For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) have been extensively used in embedding image and text data into lower dimensional latent spaces. In our project, we extend the idea of using a VAE model for low dimensional latent space extraction with the self-supervised learning technique of feature subsetting. With VAEs, the key idea is to make the model learn meaningful representations from different types of omics data, which could then be used for downstream tasks such as cancer type classification. The main goals are to overcome the curse of dimensionality and integrate methylation and expression data to combine information about different aspects of same tissue samples, and hopefully extract biologically relevant features. Our extension involves training encoder and decoder to reconstruct the data from just a subset of it. By doing this, we force the model to encode most important information in the latent representation. We also added an identity to the subsets so that the model knows which subset is being fed into it during training and testing. We experimented with our approach and found that SubOmiEmbed produces comparable results to the baseline OmiEmbed with a much smaller network and by using just a subset of the data. This work can be improved to integrate mutation-based genomic data as well.
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；National Supercomputing Center in Zhengzhou, Zhengzhou University（郑州大学郑州国家超级计算中心）
提出基于对称多头注意力编码器和解耦对比学习的 DEDUCE 模型，用于多组学癌症数据的无监督特征学习与聚类，实验显示其优于10个深度学习模型及现有方法，并识别出6种 AML 亚型。
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）
Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data
University of Idaho, Department of Computer Science（爱达荷大学计算机科学系）；Jouf University, College of Computer and Information Sciences, Department of Computer Science（朱夫大学计算机与信息科学学院计算机科学系）；University of Gezira, Faculty of Mathematical and Computer Sciences（杰兹拉大学数学与计算机科学学院）；Bentonville High School（伯恩斯维尔高中）；University of KwaZulu-Natal, School of Mathematics, Statistics and Computer Science（夸祖鲁-纳塔尔大学数学、统计与计算机科学学院）
Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science and Technology, Zhejiang University（浙江可感知智能系统重点实验室，计算机科学与技术学院，浙江大学）
TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
Mathematics and Statistical Science University of Idaho（数学与统计科学大学 Idaho 大学）；Computer Science and Engineering Mississippi State University（计算机科学与工程密苏里州立大学）
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization
The Ohio State University（俄亥俄州立大学）；Biomedical Informatics Department, The Ohio State University（俄亥俄州立大学生物医学信息学系）；Computer Science and Engineering Department, The Ohio State University（俄亥俄州立大学计算机科学与工程系）；Translational Data Analytics Institute, The Ohio State University（俄亥俄州立大学转化数据分析研究所）
Transforming Multi-Omics Integration with GANs: Applications in Alzheimer's and Cancer
Tulane Center for Biomedical Informatics（Tulane 生物医学信息学中心）；Genomics, Deming Department of Medicine, Tulane University（生物医学基因组学部， Tulane 大学医学部）；Department of Computer Science, Tulane University（计算机科学系， Tulane 大学）；Ochsner Center for Outcomes Research, Ochsner Research（Ochsner 研究中心， Ochsner 研究）
PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；Third Xiangya Hospital（湘雅三医院）；Hunan University（湖南大学）；Central South University（中南大学）
Massachusetts Institute of Technology, Computational and Systems Biology Program（麻省理工学院计算与系统生物学计划）；Broad Institute of MIT and Harvard（MIT和哈佛大学博德研究所）；Massachusetts Institute of Technology, Department of Biological Engineering（麻省理工学院生物工程系）
MLOmics: Cancer Multi-Omics Database for Machine Learning
Bioinformatics Center, Institute for Chemical Research, Kyoto University（京都大学生物信息学中心、化学研究所）；SANKEN, Osaka University（大阪大学SANKEN）；IRCN, The University of Tokyo（东京大学IRCN）；Institute for Quantitative Biosciences, The University of Tokyo（东京大学定量生物科学研究所）；Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Carle医学院）
Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
King’s College London（伦敦国王学院）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（科技研究局信息与通信研究所）；Shenzhen University（深圳大学）；University of Manchester（曼彻斯特大学）
Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Leeds Teaching Hospitals NHS Trust（利兹教学医院国民医疗服务信托）；University of Leeds（利兹大学）；Linköping University（林雪平大学）
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
College of Computer Science, Sichuan University（四川大学计算机学院）；Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences（中国科学院新疆物理化学研究所）；College of Mathematics, Sichuan University（四川大学数学学院）；School of Artificial Intelligence and Data Science, University of International Business and Economics（国际经贸大学人工智能与数据科学学院）
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）
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））
Heterogeneous Graph Attention Network With Multimodal Feature Selection for Cancer Multiomics Integration
School of Computer Science, The University of Sheffield（谢菲尔德大学计算机学院）；Centre for Machine Intelligence, The University of Sheffield（谢菲尔德大学机器智能中心）；School of Biosciences, The University of Sheffield（谢菲尔德大学生物科学学院）
Integrate Any Omics: Towards genome-wide data integration for patient stratification
Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Molecular Genetics, University of Toronto（多伦多大学分子遗传学系）；Princess Margaret Cancer Centre, University Health Network（玛格丽特公主癌症中心，大学健康网络）；Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Genetics and Genome Biology, the Hospital for Sick Children（病童医院遗传学与基因组生物学）
PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction
Cuiying Honors College（翠英荣誉学院）；School of Mathematics and Statistics（数学与统计学学院）；Shanghai Innovation Institute（上海创新研究院）；Department of Computer Science（计算机科学系）
Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data
National Research Council of Italy (CNR), Institute of Informatics（意大利国家研究理事会（CNR）信息学院）；Computational Biomedicine Unit, Department of Medical Sciences, University of Turin（都灵大学医学科学系计算生物医学单元）；University of Siena（锡耶纳大学）；Institute for High Performance Computing（高性能计算研究所）；Networking (ICAR), National Research Council of Italy (CNR)（网络（ICAR），意大利国家研究理事会（CNR））
