Evidence map›Paper›PMID 30854526›Full record

ArticleJournal of rare diseases research & treatment2016

Mechanism-based disease similarity.

Mehdi B Hamaneh, Yi-Kuo Yu

Abstract read
In one paragraph

Article in Journal of rare diseases research & treatment, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mehdi B HamanehNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Yi-Kuo YuNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.

Funding

Robust Accurate Identification of peptides from tandem mass spectrometry dataZIALM092404 · NLM · NATIONAL LIBRARY OF MEDICINE · PI YU, YI-KUO · 2009 to 2025
$13.2M
Intramural NIH HHS ZIA LM092404
6 · The paper itself

Abstract

In recent years several methods have been proposed to assign pairwise mechanism- based similarity scores to human diseases. Despite their differences in approach and performance, these methods work in a somewhat similar manner: first a set of biomolecules (genes, proteins, chemicals, etc.) is associated with each disease, and then a measure is defined to calculate the similarity between the sets assigned to a pair of diseases. Since the similarity score between two diseases is defined based on the underlying molecular processes, a high score may hint at a shared cause, and therefore a similar treatment, for both diseases. This is of great practical importance especially when a rare or newly-discovered disease, for which limited information is available, is found to be related to a disease with a known treatment. Thus, in this mini-review we briefly discuss the recently developed methods for computing mechanism-based disease- disease similarities.

Indexed as

Disease-disease similarityDisease-gene associationsgene expression datagene-gene association networkHuman disease networkProtein-protein interaction network

Identifiers

PMID30854526
PMCPMC6404756

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

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.