ArticleFrontiers in oncology2020
Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Integrative multi-omics profiling for early diagnosis, stratification and personalized management of chronic kidney disease: a new paradigm.Clinical and experimental medicine · 2025Review
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.Scientific reports · 2025Article
- Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome.Scientific data · 2024Article
- A framework for block-wise missing data in multi-omics.PloS one · 2024Article
- TowardsGenes · 2023Article
- Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response.Vaccines · 2023Article
- Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis.BioData mining · 2023Article
- Network-based multi-omics integration reveals metabolic at-risk profile within treated HIV-infection.eLife · 2023Article
- Data mining identifies novel RNA-binding proteins involved in colon and rectal carcinomas.Frontiers in cell and developmental biology · 2023Article
- HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach.Viruses · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- Network Analysis of Microarray Data.Methods in molecular biology (Clifton, N.J.) · 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
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER,
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.