Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction. - Pingping Sun; Haixu Ju; Zhenbang Liu; Qiao Ning; Jian Zhang 020; Xiaowei Zhao 004; Yanxin Huang; Zhiqiang Ma 003; Yuxin Li 001 (Comput. Math. Methods Medicine, 2013)

Prediction of protein solvent accessibility using PSO-SVR with multiple sequence-derived features and weighted sliding window scheme. - Jian Zhang 020; Wenhan Chen; Pingping Sun; Xiaowei Zhao 004; Zhiqiang Ma 003 (BioData Min., 2015)

Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm. - Jian Zhang 020; Bo Gao; Haiting Chai; Zhiqiang Ma 003; Guifu Yang (BMC Bioinform., 2016)

Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme. - Jian Zhang 020; Haiting Chai; Guifu Yang; Zhiqiang Ma 003 (BMC Bioinform., 2017)

Identification of Mammalian Enzymatic Proteins Based on Sequence-Derived Features and Species-Specific Scheme. - Haiting Chai; Jian Zhang 020 (IEEE Access, 2018)

Review and comparative assessment of sequence-based predictors of protein-binding residues. - Jian Zhang 020; Lukasz A. Kurgan (Briefings Bioinform., 2018)

HEMEsPred: Structure-Based Ligand-Specific Heme Binding Residues Prediction by Using Fast-Adaptive Ensemble Learning Scheme. - Jian Zhang 020; Haiting Chai; Bo Gao; Guifu Yang; Zhiqiang Ma 003 (IEEE ACM Trans. Comput. Biol. Bioinform., 2018)

Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. - Jian Zhang 020; Zhiqiang Ma 003; Lukasz A. Kurgan (Briefings Bioinform., 2019)

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences. - Jian Zhang 020; Lukasz A. Kurgan (Bioinform., 2019)

Corrigendum to: Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. - Jian Zhang 020; Zhiqiang Ma 003; Lukasz A. Kurgan (Briefings Bioinform., 2020)

Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins. - Jian Zhang 020; Sina Ghadermarzi; Lukasz A. Kurgan (Bioinform., 2020)

PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection. - Fuhao Zhang; Wenbo Shi; Jian Zhang 020; Min Zeng; Min Li 007; Lukasz A. Kurgan (Bioinform., 2020)

DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences. - Jian Zhang 020; Sina Ghadermarzi; Akila Katuwawala; Lukasz A. Kurgan (Briefings Bioinform., 2021)

A 28nm Energy-efficient Asynchronous SNN Accelerator with On-chip Learning for Gas Recognition. - Dexuan Huo; Jilin Zhang; Jian Zhang 020; Hong Chen 002 (ASYNC, 2023)

Designing Self-timed Asynchronous Circuits with Chisel. - Jilin Zhang; Chunqi Qian; Dexuan Huo; Jian Zhang 020; Hong Chen 002 (ASYNC, 2023)

ANAS: Asynchronous Neuromorphic Hardware Architecture Search Based on a System-Level Simulator. - Jian Zhang 020; Jilin Zhang; Dexuan Huo; Hong Chen 002 (DAC, 2023)

Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation. - Wenjie Wei; Malu Zhang; Hong Qu; Ammar Belatreche; Jian Zhang 020; Hong Chen 002 (ICCV, 2023)

ANP-I: A 28nm 1.5pJ/SOP Asynchronous Spiking Neural Network Processor Enabling Sub-O.1 μJ/Sample On-Chip Learning for Edge-AI Applications. - Jilin Zhang; Dexuan Huo; Jian Zhang 020; Chunqi Qian; Qi Liu 010; Liyang Pan; Zhihua Wang 001; Ning Qiao; Kea-Tiong Tang; Hong Chen 002 (ISSCC, 2023)

ANP-G: A 28nm 1.04pJ/SOP Sub-mm2 Spiking and Back-propagation Hybrid Neural Network Asynchronous Olfactory Processor Enabling Few-shot Class-incremental On-chip Learning. - Dexuan Huo; Jilin Zhang; Xinyu Dai; Jian Zhang 020; Chunqi Qian; Kea-Tiong Tang; Hong Chen 002 (VLSI Technology and Circuits, 2023)

SCAMPER: Accurate Type-Specific Prediction of Calcium-Binding Residues Using Sequence-Derived Features. - Jian Zhang 020; Feng Zhou; Xingchen Liang; Guifu Yang (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

ANCoEF: Asynchronous Neuromorphic Algorithm/Hardware Co-Exploration Framework with a Fully Asynchronous Simulator. - Jian Zhang 020; Xiang Zhang; Jingchen Huang; Jilin Zhang; Hong Chen 002 (CoRR, 2024)

ANP-I: A 28-nm 1.5-pJ/SOP Asynchronous Spiking Neural Network Processor Enabling Sub-0.1-μ J/Sample On-Chip Learning for Edge-AI Applications. - Jilin Zhang; Dexuan Huo; Jian Zhang 020; Chunqi Qian; Qi Liu 010; Liyang Pan; Zhihua Wang 001; Ning Qiao; Kea-Tiong Tang; Hong Chen 002 (IEEE J. Solid State Circuits, 2024)

ANP-G: A 28-nm 1.04-pJ/SOP Sub-mm² Asynchronous Hybrid Neural Network Olfactory Processor Enabling Few-Shot Class-Incremental On-Chip Learning. - Dexuan Huo; Jilin Zhang; Xinyu Dai; Jian Zhang 020; Chunqi Qian; Kea-Tiong Tang; Hong Chen 002 (IEEE J. Solid State Circuits, 2025)