World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5239
World Ranking
11704
National Ranking
4792

Overview

André Leier is affiliated with the University of Alabama at Birmingham in the United States. Their research spans primarily the fields of biochemistry, genetics, and molecular biology, with additional contributions to medicine. Within these broader fields, they have specialized in molecular biology, neurology, cancer research, genetics, and microbiology.

The main topics explored in their body of work include:

  • Machine Learning in Bioinformatics
  • Genomics and Phylogenetic Studies
  • Neurofibromatosis and Schwannoma Cases
  • Biochemical and Structural Characterization
  • RNA and protein synthesis mechanisms
  • Vaccines and immunoinformatics approaches
  • Neuroblastoma Research and Treatments

Leier has published extensively, with a significant number of papers appearing in journals such as Briefings in Bioinformatics and Molecular Therapy - Nucleic Acids. Their frequent publication venues include:

  • Briefings in Bioinformatics
  • Molecular Therapy - Nucleic Acids
  • Genomics Proteomics & Bioinformatics
  • Bioinformatics
  • Nucleic Acids Research

Selected recent publications demonstrate their focus on the intersection of machine learning and bioinformatics as well as protein and genomic studies. The recent papers include:

  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides (2021), published in Briefings in Bioinformatics
  • Procleave: Predicting Protease-Specific Substrate Cleavage Sites by Combining Sequence and Structural Information (2020), published in Genomics Proteomics & Bioinformatics
  • PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs (2020), published in Bioinformatics
  • DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy (2020), published in Briefings in Bioinformatics
  • Positive-unlabeled learning in bioinformatics and computational biology: a brief review (2021), published in Briefings in Bioinformatics

Collaboration has been a notable aspect of their research activity. Frequent co-authors include:

  • Jiangning Song
  • Tatiana T. Marquez-Lago
  • Fuyi Li
  • Geoffrey I. Webb
  • Robert A. Kesterson

This network of collaborators suggests interdisciplinary work within computational biology and bioinformatics, particularly leveraging machine learning approaches for biological and medical challenges. The emphasis on papers involving neural networks, hybrid frameworks, and sequence-structure integration highlights an interest in developing computational tools for understanding molecular and genomic functions.

Best Publications

  • iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.

    Zhen Chen;Pei Zhao;Fuyi Li;André Leier

  • iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

    Zhen Chen;Pei Zhao;Fuyi Li;Tatiana T Marquez-Lago

  • Cryptography with DNA binary strands

    André Leier;Christoph Richter;Wolfgang Banzhaf;Hilmar Rauhe

  • Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation

    Manuel Barrio;Kevin Burrage;André Leier;Tianhai Tian

  • POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

    Jiawei Wang;Bingjiao Yang;Jerico Nico De Leon Revote;André Leier

  • Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    Fuyi Li;Chen Li;Chen Li;Tatiana T Marquez-Lago;André Leier

  • A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

    Shutao Mei;Fuyi Li;André Leier;Tatiana T Marquez-Lago

  • PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.

    Jiangning Song;Fuyi Li;Andre Leier;Tatiana Marquez-Lago

  • Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

    Jiawei Wang;Bingjiao Yang;André Leier;Tatiana T Marquez-Lago

  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.

    Jing Xu;Fuyi Li;André Leier;Dongxu Xiang

  • Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information.

    Fuyi Li;Andre Leier;Quanzhong Liu;Yanan Wang

  • MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.

    Meng Zhang;Fuyi Li;Tatiana T Marquez-Lago;André Leier

  • Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

    Zhen Chen;Xuhan Liu;Fuyi Li;Chen Li;Chen Li

  • Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence.

    P. Dwight Kuo;Wolfgang Banzhaf;André Leier

  • DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

    Fuyi Li;Jinxiang Chen;Jinxiang Chen;André Leier;Tatiana Marquez-Lago

  • Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

    Yanju Zhang;Ruopeng Xie;Jiawei Wang;André Leier

  • Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

    Fuyi Li;Yanan Wang;Yanan Wang;Chen Li;Chen Li;Tatiana T Marquez-Lago

  • PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

    Jiangning Song;Huilin Wang;Jiawei Wang;André Leier

  • DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy

    Ruopeng Xie;Jiahui Li;Jiawei Wang;Wei Dai

  • Bastion3: A two-layer ensemble predictor of type III secreted effectors

    Jiawei Wang;Jiahui Li;Jiahui Li;Bingjiao Yang;Ruopeng Xie

  • PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs.

    Cangzhi Jia;Yue Bi;Jinxiang Chen;Jinxiang Chen;André Leier;André Leier

Frequent Co-Authors

Jiangning Song
Jiangning Song Monash University
Geoffrey I. Webb
Geoffrey I. Webb Monash University
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Trevor Lithgow
Trevor Lithgow Monash University
Wolfgang Banzhaf
Wolfgang Banzhaf Michigan State University
Roger J. Daly
Roger J. Daly Monash University
Robert N. Pike
Robert N. Pike La Trobe University
George Dickson
George Dickson Royal Holloway University of London
James C. Whisstock
James C. Whisstock Monash University

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