Graph machine learning for integrated multi-omics analysis

British Journal of Cancer, Год журнала: 2024, Номер 131(2), С. 205 - 211

Опубликована: Май 10, 2024

Abstract Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods data integration have been developed identification key elements that explain predict disease risk other biological outcomes. The heterogeneous graph representation multi-omics provides an advantage discerning patterns suitable predictive/exploratory analysis, thus permitting modeling complex relationships. Graph-based approaches—including neural networks—potentially offer a reliable methodological toolset can provide tangible alternative scientists clinicians seek ideas implementation strategies integrated analysis their omics sets biomedical research. workflows continue push limits technological envelope, this perspective focused literature review research articles which machine learning is utilized analyses, with several examples demonstrate effectiveness graph-based approaches.

Язык: Английский

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From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

Molecular Biotechnology, Год журнала: 2024, Номер unknown

Опубликована: Апрель 2, 2024

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for discovery, is underpinned by big data, transformative force in current era. Omics characterized its heterogeneity enormity, ushered biological biomedical research into data domain. Acknowledging significance integrating diverse omics strata, known as multi-omics studies, researchers delve intricate interrelationships among various layers. review navigates expansive landscape, showcasing tailored assays each layer through genomes to metabolomes. The sheer volume generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging robust tools. These datasets not only refine classification but also enhance diagnostics foster development therapeutic strategies. Through integration high-throughput focuses targeting modeling multiple disease-regulated networks, validating interactions targets, enhancing potential using network pharmacology approaches. Ultimately, this exploration aims illuminate impact era, shaping future research.

Язык: Английский

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The Venus score for the assessment of the quality and trustworthiness of biomedical datasets

BioData Mining, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 8, 2025

Abstract Biomedical datasets are the mainstays of computational biology and health informatics projects, can be found on multiple data platforms online or obtained from wet-lab biologists physicians. The quality trustworthiness these datasets, however, sometimes poor, producing bad results in turn, which harm patients subjects. To address this problem, policy-makers, researchers, consortia have proposed diverse regulations, guidelines, scores to assess increase reliability datasets. Although generally useful, they often incomplete impractical. guidelines Datasheets for Datasets , particular, too numerous; requirements Kaggle Dataset Usability Score focus non-scientific requisites (for example, including a cover image); European Union Artificial Intelligence Act (EU AI Act) sets forth sparse general governance requirements, we tailored biomedical AI. Against backdrop, introduce our new Venus score Our ranges 0 10 consists ten questions that anyone developing bioinformatics, medical informatics, cheminformatics dataset should answer before release. In study, first describe EU presenting their drawbacks. do so, reverse-engineer weights influential time report them study. We distill most important into domain, comprising score. apply twelve subdomains, electronic records, imaging, microarray bulk RNA-seq gene expression, cheminformatics, physiologic electrogram signals, text. Analyzing results, surface fine-grained strengths weaknesses popular as well aggregate trends. Most notably, find widespread tendency gloss over sources inaccuracy noise, may hinder reliable exploitation and, consequently, research results. Overall, confirm applicability utility data.

Язык: Английский

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Gene signatures for cancer research: A 25-year retrospective and future avenues

PLoS Computational Biology, Год журнала: 2024, Номер 20(10), С. e1012512 - e1012512

Опубликована: Окт. 16, 2024

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap inconsistencies among gene signatures across different cohorts pose challenges. dynamic nature of transcriptome, encompassing diverse RNA species functional complexities isoform levels, introduces intricacies, current face reproducibility issues due to unique transcriptomic landscape each patient. In this context, discrepancies arising from sequencing technologies, data algorithms, software tools further hinder consistency. While careful experimental design, analytical strategies, standardized protocols could enhance reproducibility, future prospects lie multiomics integration, machine learning techniques, open science practices, collaborative efforts. Standardized metrics, quality control measures, advancements single-cell RNA-seq will contribute unbiased signature identification. perspective article, we outline some thoughts insights addressing challenges, advanced methodologies enhancing reliability disease research.

Язык: Английский

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Tensor-Based Approaches for Omics Data Analysis: Applications, Challenges, and Future Directions

La Matematica, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Язык: Английский

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Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction

Briefings in Bioinformatics, Год журнала: 2025, Номер 26(2)

Опубликована: Март 1, 2025

The complementary information found in different modalities of patient data can aid more accurate modelling a patient's disease state and better understanding the underlying biological processes disease. However, analysis multi-modal, multi-omics presents many challenges. In this work, we compare performance variety ensemble machine learning (ML) algorithms that are capable late integration multi-class from modalities. methods their variations tested were (i) voting ensemble, with hard soft vote, (ii) meta learner, (iii) multi-modal AdaBoost model using learner to integrate on each boosting round, PB-MVBoost novel application mixture expert's model. These compared simple concatenation. We examine these an in-house study hepatocellular carcinoma, plus validation datasets studies breast cancer irritable bowel develop models achieve area under receiver operating curve up 0.85 find two boosted methods, vote best performing models. also stability features selected size clinical signature. Our work shows integrating omics effective ML enhances accuracy outcome predictions produces stable predictive than individual or provide recommendations for data.

Язык: Английский

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Integrating Metabolomics with Omics Techniques: Exploring Cutting-Edge Technologies in Comprehensive Biological Analysis

Опубликована: Янв. 1, 2025

Язык: Английский

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Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Cell Reports Methods, Год журнала: 2024, Номер 4(7), С. 100817 - 100817

Опубликована: Июль 1, 2024

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for prediction non-small cell lung cancer (NSCLC) patients, learning simultaneously computed tomography (CT) scan images, gene expression data, clinical information. The proposed models integrate patient-specific clinical, transcriptomic, imaging incorporate Kyoto Encyclopedia Genes Genomes (KEGG) Reactome pathway information, adding biological knowledge within process biomarkers molecular pathways. While both accurately stratify patients in high- low-risk groups when trained on a dataset only 130 introducing cross-attention mechanism sparse autoencoder significantly improves performance, highlighting tumor regions NSCLC-related genes as potential thus offering significant methodological advancement small imaging-omics-clinical samples.

Язык: Английский

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Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology

Опубликована: Авг. 26, 2024

The topic of predictive toxicology has been greatly influenced by recent progress in comprehending drug toxicity processes and enhancing medication development. integration omics technologies, such as transcriptomics, proteomics, metabolomics, with traditional toxicological assessments yielded extensive knowledge about the biological pathways implicated drug-induced toxicity. utilization a multi-omics method amplifies ability to identify biomarkers that can detect at an early stage, hence safety profile novel therapeutic medicines. Machine learning silico models, QSAR models multi-task deep algorithms, have become essential tools. They shown great accuracy predicting endpoints helped identification new targets. introduction microphysiological systems PBPK modeling enhanced transfer preclinical discoveries clinical results, providing more precise forecasts human reactions medications. Notwithstanding these progressions, obstacles diversity data complex nature require sophisticated computational techniques for efficient analysis. Continued cooperation established procedures are crucial fully utilize guaranteeing creation safer medicinal agents.

Язык: Английский

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A Commentary on Multi-omics Data Integration in Systems Vaccinology

Journal of Molecular Biology, Год журнала: 2024, Номер 436(8), С. 168522 - 168522

Опубликована: Март 7, 2024

Язык: Английский

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