World's Best Scientists 2026 revealed!
Jiangning Song

Jiangning Song

D-Index & Metrics

Computer Science

D-Index
59
Citations
10958
World Ranking
3488
National Ranking
102

Biology and Biochemistry

D-Index
61
Citations
12249
World Ranking
11435
National Ranking
310

Overview

Jiangning Song is affiliated with Monash University in Australia and has contributed extensively to the field of biochemistry, genetics, and molecular biology. Their research portfolio encompasses a wide range of topics with a strong focus on machine learning applications in bioinformatics.

The main topics of Jiangning Song's work include:

  • Machine Learning in Bioinformatics
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Vaccines and immunoinformatics approaches
  • Bioinformatics and Genomic Networks
  • AI in cancer detection
  • Air Quality and Health Impacts

Their research spans various subfields such as molecular biology, health, toxicology and mutagenesis, artificial intelligence, cancer research, and radiology, nuclear medicine and imaging. Jiangning Song's publication record reflects an emphasis on both foundational molecular studies and cutting-edge AI-based methodologies.

Jiangning Song has coauthored frequently with:

  • Fuyi Li
  • Dong-Jun Yu
  • Zhikang Wang
  • Yuming Guo
  • Shanshan Li

The scientist publishes predominantly in the following venues:

  • Briefings in Bioinformatics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Journal of Chemical Information and Modeling
  • SSRN Electronic Journal

Recent papers highlight the focus and interdisciplinary nature of their research. Notable publications include:

  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, 2021, Nucleic Acids Research
  • Feature Erasing and Diffusion Network for Occluded Person Re-Identification, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modelling study, 2023, The Lancet Planetary Health
  • An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP, 2020, Molecular Therapy - Nucleic Acids
  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides, 2021, Briefings in Bioinformatics

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

  • ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.

    Leyi Wei;Chen Zhou;Huangrong Chen;Jiangning Song

  • 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

  • PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites

    Jiangning Song;Jiangning Song;Hao Tan;Andrew J. Perry;Tatsuya Akutsu

  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

    Zhen Chen;Pei Zhao;Chen Li;Fuyi Li;Fuyi Li

  • APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

    Jun Feng Xia;Jun Feng Xia;Xing Ming Zhao;Jiangning Song;Jiangning Song;De Shuang Huang

  • iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

    Jiangning Song;Yanan Wang;Fuyi Li;Tatsuya Akutsu

  • 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

  • GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome

    Fuyi Li;Chen Li;Mingjun Wang;Geoffrey I. Webb

  • Cascleave: towards more accurate prediction of caspase substrate cleavage sites.

    Jiangning Song;Hao Tan;Hongbin Shen;Khalid Mahmood

  • 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

  • A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands.

    Pouya Faridi;Chen Li;Chen Li;Sri H. Ramarathinam;Julian P. Vivian;Julian P. Vivian

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

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

  • hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties

    Zhen Chen;Yuan Zhou;Jiangning Song;Jiangning Song;Ziding Zhang

  • Computational enzyme design approaches with significant biological outcomes: progress and challenges

    Xiaoman Li;Ziding Zhang;Jiangning Song

  • PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

    Jiangning Song;Fuyi Li;Kazuhiro Takemoto;Gholamreza Haffari

  • An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP.

    Yue Bi;Dongxu Xiang;Zongyuan Ge;Fuyi Li

  • Production of octenyl succinic anhydride-modified waxy corn starch and its characterization.

    Zhiqiang Liu;Yin Li;Fengjie Cui;Lifeng Ping

  • 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

  • Prediction of protein folding rates from primary sequence by fusing multiple sequential features

    Hong-Bin Shen;Jiang-Ning Song;Kuo-Chen Chou

  • Fabrication of high-Q lithium niobate microresonators using femtosecond laser micromachining for second harmonic generation

    J. Lin;Y. Xu;Z. Fang;M. Wang

Frequent Co-Authors

Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Geoffrey I. Webb
Geoffrey I. Webb Monash University
André Leier
André Leier University of Alabama at Birmingham
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Trevor Lithgow
Trevor Lithgow Monash University
Anthony W. Purcell
Anthony W. Purcell Monash University
James C. Whisstock
James C. Whisstock Monash University
Roger J. Daly
Roger J. Daly Monash University
Hong-Bin Shen
Hong-Bin Shen Shanghai Jiao Tong University
Yanhe Ma
Yanhe Ma Chinese Academy of Sciences

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