ReviewBioinformatics and biology insights2018
Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).
Review in Bioinformatics and biology insights, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of Anti-Diabetic Drugs on Fracture Risk: A Systematic Review and Network Meta-Analysis.Frontiers in endocrinology · 2021Pooled it
- moiraine: an R package to construct reproducible pipelines for the application and comparison of multi-omics integration methods.Bioinformatics (Oxford, England) · 2026Article
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- GLARE: discovering hidden patterns in spaceflight transcriptome using representation learning.NPJ microgravity · 2025Article
- Design aspects for prognostic factor studies.BMJ open · 2025Review
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Unlocking Fungal Potential: The CRISPR-Cas System as a Strategy for Secondary Metabolite Discovery.Journal of fungi (Basel, Switzerland) · 2024Review
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- Carotenoids in Health as Studied by Omics-Related Endpoints.Advances in nutrition (Bethesda, Md.) · 2023Review
- Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.Briefings in bioinformatics · 2022Article
- A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.Nature communications · 2022Article
- Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics.Applied microbiology and biotechnology · 2022Review
- Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation.Journal of clinical and translational hepatology · 2022Review
- Multi-omics protein-coding units as massively parallel Bayesian networks: Empirical validation of causality structure.iScience · 2022Article
- Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization.PloS one · 2022Article
- Nextcast: A software suite to analyse and model toxicogenomics data.Computational and structural biotechnology journal · 2022Article
- Transcriptomics Coupled to Proteomics Reveals Novel Targets for the Protective Role of Spermine in Diabetic Cardiomyopathy.Oxidative medicine and cellular longevity · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
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.