Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view cancer patient data. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our developed Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms.
TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes
Tianjin University（天津大学）；Northwestern Polytechnical University（西北工业大学）；La Trobe University（拉筹伯大学）；Shantou University（汕头大学）；Yangtze River Delta Research Institute of Northwestern Polytechnical University（西北工业大学长三角研究院）
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）
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。
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）
One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；Radiopharmacy Center, Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院放射药学中心）
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；University of São Paulo（圣保罗大学）
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）
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）
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）
Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification
DISIT, Computer Science Institute, University of Piemonte Orientale（DISIT计算机科学研究所，皮埃蒙特东方大学）；Center for Translational Research on Autoimmune and Allergic Diseases (CAAD), Department of Health Sciences, University of Piemonte Orientale（自身免疫和过敏疾病转化研究中心（CAAD），健康科学系，皮埃蒙特东方大学）；Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Department of Health Sciences, University of Piemonte Orientale（自身免疫疾病跨学科研究中心（IRCAD），健康科学系，皮埃蒙特东方大学）
提出一种基于 MAE 与随机裁剪的自监督框架，从 WSI 学习组织病理图像表示，并通过线性探针完成乳腺癌多亚型分类。
Classification of lung cancer subtypes on CT images with synthetic pathological priors
Research Center for Healthcare Data Science（医疗数据科学研究中心）；Zhejiang Lab（浙江大学实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Northwestern Polytechnical University（西北工业大学）；Institute of Computer Graphics and Vision（计算机图形学与视觉研究所）；Graz University of Technology（格拉茨工业大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Computer Science（计算机科学系）；Rutgers University（罗格斯大学）
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）
Machine Learning Characterization of Cancer Patients-Derived Extracellular Vesicles using Vibrational Spectroscopies
Concordia University（康考迪亚大学）；Université de Montréal（蒙特利尔大学）；Research Institute of the McGill University Health Centre（麦吉尔大学健康中心研究所）；McGill University（麦吉尔大学）；Institut national de la recherche scientifique（国家科学研究院）；University of British Columbia（不列颠哥伦比亚大学）；Istituto Mediterraneo di Oncologia（地中海肿瘤研究所）；Fondazione Gemelli-Giglio（杰梅利-吉利奥基金会）
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Bioinformatics Center, Kyoto University（京都大学生物信息学中心）；SANKEN, Osaka University（大阪大学SANKEN（大阪大学蛋白质研究所））；National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究所）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images
Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València（瓦伦西亚理工大学生物工程研究与创新研究所）；Hospital Universitario y Politécnico La Fe（拉费大学理工医院）
UnPaSt: unsupervised patient stratification by biclustering of omics data
University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑工业大学）；Netherlands Cancer Institute（荷兰癌症研究所）；ACMetric；University of Campinas（坎皮纳斯大学）；University of British Columbia（不列颠哥伦比亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Altius Institute for Biomedical Sciences（Altius生物医学科学研究所）；University of Liège（列日大学）；Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Simon Fraser University（西蒙弗雷泽大学）；Vancouver Prostate Centre（温哥华前列腺中心）；Leibniz Institute of Virology（莱布尼茨病毒学研究所）；University of Southern Denmark（南丹麦大学）
