The discovery of disease biomarkers from gene expression data has been greatly advanced by feature selection (FS) methods, especially using ensemble FS (EFS) strategies with perturbation at the data level (i.e., homogeneous, Hom-EFS) or method level (i.e., heterogeneous, Het-EFS). Here we proposed a Hybrid EFS (Hyb-EFS) design that explores both types of perturbation to improve the stability and the predictive power of candidate biomarkers. With this, Hyb-EFS aims to disrupt associations of good performance with a single dataset, single algorithm, or a specific combination of both, which is particularly interesting for better reproducibility of genomic biomarkers. We investigated the adequacy of our approach for microarray data related to four types of cancer, carrying out an extensive comparison with other ensemble and single FS approaches. Five FS methods were used in our experiments: Wx, Symmetrical Uncertainty (SU), Gain Ratio (GR), Characteristic Direction (GeoDE), and ReliefF. We observed that the Hyb-EFS and Het-EFS approaches attenuated the large performance variation observed for most single FS and Hom-EFS across distinct datasets. Also, the Hyb-EFS improved upon the stability of the Het-EFS within our domain. Comparing the Hyb-EFS and Het-EFS composed of the top-performing selectors (Wx, GR, and SU), our hybrid approach surpassed the equivalent heterogeneous design and the best Hom-EFS (Hom-Wx). Interestingly, the rankings produced by our Hyb-EFS reached greater biological plausibility, with a notably high enrichment for cancer-related genes and pathways. Thus, our experiments suggest the potential of the proposed Hybrid EFS design in discovering candidate biomarkers from microarray data. Finally, we provide an open-source framework to support similar analyses in other domains, both as a user-friendly application and a plain Python package.
Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway（癌症遗传学系，癌症研究所，奥斯陆大学医院，奥斯陆，挪威）；Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, University of Oslo, Oslo, Norway（奥斯陆生物统计与流行病学中心（OCBE），生物统计学系，奥斯陆大学，奥斯陆，挪威）；Department of Informatics, University of Oslo, Oslo, Norway（信息学系，奥斯陆大学，奥斯陆，挪威）；Department of Statistics, Ludwig Maximilian University of Munich, Munich, Germany（统计学系，慕尼黑路德维希-马克西米利安大学，慕尼黑，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））
Quantum AI for Cancer Diagnostic Biomarker Discovery
Department of Electrical and Computer Engineering, North Carolina State University, NC, USA（电气与计算机工程系，北卡罗来纳州立大学，NC，USA）；Department of Biochemistry, Purdue University, IN, USA（生物化学系，普渡大学，IN，USA）；Department of Chemistry, North Carolina State University, Raleigh, NC 27695（化学系，北卡罗来纳州立大学，雷德伍德，NC 27695）
A Graph Based Raman Spectral Processing Technique for Exosome Classification
Insight Centre for Data Analytics（数据分析洞察中心）；Dublin City University（都柏林城市大学）；School of Computing（计算学院）；School of Chemical Sciences（化学科学学院）；Ho Chi Minh City Open University（胡志明市开放大学）
Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
State Key Laboratory of Software Engineering, School of Computer, Wuhan University（武汉大学计算机学院软件工程国家重点实验室）；National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院国家数学与交叉科学中心）
You Only Train Once: Differentiable Subset Selection for Omics Data
Department of Computer Science, ETH Zurich（计算机科学系，苏黎世联邦理工学院）；Department of Intensive Care and Neonatology, University Children’s Hospital Zurich（重症医学与新生儿科，苏黎世大学儿童医院）
RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection
Department of Computer Science, New Jersey Institute of Technology（计算机科学系，新泽西理工学院）；Department of Data Science, New Jersey Institute of Technology（数据科学系，新泽西理工学院）；Department of Biostatistics（生物统计学系）；Division of Cardiology, Brown University Health（心脏病学部，布朗大学健康中心）；Department of Medicine, Warren Alpert Medical School of Brown University（医学系，布朗大学沃伦·阿尔珀特医学院）
An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Department of Mathematics and Physics, Mathematics Major, Gangneung-Wonju National University（数学与物理系，数学专业，江陵-Wonju国立大学）；Department of Data Science, Gangneung-Wonju National University（数据科学系，江陵-Wonju国立大学）
Enhancing predictive imaging biomarker discovery through treatment effect analysis
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；ETH Zürich（苏黎世联邦理工学院）；University Hospital Bonn（波恩大学医院）；University of Bonn（波恩大学）；Heidelberg University Hospital（海德堡大学医院）
A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Department of Computer Science , University of Pisa（计算机科学系，比萨大学）；Department of Clinical and Biological Sciences, University of Torino（临床与生物科学系，都灵大学）；Department of Computer Sciences, University of Torino（计算机科学系，都灵大学）；Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）
该研究构建整合八个 PBMC 微阵列数据集的机器学习流水线，用 XGBoost 与 SHAP 发现 MS 生物标志物，并与 DEA 比较，证明二者互补且可揭示相关疾病通路。
Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
Electric and Computer Engineering Department, Universidad Nacional del Sur（国家南方大学电子与计算机工程系）；Institute of Computer Science and Engineering, National Scientific and Technological Research Council of Argentina (CONICET)（阿根廷国家科学与技术研究理事会计算机科学与工程研究所）；Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra（庞培法布拉大学脑认知中心、信息与通信技术系计算神经科学组）；Institució Catalana de la Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育研究所）；Department of Information and Communication Technologies, Universitat Pompeu Fabra（庞培法布拉大学信息与通信技术系）；ViRVIG, University of Girona（吉罗纳大学ViRVIG）
Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Digital Technology Research Center, National Research Council Canada（加拿大国家研究理事会数字技术研究中心）；Biomedical Science and Hematology Departments, College of Medicine and Health Sciences, Sultan Qaboos University（苏丹奥曼穆萨特大学医学院与健康科学学院生物医学科学与血液科）
CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data
Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Department of Psychology, University of Virginia（弗吉尼亚大学心理学系）；Department of Neurology, University of Virginia（弗吉尼亚大学神经病学系）；Departments of Computer Science and Biology, Johns Hopkins University（约翰霍普金斯大学计算机科学与生物学系）
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Tulane Center for Biomedical Informatics and Genomics（杜兰大学生物医学信息学与基因组学中心）；Division of Biomedical Informatics and Genomics（生物医学信息学与基因组学部门）；Deming Department of Medicine（戴明医学系）；Tulane University（杜兰大学）
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
Central South University（中南大学）；Xinjiang University（新疆大学）；Shenzhen University of Advanced Technology（深圳先进技术研究院？不，正确翻译：深圳理工大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Dundee（邓迪大学）；Griffith University（格里菲斯大学）；Shenzhen Health Development Research and Data Management Center（深圳卫生发展研究与数据管理中心）
Deep Learning Discovery of Demographic Biomarkers in Echocardiography
Cedars-Sinai Medical Center（西达赛奈医疗中心）；Stanford University（斯坦福大学）；University of California San Francisco（加州大学旧金山分校）；San Francisco Veteran Affairs Medical Center（旧金山退伍军人事务医疗中心）
ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Department of Computer Science \& Software Engineering, Concordia University, 2155 Guy St, Montreal, QC H3H 2L9, Canada；Department of Electrical \& Computer Engineering, University of Toronto, 10 King's College Rd, Toronto, ON M5S 3G8, Canada；Department of Chemistry \& Biology, Toronto Metropolitan University, 350 Victoria St. Toronto, ON M5B 2K3, Canada；Department of Medicine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Edouard Montpetit Blvd, Montreal, QC H3T 1J4, Canada；Axe Cancer, Centre de recherche du CHUM, 900 Saint-Denis St, Montréal, QC H2X 0A9, Canada；Anatomic Pathology, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada；Department of Laboratory Medicine \& Pathobiology, University of Toronto, Simcoe Hall, 1 King's College Circle, Toronto, ON M5S 3K3, Canada；Department of Pathology \& Molecular Medicine, Queen’s University, 88 Stuart Street Queen's University Kingston, ON K7L 3N6 Canada；Department of Pathology \& Molecular Medicine, Université de Montréal, 2900 Édouard-Montpetit Blvd, Montréal, QC H3T 1J4, Canada
Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学先进交叉学科学院）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学人工智能学院）；National University of Singapore, Singapore（新加坡国立大学）
Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Dhaka, Bangladesh（信息与通信技术系，穆拉纳巴沙尼科学与技术大学）；Department of Computer Science, Tulane University, New Orleans, LA, USA（计算机科学系，杜兰大学）；Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 138632, Singapore（生物信息学研究所（BII），科技研究局（A*STAR））；Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA（杜兰大学生物医学信息学与基因组学中心，医学部）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（Ochsner研究中心，Ochsner研究）
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data
East China University of Science and Technology（华东理工大学）；Jacobs Technion-Cornell Institute（雅各布布·泰克尼翁-康奈尔研究所）；Cornell Tech（康奈尔科技校区）；Cornell University（康奈尔大学）
Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery
Computational Imaging Research Lab, Department for Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗部门计算成像研究实验室）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Division of General and Pediatric Radiology, Department for Biomedical Imaging and Image-guided Therapy（普通放射科与儿科放射科，生物医学成像与影像引导治疗部门）；Medical University of Vienna（维也纳医学大学）
