Bioinformatics Lab
kitchong@jbnu.ac.kr

Welcome to Our Bioinformatics Lab!

Bioinformatics Lab

Welcome to Bioinformatics Lab

Our lab is part of the Electronics Engineering Faculty in JeonBuk National University and we have put a lot of effort on preparing not only excellent employees of domestic IT industry but human resource development with international competitiveness and leadership. Our lab disciplines is to build computational systems for Transciption Factor Binding Site, Alternative Splicing, Branch Point Selection, etc.

Our lab goal is training industry's most wanted human resource with specialized knowledge and practical skills, excellent researcher with creativity, globally competitive advanced human resource. Many students involved in our lab in a variety of programs which are provided by our university and faculty, such as Industry Internship program, Professor Laboratory Practicum program etc. Graduates are contributing IT industry development greatly from wide range of sectors including enterprises, research institutes and universities.

Read More

Bioinformatics Tools

The list of the developed tools by our research group. These tools are for educational and research purposes only.

2S-piRCNN

Identification of Functional piRNAs Using a Convolutional Neural Network

RUN

4mC-w2vec

Identifying DNA N4-methylcytosine Sites in the Rosaceae Genome with a Deep Learning Model relying on Distributed Feature Representation

RUN

4mCCNN

Identification of N4-Methylcytosine Sites in Prokaryotes Using Convolutional Neural Network

RUN

4mCPred-CNN

Prediction of DNA N4-Methylcytosine in mouse genome using Convolutional Neural Network

RUN

Cr-Prom

A Convolutional Neural Network-based Model for the Prediction of Rice Promoters

RUN

DCNN-4mC

Densely Connected Neural Network Based N4-methylcytosine Site Prediction in Multiple Species

RUN

DeePromoter

Robust Promoter Predictor Using Deep Learning

RUN

DL-m6A

Identification of N6-methyladenosine Sites in Mammals using deep learning based on different encoding schemes

RUN

DNA6mA-MINT

DNA-6mA Modification Identification Neural Tool

RUN

DSC

Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning

RUN

i4mC-Deep

Intelligent predictor for N4-methylcytosine sites in prokaryotics by using deep learning approach with chemical properties

RUN

i6mA-Caps

A CapsuleNet-based framework for identifying DNA 6mA

RUN

i6mA-stack

A stacking ensemble-based computational prediction of DNA N6-methyladenine (6mA) sites in the Rosaceae genome

RUN

iDNA6mA

Intelligent Computational Model For Identification of DNA N6-methyladenine Sites in The Rice Genome

RUN

iEnhancer-CNN

Identifying Enhancers and Their Strength by the Integration of Word Embedding and Convolution Neural Network

RUN

iIM-CNN

Cross-Species m6A identification using Convolution Neural Network

RUN

iIRMethyl

Interpretable Machine Learning (model for) Identification of Arginine Methylation Sites

RUN

iltox

Ionic Liquids Toxicity Prediction Using Deep Kernel Learning

RUN

iProm-Zea

A two-layer model to identify Plant promoters and their types using convolutional neural network

RUN

iRG-4mC

Neural Network Based Tool for Modification Identification of DNA 4mC Sites in Rosaceae Genome

RUN

iRhm5CNN

Prediction of RNA 5-Hydroxymethylcytosine Modifications Using Deep Learning

RUN

m6A-NeuralTool

Convolution Neural Tool for RNA N6-Methyladenosine Site Identification in Different Species

RUN

ncRDeep

Non coding RNA classification with convolutional neural network

RUN

ncRDense

A novel computational approach for classification of non-coding RNA family by deep learning

RUN

piRDA

Identification of piRNA disease associations using deep learning

RUN

pm6A-CNN

A CNN-based RNA N6-methyladenosine site predictor for multiple species using heterogeneous features representation

RUN

Prom70-CNN

Prediction of Sigma70 Promotors Using Conven-tional Neural Networks

RUN

RNABP

Branch Point Selection in RNA Splicing Using Deep Learning

RUN

SpineNet6mA

A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes

RUN

TS-m6A-DL

Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model

RUN

XG-ac4C

Identification of N4-acetylcytidine (ac4C) in mRNA by using eXtreme Gradient Boosting method with electron-ion interaction pseudopotentials

RUN

ZayyuNet

A unified deep learning model for the identification of epigenetic modifications using raw genomic sequences

RUN

Contact us

Address

Address

Engineering, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54896 Republic of Korea

Email

Prof. Kil To Chong: kitchong@jbnu.ac.kr
Dr. Hilal Tayara: hilaltayara@jbnu.ac.kr