Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data

We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data. However, there is a lack of studies that use SSL methods to exploit inter-omics relationships on unlabelled multi-omics data. In this work, we develop a novel and efficient pre-training paradigm that consists of various SS…

Stacked Autoencoder Based Multi-Omics Data Integration for Cancer Survival Prediction

Cancer survival prediction is important for developing personalized treatments and inducing disease-causing mechanisms. Multi-omics data integration is attracting widespread interest in cancer research for providing information for understanding cancer progression at multiple genetic levels. Many works, however, are limited because of the high dimensionality and heterogeneity of multi-omics data. In this paper, we propose a novel method to integrate multi-omics data for cancer survival prediction, called Stacked AutoEncoder-based Survival Prediction Neural Network (SAEsurv-net). In the cancer …

MLOmics：面向机器学习的癌症多组学数据库

MLOmics: Cancer Multi-Omics Database for Machine Learning

针对现有公共癌症多组学数据库无法直接供机器学习模型使用的问题，本文提出开放数据库MLOmics，含8314份覆盖32种癌症的四类组学样本及基准等，助力相关模型开发与跨学科分析。

用于癌症分子亚型分类的多模态图神经网络框架

A Multimodal Graph Neural Network Framework of Cancer Molecular Subtype Classification

提出端到端多组学GNN框架，用异质多层图融合组学内与组学间连接及全局基因组特征，提升癌症分子亚型分类性能。

基于先进图神经网络的多组学整合用于癌症分类的对比分析

Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification

本研究针对多组学数据整合的高维度等挑战，构建了结合LASSO特征选择的三种图神经网络模型，用于31种癌症分类，其中LASSO-MOGAT取得95.9%的最优准确率，证明图架构整合多组学数据可提升分类效果并助力癌症机制研究。

通过展开图平滑先验进行癌症亚型推断的多组学分析

Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors

针对现有方法忽视组学间耦合、难以解析癌症亚型异质性的问题，提出GTMancer框架，利用对比学习嵌入多组学数据，通过展开图优化和双注意力系数捕获结构先验，在七个数据集上超越现有方法。

Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models

Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computational models to predict and prioritize effective drug combinations is important for combinatory therapy discovery in cancer. In this study, we proposed a novel deep learning model, AuDNNsynergy, to prediction drug combinations by integrating multi-omics data and chemical structur…

MODIS：面向小型及未配对数据集的多组学数据整合

MODIS: Multi-Omics Data Integration for Small and unpaired datasets

本文提出半监督框架MODIS，通过结合大型参考数据库与小型目标数据集训练，解决多组学未配对、数据稀缺及类别不平衡问题，在TCGA等数据集上验证了其高预测精度与鲁棒性。

使用核机器回归识别多组学相互作用以发现肺癌药物靶点

Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression

该研究整合TCGA肺癌多组学数据，结合差异分析与KMR识别组学相互作用，发现38个相关基因、8个重点靶点及3种候选药物。

Bidimensional linked matrix factorization for pan-omics pan-cancer analysis

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, pan-omics pan-cancer analysis, have extended our knowledge of molecular heterogenity beyond what was observed in single tumor and single platform studies. However, these studies have been limited by available statistical methodology. We propose a flexible approach to the simultaneous factorization and decomposition of variation across such bidimensionally linked matrices, BI…

手工特征与深度学习放射组学的非冗余组合：在胰腺癌早期检测中的应用

Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer

本研究针对手工与深度学习放射组学特征冗余问题，通过VAE提取与手工特征互信息最小化的DLR特征，组合后用于胰腺癌早期检测，在独立测试集上实现了AUC提升。

CMGL：基于置信度的多组学图学习用于癌症亚型分类

CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification

CMGL通过证据深度学习估计每样本组学可靠性，指导跨组学融合与图构建，提升癌症亚型分类性能。

利用完整和部分多组学数据集进行癌症亚型分类的多层矩阵分解

Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset

针对当前癌症亚型分类依赖完整多组学数据、忽略组学多层隐式关系的问题，提出多层矩阵分解（MLMF）方法，通过多组学特征矩阵的多层分解与融合及类别指示矩阵处理缺失数据，在10个数据集上性能优于或媲美现有方法。

LASSO-MOGAT：一种用于癌症分类的多组学图注意力框架

LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification

本文提出LASSO-MOGAT框架，结合LIMMA、LASSO特征选择与图注意力网络整合mRNA、microRNA和DNA甲基化数据，利用PPI网络对31种癌症类型进行分类，并通过五折交叉验证证明了其精确性和可靠性。

Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data

Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtype-Former, a deep learning method based on MLP and Transformer Block, to extract the low-dimensional representation of the multi-omics data. K-means and Consensus Clustering are also used to achieve ac…

DEDUCE：基于多头注意力解耦对比学习的多组学数据癌症亚型发现

DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data

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。

通过整合多组学数据中的数据集间与数据集内关系识别癌症亚型

Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data

本文提出 LIDAF 方法，通过典型相关分析与三步亲和矩阵融合整合多组学数据的数据集间和数据集内线性关系，提升癌症亚型聚类与生存区分性能。

用于增强肿瘤学多组学数据分析的自归一化基础模型

Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology

本研究提出在33种癌症多组学数据上训练的基础模型SeNMo，可高效处理高维低样本的多组学数据，在总生存期预测、癌症类型分类、三级淋巴结构预测等任务上均表现优异且泛化性强。

图神经网络在多组学癌症研究中的应用：结构化综述

Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey

本综述系统梳理了图神经网络在多组学癌症研究中的应用，按组学层、网络结构和生物学任务分类，并指出混合可解释模型、注意力机制及患者特异性图等新兴趋势。

OmicsCL：用于癌症亚型发现和生存分层的无监督对比学习

OmicsCL: Unsupervised Contrastive Learning for Cancer Subtype Discovery and Survival Stratification

提出OmicsCL框架，通过生存感知对比损失将异构多组学数据嵌入统一潜在空间，无需标签即可发现癌症亚型并实现生存分层，在TCGA BRCA数据集上表现出强无监督一致性和鲁棒性。

基于多组学特征选择的遗传算法：癌症生存分析中的比较研究

Genetic algorithms for multi-omic feature selection: a comparative study in cancer survival analysis

本文提出Sweeping*算法，结合单视和多视优化，通过多组学层间交互提升生存预测精度，验证了多组学整合在癌症生存分析中的有效性。

MoXGATE：用于多组学胃肠道癌症亚型分类的模态感知交叉注意力

MoXGATE: Modality-aware cross-attention for multi-omic gastrointestinal cancer sub-type classification

提出 MoXGATE 深度学习框架，通过交叉注意力与可学习模态权重融合多组学特征，并结合 focal loss，在 TCGA 多癌种亚型分类中达到 95% 准确率。

可解释图Kolmogorov-Arnold网络用于基于多组学数据的多癌症分类和生物标志物识别

Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data

提出MOGKAN框架，结合图神经网络与Kolmogorov-Arnold定理，利用多组学数据和PPI网络实现31种癌症的高精度分类与可解释生物标志物识别。

异质图注意力网络改进癌症多组学整合

Heterogeneous graph attention network improves cancer multiomics integration

本文提出HeteroGATomics，利用多智能体联合特征选择和异质图注意力网络整合多组学数据，在三个癌症数据集上提升诊断性能并识别关键生物标志物。

用于癌症多组学整合、通路活性推断与风险分层的生物信息引导深度神经网络

Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer

针对多组学整合中可解释性与表征能力的权衡难题，提出通路活性自编码器，依托生物先验约束实现多组学有效整合，在乳腺癌相关任务上取得优异表现，给出实用整合指南。

基于多组学的乳腺癌预测机器学习模型基准测试

Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction

研究针对基于多组学数据预测乳腺癌雌激素受体（ER）状态，对随机森林等经典机器学习模型进行基准测试，采用严格实验框架，结果显示RNA表达预测信号最强，多组学整合有改进，随机森林性能最佳，还选出相关基因支持模型有效性。

PACS：基于多头注意力机制模型的多组学数据癌症亚型预测与分析

PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model

针对癌症异质性导致的亚型分类需求，本研究提出有监督多头注意力机制模型SMA，通过特征共享与Siamese多头注意力编码器融合学习多组学特征，在三类数据集上相较AE、CNN、GNN类模型取得更优分类效果，为多组学研究提供新方法。

A Framework for Implementing Machine Learning on Omics Data

The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These data are often generated across different technologies in different labs, and frequently with high dimensionality. In this paper we present a framework for combining -omics data sets, and for handling high dimensional data, making -omics research more accessible to machine learning applications. We demonstrate the success of this framework…

MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy

We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment. Our model learns drug representations at multiple scales based on a drug-protein interaction network and metadata. Structural properties of compounds and proteins are encoded to create vertex features for a message-passing scheme that operates on the bipartite interaction graph. Propagated messages form multi-resolution drug representations which we utilized to create drug pair descriptors. By conditioning the drug c…

TF-DWGNet: 基于张量融合的有向加权图神经网络用于多组学癌症亚型分类

TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification

提出TF-DWGNet框架，结合基于树的有向加权图构建与张量融合机制，解决多组学数据异质性和高阶交互问题，在癌症亚型分类中优于现有方法并提供可解释性。

迈向基于AI的精准肿瘤学：基于多组学数据的个性化反事实治疗建议机器学习框架

Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data

提出一个基于多组学数据的模块化机器学习框架，通过聚合专业反事实专家提供个性化癌症治疗建议，解决高维数据和分配偏差问题，为临床决策提供校准置信度和解释。

IGCN：用于多组学整合中患者层面洞察与生物标志物发现的整合图卷积网络

IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration

本文提出名为IGCN的整合图卷积网络，通过识别不同组学数据对患者预测的重要性并定位关键生物标志物，解决了多组学整合分析中缺乏预测可解释性的问题，在癌症亚型与生物医学分类任务中表现优于现有方法。

通过混合集成特征选择和多组学数据优化胰腺癌的预后生物标志物发现

Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data

本文提出混合集成特征选择方法，结合数据子采样与多种预后模型，通过投票理论机制整合嵌入式和包装策略，提升胰腺癌预后生物标志物的预测性能和稳定性。

用于癌症亚型诊断的神经群体解码与不平衡多组学数据集

Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis

本文将WTA脉冲神经网络用于TCGA多组学癌症亚型诊断，结合基于Kohonen自组织映射的基因相似性编码，揭示群体解码方法对分类性能及不平衡数据的显著影响。

机器学习在组学数据中的应用

Machine learning applied to omics data

本章回顾并评估了随机森林和惩罚多项逻辑回归在胰腺癌组学整合分析中的应用，并提出关联规则以提升预测能力，在TCGA数据上验证了预测免疫浸润的良好性能。

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

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 exten…

整合病理图像与高通量组学数据的基于机器学习的癌症总生存期预测多模态预后模型：一项系统综述

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review

该系统综述梳理了整合病理全切片图像与高通量组学的机器学习癌症预后模型研究，总结了方法类型与性能表现，指出领域存在方法严谨性不足、缺乏外部验证与临床评估等问题。

DoGMA：一种用于肿瘤学多组学对齐与多任务学习的中心法则引导基础模型

DoGMA: A Central-Dogma-Guided Foundation Model for Multi-Omics Alignment and Multi-Task Learning in Oncology

本研究提出中心法则引导的多组学基础模型DoGMA，通过Transformer-MoE架构结合定向注意力与掩码分层多组学重构预训练，在泛癌多组学下游任务中展现出优异性能，为多组学注意力机制设计提供新方向。

多组学与量子机器学习融合用于肺癌亚型分类

Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification

本文提出利用量子机器学习整合多组学数据并进行特征选择，以实现肺鳞状细胞癌和肺腺癌两种肺癌亚型的诊断分类，并开发了寻找最佳区分特征的方法，具有生物标志物发现潜力。

整合任意组学：面向全基因组数据整合的患者分层

Integrate Any Omics: Towards genome-wide data integration for patient stratification

本文提出 IntegrAO 无监督框架，利用图神经网络整合不完整多组学数据并分类新患者，在五个癌症队列上验证了其对缺失数据的鲁棒性和分类准确性。

Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis

Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology. However, multi-omic data has the "big p, small N" problem (the number of features is large, but the number of samples is small), it is challenging to train a complicated machine learning model from the multi-omic data alone and make it generalize well. Here we propose a framework termed Multi-view Factorization AutoEncoder with network constraints to integrate multi-omic data with domain knowledge (biological interactions …

Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders

Cancer is a complex disease with significant social and economic impact. Advancements in high-throughput molecular assays and the reduced cost for performing high-quality multi-omics measurements have fuelled insights through machine learning . Previous studies have shown promise on using multiple omic layers to predict survival and stratify cancer patients. In this paper, we developed a Supervised Autoencoder (SAE) model for survival-based multi-omic integration which improves upon previous work, and report a Concrete Supervised Autoencoder model (CSAE), which uses feature selection to jointl…

PathMoG：一种以通路为中心的模块化图神经网络用于多组学生存预测

PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

PathMoG通过模块化图神经网络整合多组学数据，提升癌症生存预测的准确性，提供多层级的可解释性。

A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine

An unsolved fundamental problem in biology and ecology is to predict observable traits (phenotypes) from a new genetic constitution (genotype) of an organism under environmental perturbations (e.g., drug treatment). The emergence of multiple omics data provides new opportunities but imposes great challenges in the predictive modeling of genotype-phenotype associations. Firstly, the high-dimensionality of genomics data and the lack of labeled data often make the existing supervised learning techniques less successful. Secondly, it is a challenging task to integrate heterogeneous omics data from…

理解乳腺癌生存：在多组学数据上使用因果发现与语言模型

Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data

本文针对乳腺癌生存预测中因果发现评估困难的问题，利用PC、GES和广义精度矩阵算法分析705名患者的多组学数据，并结合BlueBERT等生物医学语言模型验证结果，以揭示可靠的临床因果关系。

Multimodal Learning for Multi-Omics: A Survey

With advanced imaging, sequencing, and profiling technologies, multiple omics data become increasingly available and hold promises for many healthcare applications such as cancer diagnosis and treatment. Multimodal learning for integrative multi-omics analysis can help researchers and practitioners gain deep insights into human diseases and improve clinical decisions. However, several challenges are hindering the development in this area, including the availability of easily accessible open-source tools. This survey aims to provide an up-to-date overview of the data challenges, fusion approach…

Large-scale benchmark study of survival prediction methods using multi-omics data

Multi-omics data, that is, datasets containing different types of high-dimensional molecular variables (often in addition to classical clinical variables), are increasingly generated for the investigation of various diseases. Nevertheless, questions remain regarding the usefulness of multi-omics data for the prediction of disease outcomes such as survival time. It is also unclear which methods are most appropriate to derive such prediction models. We aim to give some answers to these questions by means of a large-scale benchmark study using real data. Different prediction methods from machine …

ARO：面向多组学数据的对齐表示学习

ARO: Aligned Representation learning for multi-Omics data

ARO提出一种对齐表示学习方法，从多组学癌症数据中学习表示以重建缺失和未配对模态，在有限数据下实现高重建精度（MSE 0.15），并支持下游分类任务，提供经济高效的多组学分析方案。

基于图卷积网络的多组学模态融合与蛋白质网络嵌入建模达拉菲尼反应

Modeling Dabrafenib Response Using Multi-Omics Modality Fusion and Protein Network Embeddings Based on Graph Convolutional Networks

本研究通过多组学融合与图卷积网络提升达拉菲尼反应预测，揭示互补分子决定因素。

生成肿瘤空间蛋白质组的反事实解释，以发现增强免疫浸润的有效策略

Generating counterfactual explanations of tumor spatial proteomes to discover effective strategies for enhancing immune infiltration

本研究利用空间组学数据，通过自监督学习和反事实优化，预测并设计增强肿瘤T细胞浸润的扰动策略，为癌症免疫治疗提供新范式。

The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data Integration

Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprecedented depth and scale. A large amount of genomic data is now distributed through consortia like The Cancer Genome Atlas (TCGA), where specific types of biological information on specific type of tissue or cell are available. In cancer research, the challenge is now to perform integrative analyses of high-dimensional multi-omic data with the goal to better understand genomic processes that correlate with cancer outc…

通过scRNA-seq进行泛癌基因集发现以优化基于深度学习的下游任务

Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks

本研究针对泛癌研究中RNA-seq数据高维复杂的问题，提出基于scRNA-seq结合hdWGCNA与XGBoost筛选基因集的方法，在多个泛癌下游深度学习任务中性能优于参考基因集，还发现了关键泛癌生物标志物。

乳腺癌的量子增强分层：探索真实组学数据的量子表达力

Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data

本研究探索量子核在乳腺癌分子亚型分类中的表达力与噪声鲁棒性，发现其能以更少数据实现与经典方法相当的聚类效果，并展示在精准肿瘤学中的应用潜力。

UnPaSt：通过多组学数据的聚类进行无监督患者分层

UnPaSt: unsupervised patient stratification by biclustering of omics data

UnPaSt通过双聚类方法在多组学数据中实现无监督患者分层，有效识别疾病亚型并揭示数据异质性。