Lezheng Yu

发表

Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

Yanzhi Guo, Lezheng Yu, Z. Wen, 2008, Nucleic acids research.

Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

Yanzhi Guo, Lezheng Yu, Meng-long Li, 2009, Journal of theoretical biology.

Predicting protein lysine methylation sites by incorporating single-residue structural features into Chou's pseudo components

Yanzhi Guo, Lezheng Yu, Meng-long Li, 2018, Chemometrics and Intelligent Laboratory Systems.

DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors

Lezheng Yu, Yizhou Li, Jiesi Luo, 2021, Frontiers in Microbiology.

An Improved Approach for Rapidly Identifying Different Types of Gram-Negative Bacterial Secreted Proteins

Lezheng Yu, Yizhou Li, Fengjuan Liu, 2018 .

SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition.

Menglong Li, Yanzhi Guo, Lezheng Yu, 2010, Journal of theoretical biology.

SecretP: A new method for predicting mammalian secreted proteins

Menglong Li, Yanzhi Guo, Lezheng Yu, 2010, Peptides.

A method for gene essentiality in miRNA-TF-mRNA co-regulatory network and its application on prostate cancer

Lezheng Yu, Meng-long Li, Yizhou Li, 2019, Chemometrics and Intelligent Laboratory Systems.

In silico identification of Gram-negative bacterial secreted proteins from primary sequence

Menglong Li, Xuemei Pu, Yanzhi Guo, 2013, Comput. Biol. Medicine.

layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP

Lezheng Yu, Jiesi Luo, Li Xue, 2022, iScience.

The applications of deep learning algorithms on in silico druggable proteins identification

Lezheng Yu, Yizhou Li, Jiesi Luo, 2022, Journal of advanced research.

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

Li Yang, Menglong Li, Yizhou Li, 2011, BMC Bioinformatics.

Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning

Lezheng Yu, Jiesi Luo, Li Xue, 2022, Frontiers in Microbiology.

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

Lezheng Yu, Jiesi Luo, Li Xue, 2021, NAR genomics and bioinformatics.

Functional classification of secreted proteins by position specific scoring matrix and auto covariance

Menglong Li, Yanzhi Guo, Lezheng Yu, 2012 .

Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes

Di Wu, Juan Li, Menglong Li, 2012, Comput. Biol. Chem..

DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm

Yizhou Li, Jiesi Luo, Runyu Jing, 2020, Molecular therapy. Nucleic acids.

Fast and Efficient Design of Deep Neural Networks for Predicting N7-Methylguanosine Sites Using autoBioSeqpy

Lezheng Yu, Jiesi Luo, Runyu Jing, 2023, ACS omega.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy

Lezheng Yu, Jiesi Luo, Li Xue, 2023, Frontiers in Microbiology.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework

Lezheng Yu, Li Xue, Fengjuan Liu, 2023, Computational and structural biotechnology journal.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Lezheng Yu, Runyu Jing, Jiesi Luo, 2024, Proteomics.

Using the improved position specific scoring matrix and ensemble learning method to predict drug-binding residues from protein sequences

Xuemei Pu, Yanzhi Guo, Juan Li, 2012 .

An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition

Chao Wang, Jiang Wu, Meng-Long Li, 2010, The protein journal.