNN-2019 conference. The title is "Transformation-gated LSTM: efficient capture of short-term mutation dependencies for multivariate time series prediction tasks".

GeneticPromptLab uses genetic algorithms for automated prompt engineering (for LLMs), enhancing quality and diversity through iterative selection, crossover, and mutation, while efficiently exploring minimal yet diverse samples from the training set.