The rapid adoption of data-driven methods in biomedicine has intensified concerns over privacy, governance, and regulation, limiting raw data sharing and hindering the assembly of representative cohorts for clinically relevant AI. This landscape necessitates practical, efficient privacy solutions, as cryptographic defenses often impose heavy overhead and differential privacy can degrade performance, leading to sub-optimal outcomes in real-world settings. Here, we present a lightweight federated learning method, INFL, based on Implicit Neural Representations that addresses these challenges. Our approach integrates plug-and-play, coordinate-conditioned modules into client models, embeds a secret key directly into the architecture, and supports seamless aggregation across heterogeneous sites. Across diverse biomedical omics tasks, including cohort-scale classification in bulk proteomics, regression for perturbation prediction in single-cell transcriptomics, and clustering in spatial transcriptomics and multi-omics with both public and private data, we demonstrate that INFL achieves strong, controllable privacy while maintaining utility, preserving the performance necessary for downstream scientific and clinical applications.
Batch effects can impair federated learning in multi-center omics studies
Institute for Computational Systems Biomedicine, University of Hamburg（汉堡大学计算系统生物医学研究所）；Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich（慕尼黑技术大学生命科学学院蛋白质组学与生物分析系）；Department of Mathematics and Computer Science, University of Southern Denmark（丹麦南部大学数学与计算机科学系）；Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡大学生物医学网络科学实验室）；Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich（慕尼黑技术大学生命科学学院系统生物学数据科学）；Institute of Clinical Molecular Biology (IKMB), Kiel University and University Medical Center Schleswig-Holstein（基尔大学与石勒苏益格-荷尔斯泰因大学医学中心临床分子生物学研究所）
Imperial College London（伦敦帝国理工学院）；Hong Kong Baptist University（香港浸会大学）
High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and do…
Imperial College London（帝国理工学院）；University College London（伦敦大学学院）；Hong Kong Baptist University（香港浸会大学）
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonst…
OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling
Institute for Informatics, Data Science and Biostatistics（信息学、数据科学与生物统计学研究所）；Washington University in St. Louis（华盛顿大学圣路易斯分校）；Department of Computer Science（计算机科学系）；NeuroGenomics and Informatics Center（神经基因组学与信息学中心）；Department of Genetics（遗传学系）；Department of Surgery（外科医学系）；Siteman Cancer Center（西门癌中心）；Department of Medicine（医学系）；Department of Pediatrics（儿科医学系）；University of Connecticut（康涅狄格大学）
Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. Through ablation experiments, we prove that the combination of multiple biological modalities (i.e multi-omics) leads to powerful embeddings and better link prediction performances. Our evaluation shows that the integration of gene methylation profiles and RNA-sequencing data significantly improves the link predi…
University of California, Los Angeles（加州大学洛杉矶分校）；University of Cambridge（剑桥大学）；Alan Turing Institute（艾伦·图灵研究所）
Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns. This is challenging because i) complex interactions within and across observed views need to be properly addressed for optimal predictive power and ii) observations with various view-missing patterns need to be flexibly integrated. To address such challenges, we prop…
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease
University of Luxembourg（卢森堡大学）；École polytechnique fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Cambridge（剑桥大学）；Luxembourg Center for Systems Biomedicine（卢森堡系统生物医学中心）
Data Science Institute, Imperial College London（伦敦帝国理工学院数据科学研究所）
Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-…
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
MRC Biostatistics Unit University of Cambridge East Forvie Building（医学研究理事会生物统计学单位剑桥大学东部大楼）；MRC Biostatistics Unit and CRUK Cambridge Centre Ovarian Programme and Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID) University of Cambridge（医学研究理事会生物统计学单位和癌症研究英国委员会剑桥中心卵巢计划及剑桥治疗免疫学和传染病研究所（CITIID）剑桥大学）
In recent years, a comprehensive study of multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary information of a disease, multi-view data analysis with complex interactions is challenging for a deeper and holistic understanding of biological systems. In this paper, we propose a novel generalized kernel machine approach to identify higher-order com…
Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes a novel objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices, each of which may have millions of entries. We demonstrate that SiMLR outperforms closely related methods on superv…
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）
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinforma…
MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics Classification
College of Intelligent Systems Science and Engineering, Harbin Engineering University（哈尔滨工程大学智能系统科学与工程学院）；Qingdao Innovation and Development Center, Harbin Engineering University（哈尔滨工程大学青岛创新发展中心）；Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院医学信息研究所）
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It spans a wide scope of applications from Internet-of-Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this paper, we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconf…
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Georgia Institute of Technology（佐治亚理工学院）；Mayo Clinic Arizona（妙佑医疗国际亚利桑那州院区）；Arizona State University（亚利桑那州立大学）；Columbia University Medical Center（哥伦比亚大学医学中心）
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
Cornell University（康奈尔大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Cornell Tech（康奈尔科技学院）；Weill Cornell Medical School（威尔康奈尔医学院）；University of Pennsylvania（宾夕法尼亚大学）；Michigan State University（密歇根州立大学）
In biomedical research, many different types of patient data can be collected, such as various types of omics data and medical imaging modalities. Applying multi-view learning to these different sources of information can increase the accuracy of medical classification models compared with single-view procedures. However, collecting biomedical data can be expensive and/or burdening for patients, so that it is important to reduce the amount of required data collection. It is therefore necessary to develop multi-view learning methods which can accurately identify those views that are most import…
University of Minnesota School of Public Health（明尼苏达大学公共卫生学院）；Division of Biostatistics（生物统计学系）
Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform). This is limiting for data that take the form of bidimensionally linked matrices (e.g., multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies. In this paper, we propose BIDIFAC (Bidimensiona…
University of Manchester（曼彻斯特大学）；CRUK Manchester Institute（英国癌症研究中心曼彻斯特研究所）；Idiap Research Institute（Idiap研究所）
There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings. This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the …
