ReviewComputational and structural biotechnology journal2024
Knowledge-guided learning methods for integrative analysis of multi-omics data.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Omics Approaches in Hantavirus Research: Current Advances, Challenges, and Future Perspectives.Biotech (Basel (Switzerland)) · 2026Review
- Decoding the significance of TERT gene in Polycystic Ovary Syndrome: From molecular mechanisms to emerging diagnostic and therapeutic strategies.Journal of ovarian research · 2026Review
- Coordinated DNA methyltransferase 3A and methyltransferase-like 7A activity reprograms the tumor microenvironment through discoidin domain receptor 1 signaling.Cancer biology & medicine · 2026Article
- Integrative Modeling of Read Depth and B-Allele Frequency Improves Single-Cell Copy Number Calling from Targeted DNA Sequencing Panels.Computational and structural biotechnology journal · 2026Article
- A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification.Computational and structural biotechnology journal · 2026Article
- Advancing Intervertebral Disc Biology via Omics: Implications for Nucleus Pulposus Progenitor Cell-Based Regeneration.JOR spine · 2025Review
- Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.Clinical and experimental medicine · 2025Review
- Refining treatment strategies for non-small cell lung cancer lacking actionable mutations: insights from multi-omics studies.British journal of cancer · 2025Review
- Laser capture microdissection.Nature reviews. Methods primers · 2025Article
- Multi-omics decodes host-specific and environmental microbiome interactions in sepsis.Frontiers in microbiology · 2025Review
- Saliva Proteome, Metabolome and Microbiome Signatures for Detection of Alzheimer's Disease.Metabolites · 2024Article
- Article
- Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells.Biomedicines · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.