Fig. 1

Schematic workflow of creating the MLOmics. The process starts with collecting patient samples covering 32 cancer types from the TCGA project. All resources in diverse data types and sizes are uniformly integrated and processed to contain data of four omics types. Datasets for benchmark ML tasks were constructed based on the processed data. MLOmics also selected baselines, metrics, and resources to support downstream biological analysis. Overview of the MLOmics. MLOmics provides an interface for developing and evaluating machine learning models based on cancer multi-omics data. MLOmics provides datasets in three feature scales for 20 classification, clustering, and omics imputation learning tasks. MLOmics also provides statistical, ML, and DL baselines for each task, which are evaluated by fair metrics. Bio-knowledge database linking with MLOmics. MLOmics provides resources to link with other bio-knowledge databases, enabling the integration of outer resources for applications such as ML evaluation, gene-disease association exploration, network inference, and functional analysis.