lin.uestc.edu.cn

LinDing Group

Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. IPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. INuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. The construction and analysis of lysogenic/lytic genetic regulatory network of phage λ.

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Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. IPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. INuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. The construction and analysis of lysogenic/lytic genetic regulatory network of phage λ.

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LinDing Group | lin.uestc.edu.cn Reviews

https://lin.uestc.edu.cn

Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. IPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. INuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. The construction and analysis of lysogenic/lytic genetic regulatory network of phage λ.

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LinDing Group

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LinDing Group

http://lin.uestc.edu.cn/database.html

A resource for RNA Subcellular Localizations. A database for experimentally verified sigma-54 promoters.

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LinDing Group

http://lin.uestc.edu.cn/Publication.html

As first author or corresponding author:. 78 Wang-Ren Qiu, Bi-Qian Sun, Hua Tang, Jian Huang*, Hao Lin*. (2017) Identify and analysis crotonylation sites in histone by using support vector machines. Artificial Intelligence In Medicine. Accepted. (2015 IF: 2.142). 77 Hong-Yan Lai, Xin-Xin Chen, Wei Chen, Hua Tang*, Hao Lin*. (2017) Sequence-based predictive modeling to identify cancerlectins. Accepted. (2015 IF: 5.008). IEEE/ACM Transactions on Computational Biology and Bioinformatics. 72 Wei Chen*, Pengm...

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LinDing Group

http://lin.uestc.edu.cn/about%20us.html

Our interests are to design and use computational approach to study protein and DNA. Now we mainly focus on the following fields:. 1) Developing efficient and accurate method to predict ion channel, membrane protein type, outer membrane protein. 2) Designing theoretic algorithm to study Mycobacterial protein, such as membrane protein, protein subcellular localization and secretory protein. 4) Constructing biological regulatory network and studying the physical mechanics of the network.

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LinDing Group

http://lin.uestc.edu.cn/dna.html

Identification of nucleosomes in. Prediction of recombination spots in. A tool for generating pseudo K-tuple nucleotide composition. Identification of translation initiation site in human genes. Identification of sigma54 and sigma70 promoter in prokaryotic genomes. Identification of origin of replication in. Identification of N6- methyladenosine sites in RNAs. Identifying RNA pseudouridine sites. Predicting adenosine to inosine editing sites.

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lin.uestc.edu.cn

LinDing Group

Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. IPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. INuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. The construction and analysis of lysogenic/lytic genetic regulatory network of phage λ.

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