hilal-t

Chonbuk National University

Jeonju, S.Korea

Pinned Repositories

iDNA6mA
iDNA6mA (5-step rule): Identification of DNA N6-methyladenine Sites in The Rice Genome by Intelligent Computational Model via Chou’s 5-step Rule
Language:Jupyter Notebook11
AI_Class_2024_01
Language:Python00
ConvCRF
This repository contains the reference implementation for our proposed Convolutional CRFs.
Language:Python00
DIGITS
Deep Learning GPU Training System
Language:Python00
FactorNet
A deep learning package for predicting TF binding
Language:Python00
iLearn
iLearn, a Python Toolkit and Web Server Integrating the Functionality of Feature Calculation, Extraction, Clustering, Feature Selection, Feature Normalization, Dimension Reduction and Model Construction for Classification, Best Model Selection, Ensemble Learning and Result Visualization for DNA, RNA and Protein Sequences.
Language:Python00
m5C-pred
XGBoost Framework with Feature Selection for the Prediction of RNA N5-methylcytosine sites
Language:Python00
ProtTrans
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Language:Jupyter Notebook00
SMMIR-CFRAutoEnc
Language:Python00

hilal-t/iDNA6mA
iDNA6mA (5-step rule): Identification of DNA N6-methyladenine Sites in The Rice Genome by Intelligent Computational Model via Chou’s 5-step Rule
Language:Jupyter Notebook11
hilal-t/AI_Class_2024_01
Language:Python
hilal-t/ConvCRF
This repository contains the reference implementation for our proposed Convolutional CRFs.
Language:Python
hilal-t/DIGITS
Deep Learning GPU Training System
Language:Python
hilal-t/FactorNet
A deep learning package for predicting TF binding
Language:Python
hilal-t/iLearn
iLearn, a Python Toolkit and Web Server Integrating the Functionality of Feature Calculation, Extraction, Clustering, Feature Selection, Feature Normalization, Dimension Reduction and Model Construction for Classification, Best Model Selection, Ensemble Learning and Result Visualization for DNA, RNA and Protein Sequences.
Language:Python
hilal-t/m5C-pred
XGBoost Framework with Feature Selection for the Prediction of RNA N5-methylcytosine sites
hilal-t/ProtTrans
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
hilal-t/SMMIR-CFRAutoEnc