Multi-objective Genetic Programming with Multi-view Multi-level Feature for Enhanced Protein Secondary Structure Prediction
National Frontiers Science Center for Industrial Intelligence and Systems Optimization（国家工业智能与系统优化前沿科学中心）；Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University)（智能工业数据解析与优化关键实验室（东北大学））；Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry（辽宁智能工业数据解析与优化工程实验室）；Liaoning Key Laboratory of Manufacturing System and Logistics Optimization（辽宁制造系统与物流优化重点实验室）
Predicting protein secondary structure is essential for understanding protein function and advancing drug discovery. However, the intricate sequence-structure relationship poses significant challenges for accurate modeling. To address these, we propose MOGP-MMF, a multi-objective genetic programming framework that reformulates PSSP as an automated optimization task focused on feature selection and fusion. Specifically, MOGP-MMF introduces a multi-view multi-level representation strategy that integrates evolutionary, semantic, and newly introduced structural views to capture the comprehensive protein folding logic. Leveraging an enriched operator set, the framework evolves both linear and nonlinear fusion functions, effectively capturing high-order feature interactions while reducing fusion complexity. To resolve the accuracy-complexity trade-off, an improved multi-objective GP algorithm is developed, incorporating a knowledge transfer mechanism that utilizes prior evolutionary experience to guide the population toward global optima. Extensive experiments across seven benchmark datasets demonstrate that MOGP-MMF surpasses state-of-the-art methods, particularly in Q8 accuracy and structural integrity. Furthermore, MOGP-MMF generates a diverse set of non-dominated solutions, offering flexible model selection schemes for various practical application scenarios. The source code is available on GitHub: https://github.com/qian-ann/MOGP-MMF/tree/main.
Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers
Banaras Hindu University, Varanasi, India（班纳拉锡大学）；Indiana University, Bloomington（印第安纳大学）；Indian Institute of Technology Jodhpur, India（印度理工学院朱罗尔分校）；Indian Institute of Technology Patna, India（印度理工学院帕坦分校）
Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification
The School of Computing and Informatic, University of Louisiana at Lafayette（计算与信息学院，路易斯安那州立大学拉法叶分校）；Department of Chemistry, University of Louisiana at Lafayette（化学系，路易斯安那州立大学拉法叶分校）
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah（数学与科学计算与成像研究所（SCI）大学）；Department of Mathematics, UCLA（数学系，加州大学洛杉矶分校）；Department of Mathematics, UC Davis（数学系，加州大学戴维斯分校）
