World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
6915
World Ranking
8858
National Ranking
1141

Overview

Ran Su is affiliated with Tianjin University in China and has contributed extensively to the intersection of biochemistry, genetics, molecular biology, and computer science. Their research involves a strong focus on bioinformatics, molecular biology, and artificial intelligence applications within biological contexts.

Their publication record features works in well-regarded academic venues, reflecting a specialized interest in both the computational and biological sciences. Frequent publication venues include:

  • Briefings in Bioinformatics
  • IEEE Journal of Biomedical and Health Informatics
  • Frontiers in Bioengineering and Biotechnology
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)

Ran Su's primary fields of study encompass:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Within these fields, their work touches on the following subfields:

  • Molecular Biology
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging

The scientist's research topics frequently cover:

  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods
  • AI in cancer detection
  • Bioinformatics and Genomic Networks
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Machine Learning in Materials Science

Ran Su has coauthored works with the following frequent collaborators:

  • Leyi Wei
  • Changming Sun
  • Qiangguo Jin
  • Quan Zou
  • Junru Jin

Notable recent papers include:

  • RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans, 2020, Frontiers in Bioengineering and Biotechnology
  • iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations, 2022, Genome Biology
  • DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis, 2023, Nucleic Acids Research
  • Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework, 2020, Briefings in Bioinformatics
  • Cascade knowledge diffusion network for skin lesion diagnosis and segmentation, 2020, Applied Soft Computing

Best Publications

  • DUNet: A deformable network for retinal vessel segmentation

    Qiangguo Jin;Zhaopeng Meng;Zhaopeng Meng;Tuan D. Pham;Qi Chen

  • RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

    Qiangguo Jin;Zhaopeng Meng;Changming Sun;Leyi Wei

  • 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

  • Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

    Leyi Wei;Pengwei Xing;Jiancang Zeng;JinXiu Chen

  • Prediction of human protein subcellular localization using deep learning

    Leyi Wei;Leyi Wei;Yijie Ding;Ran Su;Ran Su;Jijun Tang

  • Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

    Ran Su;Xinyi Liu;Leyi Wei;Quan Zou

  • CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency

    Leyi Wei;PengWei Xing;Ran Su;Gaotao Shi

  • PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning.

    Leyi Wei;Chen Zhou;Ran Su;Quan Zou

  • DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis

    Unknown

  • M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

    Leyi Wei;Huangrong Chen;Ran Su;Ran Su

  • iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

    Unknown

  • Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

    Leyi Wei;Shasha Luan;Luis Augusto Eijy Nagai;Ran Su

  • ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.

    Bing Rao;Chen Zhou;Guoying Zhang;Ran Su

  • Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

    Ran Su;Jie Hu;Quan Zou;Balachandran Manavalan

  • Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

    Leyi Wei;Leyi Wei;Ran Su;Ran Su;Bing Wang;Xiuting Li

  • Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE

    Qi Chen;Zhaopeng Meng;Zhaopeng Meng;Xinyi Liu;Qianguo Jin

  • Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

    Leyi Wei;Wenjia He;Adeel Malik;Ran Su

  • Iterative feature representations improve N4-methylcytosine site prediction.

    Leyi Wei;Ran Su;Shasha Luan;Zhijun Liao

  • CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning.

    Xiaoli Qiang;Chen Zhou;Xiucai Ye;Pu-Feng Du

  • Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

    Ran Su;Huichen Wu;Bo Xu;Xiaofeng Liu

  • Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods

    Karthikeyan Kandasamy;Jacqueline Kai Chin Chuah;Ran Su;Peng Huang

  • M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species.

    Xiaoli Qiang;Huangrong Chen;Xiucai Ye;Ran Su

  • Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

    Leyi Wei;Jie Hu;Fuyi Li;Jiangning Song

Frequent Co-Authors

Leyi Wei
Leyi Wei Shandong University
Quan Zou
Quan Zou University of Electronic Science and Technology of China
Changming Sun
Changming Sun Commonwealth Scientific and Industrial Research Organisation
Tuan D. Pham
Tuan D. Pham Queen Mary University of London
Wei Chen
Wei Chen Chengdu University of Traditional Chinese Medicine
Jiangning Song
Jiangning Song Monash University
Jijun Tang
Jijun Tang University of South Carolina

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