一、 个人基本情况

    李孟山：1980年出生，博士，教授，2014年于南昌大学获博士学位，现为公共资源与实验室管理处（信息与教育技术中心）主任，华中师范大学电子信息专业博士生导师，赣南师范大学硕士生（电子信息、控制科学与工程）、博士生（化学）导师；江西省高校中青年学科（专业）带头人，十佳青年教工，赣江之星。

二、 主要研究方向

    本人主要从事机器学习、化学信息、人工智能算法、大模型、知识图谱、生物医药工程、生物信息学等领域研究工作。

    联系方式：18379798321，[email protected]

三、 主要学术成果

    近年主持完成国家自然科学基金2项，教育部高等教育司2017年产学合作协同育人项目1项，主持省部级课题2项，参与国家自然科学基金项目3项；《Chemical Reviews》、《Nature Communications》、《IEEE Transactions on Cybernetics》、《IEEE Transactions on Fuzzy Systems》、《IEEE Transactions on Industrial Informatics》、《Applied Artificial Intelligence》、《Analytical Chemistry》、《Journal of Chemical Theory and Computation》、《Industrial & Engineering Chemistry Research》、《Journal of Chemical Information and Modeling》、《Chemical Engineering Communications》、《Fluid Phase Equilibria》、《Chinese Journal of Chemistry》等近100本SCI收录期刊特邀审稿专家；

    以第一作者或通讯作者在国内外重要的SCI收录期刊上发表论文100余篇。

科研项目：

1、国家自然科学基金，大规模粒子并行演化溶解行为多尺度模型的关键技术研究 ，52063002，35.00万元。

2、国家自然科学基金，演化粒子动力学溶解行为多尺度模型研究，51663001，30万元。

近五年部分代表性成果总结如下1-19

(1) Yelin, L.; Yan, W.; Xiaojun, X.; Jihong, Z.; Lixin, G.; Mengshan, L., Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition. Npj Digital Medicine. 2026, 9, 500.

(2) Haiyang, X.; Yan, W.; Xiaojun, X.; Jihong, Z.; Lixin, G.; Xingyuan, H.; Hesheng, L.; Mengshan, L., Solubility named entity recognition using ensemble learning with enhanced model-agnostic meta-learning and dual feature encoding. Eng. Appl. Artif. Intel. 2026, 167 (2), 113840.

(3) Fang, Y.; Xiao, H.; Zhou, W.; Guan, L.; Li, M.; Meng, F.; Zhu, J., DBML-Font :Double-branch multi-level feature fusion based on diffusion model for few-shot font generation. Neural Networks 2026, 200, 108825.

(4) Zhenghui, L.; Wenxing, H.; Yan, W.; Jihong, Z.; Xiaojun, X.; Lixin, G.; Mengshan, L., Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction. Food Chem. 2025, 468, 142464.

(5) Yan, W.; Yu, F.; Tan, L.; Mengshan, L.; Xiaojun, X.; Weihong, Z.; Sheng, S.; Jun, W.; Fu-an, W., A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction. BMC Biology 2025, 23 (1), 108.

(6) Xiao, H. Y.; Yan, R. M.; Wu, Y.; Guan, L. X.; Li, M. S., Knowledge Graph for Solubility Big Data: Construction and Applications. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery 2025, 15 (1), e1570.

(7) Tan, L.; Mengshan, L.; Yu, F.; Yelin, L.; Jihong, Z.; Lixin, G., Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding. Bmc Genomics 2025, 25 (1), 1253.

(8) Luo, R.; Liu, J.; Guan, L.; Li, M., HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter. Methods 2025, 235, 71-80.

(9) Li, T.; Li, M.; Li, Y.; Zhu, J.; Guan, L., DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding. Interdisciplinary Sciences-Computational Life Sciences 2025, 17 (4), 873-891.

(10) Hu, W.; Yue, Y.; Yan, R.; Guan, L.; Li, M., An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy. BMC Biology 2025, 23 (1), 47.

(11) Wei, W.; Mengshan, L.; Yan, W.; Lixin, G., Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine. BMC Chemistry 2024, 18 (1), 24.

(12) Rentao, L.; Yelin, L.; Lixin, G.; Mengshan, L., Predicting DNA sequence splice site based on graph convolutional network and DNA graph construction. Journal of King Saud University - Computer and Information Sciences 2024, 36 (5), 102089.

(13) Hu, W.; Li, Y.; Wu, Y.; Guan, L.; Li, M., A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding. Iscience 2024, 27 (6), 110030.

(14) Hu, W.; Li, M.; Xiao, H.; Guan, L., Essential genes identification model based on sequence feature map and graph convolutional neural network. Bmc Genomics 2024, 25 (1), 47.

(15) Guangwen, T.; Mengshan, L.; Biyu, H.; Jihong, Z.; Lixin, G., Achieving accurate trajectory predicting and tracking for autonomous vehicles via reinforcement learning-assisted control approaches. Eng. Appl. Artif. Intel. 2024, 135, 108773.

(16) Biyu, H.; Mengshan, L.; Yuxin, H.; Ming, Z.; Nan, W.; Lixin, G., A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism. Bmc Cancer 2024, 24 (1), 683.

(17) Yan, W.; Tan, L.; Mengshan, L.; Weihong, Z.; Sheng, S.; Jun, W.; Fu-an, W., Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction. Bmc Genomics 2023, 24 (1), 758.

(18) Liu, Y.; Wei, S.; Huang, H.; Lai, Q.; Li, M.; Guan, L., Naming Entity recognition of citrus pests and diseases based on the BERT-BiLSTM-CRF model. Expert. Syst. Appl. 2023, 234, 121103.

(19) Hu, W.; Guan, L.; Li, M., Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network. Plos Computational Biology 2023, 19 (8), e1011370.