fightingforcode
I am TuZhenyi,a student in Huaqiao University, I am eager to learn on GitHub and contribute my part to this community.
Huaqiao UniversityXiamen,Fujian,China
fightingforcode's Stars
rorcde/mds20_stega
This is the repository for the Models of Sequence Data 2020 Edition for the project RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks
yjs1224/TextSteg
HcwSignal/DNA-Synthetic-Steganography-Based-on-Conditional-Probability-Adaptive-Coding
ml4bio/RNA-FM
RNA foundation model
ZhangLab312/HEAP
FakeEnd/iDNA_ABF
python codes for iDNA-ABF: multi-scale deep biological language learning model for the accurate and interpretable prediction of DNA methylations
chen-bioinfo/iEnhancer-ELM
Yang-J-LIN/lncLocator2
FunctionLab/selene
a framework for training sequence-level deep learning networks
calico/scBasset
Sequence-based Modeling of single-cell ATAC-seq using Convolutional Neural Networks.
ljw-struggle/Bioinfor-DeepATT
DeepATT, a hybrid deep neural network method for identifying functional effects of DNA sequences.
HaoWuLab-Bioinformatics/lncLocator-imb
lncLocator-imb: an imbalance-tolerant ensemble deep learning framework for predicting long non-coding RNA subcellular localization
labmlai/labml
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
seonwoo-min/DeeperHSP
Official Pytorch implementation of DeeperHSP (Protein transfer learning improves identification of heat shock protein families), PLOS ONE 2021
seonwoo-min/TargetNet
Official Pytorch implementation of TargetNet, Bioinformatics 2022
pure-admin/vue-pure-admin
全面ESM+Vue3+Vite+Element-Plus+TypeScript编写的一款后台管理系统(兼容移动端)
HazyResearch/hyena-dna
Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena
nadavbra/protein_bert
theislab/single-cell-best-practices
https://www.sc-best-practices.org
amanchadha/coursera-deep-learning-specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models