ReviewMolecular therapy. Nucleic acids2019
Computational Methods for Identifying Similar Diseases.
Review in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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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.
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Who cites it
52 citing papers in PubMed.
- Enhanced drug-disease association prediction through representation learning on similarity networks.Biology methods & protocols · 2026Article
- Improving computational drug repositioning through multi-source disease similarity networks.Scientific reports · 2025Article
- Causal Relationship Between Hypertension And Vertigo: A Mendelian Randomization Study.Current neurovascular research · 2025Article
- Integration of the bulk transcriptome and single-cell transcriptome reveals efferocytosis features in lung adenocarcinoma prognosis and immunotherapy by combining deep learning.Cancer cell international · 2024Article
- A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning.Briefings in bioinformatics · 2023Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.Genes · 2022Review
- Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction.PeerJ · 2022Article
- ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition.BMC bioinformatics · 2021Article
- A pipeline for RNA-seq based eQTL analysis with automated quality control procedures.BMC bioinformatics · 2021Article
- Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.Briefings in bioinformatics · 2021Review
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
- Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data.Frontiers in cell and developmental biology · 2021Article
- iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.Computational and mathematical methods in medicine · 2021Article
- Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China.Frontiers in cell and developmental biology · 2021Article
- Article
- Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases.Frontiers in genetics · 2021Review
- iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms "category-based," "simulated-patient-based," and "benchmark-data-based" were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity.
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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 Socratic graph.