ArticleScientific reports2017
Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Co-expression Gene Networks and Machine-learning Algorithms Unveil a Core Genetic Toolkit for Reproductive Division of Labour in Rudimentary Insect Societies.Genome biology and evolution · 2023Pooled it
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Mini-Galaxy: Rethinking Complex Human Diseases Through the Lens of Systems Biology and Multilayered AI Network Perspectives.International journal of molecular sciences · 2026Review
- Integrative machine learning-guidedRSC advances · 2025Article
- Oxytocin and vasopressin enhance social pain empathy via common and distinct of neural expressions, genetic pathways, and networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Glucagon-like Peptide-1 Receptor Agonists: A New Frontier in Treating Alcohol Use Disorder.Brain sciences · 2025Review
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Neural Patterns of Social Pain in the Brain-Wide Representations Across Social Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways.European journal of medical research · 2024Article
- Machine learning-guided synthesis of nanomaterials for breast cancer therapy.Scientific reports · 2024Article
- Gene expression analysis identifies hub genes and pathways distinguishing fatal from survivor outcomes of Ebola virus disease.FASEB bioAdvances · 2024Article
- Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling.The EPMA journal · 2024Article
- Integrated image and location analysis for wound classification: a deep learning approach.Scientific reports · 2024Article
- Review
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
- Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.Drug discovery today · 2024Review
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- Learn from antibody-drug conjugates: consideration in the future construction of peptide-drug conjugates for cancer therapy.Experimental hematology & oncology · 2022Review
- LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction.Life (Basel, Switzerland) · 2022Article
- Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10
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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.