ReviewJournal of the Royal Society, Interface2015
Methods for biological data integration: perspectives and challenges.
Review in Journal of the Royal Society, Interface, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 2 of them syntheses 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
106 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Gene signature associated with benign neurofibroma transformation to malignant peripheral nerve sheath tumors.PloS one · 2017Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- BioOne: a national-scale platform for integrated discovery and utilization of diverse biological resources in South Korea.Genomics & informatics · 2026Article
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Unlocking the power of extracellular vesicles: multi-omics integration for cancer biomarker discovery.Biomarker research · 2026Review
- Multi-omics strategies for biomarker discovery and application in personalized oncology.Molecular biomedicine · 2025Review
- Early cancer detection via multi-omics cfDNA fragmentation using early-late fusion neural network with sample-modality evaluation.Briefings in bioinformatics · 2025Article
- Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.Brain : a journal of neurology · 2025Article
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- A computational framework for extracting biological insights from SRA cancer data.Scientific reports · 2025Article
- Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.Biomolecules · 2025Review
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Computational methods for alignment and integration of spatially resolved transcriptomics data.Computational and structural biotechnology journal · 2024Review
- Prediction of future dementia among patients with mild cognitive impairment (MCI) by integrating multimodal clinical data.Heliyon · 2024Article
- Constructing a Clinical Patient Similarity Network of Gastric Cancer.Bioengineering (Basel, Switzerland) · 2024Article
- Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.Environment international · 2024Article
- Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.Biomedicines · 2024Review
- Towards the Generation of Medical Imaging Classifiers Robust to Common Perturbations.BioMedInformatics · 2024Article
46 more citing papers are in PubMed but not listed here.
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
Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different state-of-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.
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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.