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Artificial Intelligence/ML in Molecular Cancer Research
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Artificial Intelligence/ML in Molecular Cancer Research

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 3649

Special Issue Editor

Dr. Giovanni Cugliari

E-Mail Website
Guest Editor
Department of Medical Sciences, University of Turin, 10126 Turin, Italy
Interests: epigenetics; DNA methylation; cancer; statistical methods; artificial intelligence; machine learning; multi-omics analyses
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer Disease is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities can be very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from risk assessment, early detection and prediction, tumorigenesis, disease progression and treatment effects. Artificial intelligence (AI), specifically machine/deep learning algorithms, could make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. The Special Issue will be focused on the integrative approach to several OMICs platforms to connect simultaneously Genome, Epigenome, Transcriptome, Proteome, Metabolome in complex diseases. The secondary endpoint is the application of advanced statistical methods with the goal to infer about causality in desperate study design as case-control, prospective, cross-sectional, systematic review and metanalysis. Some examples should be Genetic/Epigenetic/Transcriptomic Risk Score, Mediation, Mendelian Randomization, Directed Acyclic Graph, Structural Equation Modelling, Latent variables, Bayesian inference, MCMC and permutation, Clustering, Cross fold validation, Imputation, Forecasting and Aging-related biomarkers with the aim to add information related to the relationship between causal inference and biological function applied to subject clustering, early detection, prognosis estimation, treatment response evaluation during follow-up. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Dr. Giovanni Cugliari
Guest Editor

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Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • cancer
  • multi-omics analysis
  • statistical methods

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Published Papers (1 paper)

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Research

16 pages, 2853 KiB  
Article
MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction
by Raihanul Bari Tanvir, Md Mezbahul Islam, Masrur Sobhan, Dongsheng Luo and Ananda Mohan Mondal
Int. J. Mol. Sci. 2024, 25(5), 2788; https://doi.org/10.3390/ijms25052788 - 28 Feb 2024
Cited by 5 | Viewed by 3235
Abstract
Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics [...] Read more.
Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients’ profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features. Full article
(This article belongs to the Special Issue Artificial Intelligence/ML in Molecular Cancer Research)
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