ArticleBMC bioinformatics2019
Multiple-kernel learning for genomic data mining and prediction.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Interpretable and integrative analysis of single-cell multiomics with scMKL.Communications biology · 2025Article
- MLOmics: Cancer Multi-Omics Database for Machine Learning.Scientific data · 2025Article
- Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome.Frontiers in physiology · 2025Article
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning.American journal of human genetics · 2024Article
- Supervised multiple kernel learning approaches for multi-omics data integration.BioData mining · 2024Article
- GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.bioRxiv : the preprint server for biology · 2024Article
- Deep learning-based multimodal spatial transcriptomics analysis for cancer.Advances in cancer research · 2024Review
- Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.Sensors (Basel, Switzerland) · 2023Article
- Predicting functional effects of ion channel variants using new phenotypic machine learning methods.PLoS computational biology · 2023Article
- Multi-omic integration by machine learning (MIMaL).Bioinformatics (Oxford, England) · 2022Article
- Article
- Fenchel duality of Cox partial likelihood with an application in survival kernel learning.Artificial intelligence in medicine · 2021Article
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages.IEEE transactions on medical imaging · 2020Article
- Challenge-Enabled Machine Learning to Drug-Response Prediction.The AAPS journal · 2020Review
- Multiview learning for understanding functional multiomics.PLoS computational biology · 2020Review
- State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing.Frontiers in genetics · 2020Review
- MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data.BMC bioinformatics · 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
5 authors.
Funding
Abstract
backgroundAdvances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets.
resultsWe provide three implementations of MKL in R. These methods are applied to simulated data to illustrate that MKL can select appropriate models. We also apply MKL to combine clinical information with miRNA gene expression data of ovarian cancer study into a single analysis. Lastly, we show that MKL can identify gene sets that are known to play a role in the prognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as, identify new gene sets for the future research.
conclusionMultiple kernel learning coupled with modern optimization techniques provides a promising learning tool for building predictive models based on multi-source genomic data. MKL also provides an automated scheme for kernel prioritization and parameter tuning. The methods used in the paper are implemented as an R package called RMKL package, which is freely available for download through CRAN at https://CRAN.R-project.org/package=RMKL .
Indexed as
Identifiers
What OpenQuestion holds
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.