Popular GitHub Repositories for Dna Center

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

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

Intel? AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel? Xeon? Scalable processors and Intel? Data Center GPUs

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

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

? Generative modeling of regulatory DNA sequences with diffusion probabilistic models ?

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

Image segmentation - general superpixel segmentation & center detection & region growing

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

Implementation for CenterFormer: Center-based Transformer for 3D Object Detection (ECCV 2022)

Genomic Pre-trained Network

An unofficial Gluon FR Toolkit for face recognition. https://gluon-face.readthedocs.io

BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model

One-shot Learning and deep face recognition notebooks and workshop materials

GENA-LM is a transformer masked language model trained on human DNA sequence.

Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"

人脸识别算法，结合facenet网络结构和center loss作为损失，基于tensorflow框架，含训练和测试代码，支持从头训练和摄像头测试

Code for the tutorial/review paper for RL-based-fine-tuniing. In this code, we especially focus on the design of biological sequences like DNA (enhancers) and RNA (UTRs) design.

PyTorch implementation of latent space reinforcement learning for E2E dialog published at NAACL 2019. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU