ArticleMolecular systems biology2008
Network-based global inference of human disease genes.
Article in Molecular systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 298 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
298 citing papers in PubMed.
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- Study on subtyping and Traditional Chinese Medicine treatment of depression based on machine learning and text mining.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- Identification of two molecularly and prognostically distinct subtypes in acral melanoma using network prediction method.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- Network Pharmacology and Molecular Dynamics Identified Potential Androgen Receptor-Targeted Metabolites inInternational journal of molecular sciences · 2025Article
- Mechanism of Qigu capsule as a treatment for sarcopenia based on network pharmacology and experimental validation.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- Mechanistic Exploration ofBioMed research international · 2025Article
- KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.Briefings in bioinformatics · 2024Article
- Quantitative Proteomics Combined with Network Pharmacology Analysis Unveils the Biological Basis of Schisandrin B in Treating Diabetic Nephropathy.Combinatorial chemistry & high throughput screening · 2024Article
- Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.Drug discovery today · 2024Review
- Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification.PloS one · 2024Article
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.Briefings in bioinformatics · 2023Review
- Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods.Cancer biology & medicine · 2023Review
- Article
- A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets.Scientific reports · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.Frontiers in genetics · 2023Article
- Construction and analysis of protein-protein interaction networks based on nuclear proteomics data of the desiccation-tolerantCommunicative & integrative biology · 2023Article
238 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
4 authors.
Funding
Abstract
Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein-protein interactions, disease phenotype similarities, and known gene-phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome-wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype-genotype relationships. The genome-wide prioritization of candidate genes for over 5000 human phenotypes, including those with under-characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.
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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.