ReviewBriefings in bioinformatics2015
Kernel methods for large-scale genomic data analysis.
Review in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.Research square · 2026Article
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Article
- Efficient large-scale genomic prediction in approximate genome-based kernel model.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Supervised multiple kernel learning approaches for multi-omics data integration.BioData mining · 2024Article
- Visualizing scRNA-Seq data at population scale with GloScope.Genome biology · 2024Article
- Visualizing scRNA-Seq Data at Population Scale with GloScope.bioRxiv : the preprint server for biology · 2024Article
- Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms.Scientific reports · 2024Article
- Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis.Frontiers in medicine · 2023Article
- SIMBSIG: similarity search and clustering for biobank-scale data.Bioinformatics (Oxford, England) · 2023Article
- Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies.Journal of translational medicine · 2021Article
- IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.PLoS computational biology · 2021Article
- Segmentation of Change in Surface Geometry Analysis for Cultural Heritage Applications.Sensors (Basel, Switzerland) · 2021Article
- Fenchel duality of Cox partial likelihood with an application in survival kernel learning.Artificial intelligence in medicine · 2021Article
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.Nature protocols · 2020Article
- Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions.Frontiers in genetics · 2020Article
- Semblance: An empirical similarity kernel on probability spaces.Science advances · 2019Article
- Machine and deep learning meet genome-scale metabolic modeling.PLoS computational biology · 2019Review
- A weighted kernel machine regression approach to environmental pollutants and infertility.Statistics in medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion.
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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.