Evidence map›Paper›PMID 26215861›Full record

ReviewSeminars in nephrology2015

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes.

Yuanfang Guan, Sebastian Martini, Laura H Mariani

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Seminars in nephrology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 36% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 2 countries.

Yuanfang GuanDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI; Department of Internal Medicine, University of Michigan, Ann Arbor, MI; Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI.
Sebastian MartiniDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI; Nephrologisches Zentrum, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-University Munich, Munich, Germany.
Laura H MarianiDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI.
University of Michigan–Ann Arbor · USLudwig-Maximilians-Universität München · DE

Funding

Machine Learning for Drug Response PredictionR35GM133346 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yuanfang Guan · 2019 to 2026
$3.2M
Integrating context-specific networks to predict ataxia genesR21NS082212 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BURMEISTER, MARGIT, GUAN, YUANFANG · 2013 to 2014
$426k
NIGMS NIH HHS R35 GM133346NINDS NIH HHS R21 NS082212
6 · The paper itself

Abstract

In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However, it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data, to assign functional roles of genetic variations involved in kidney function. Furthermore, we introduce state-of-the-art machine learning algorithms, Bayesian networks, support vector machines, and Gaussian process regression, which have been applied successfully to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be deployed successfully in the nephrology field in the near future.

Indexed as

AlgorithmsComputational BiologyGene Expression ProfilingGene Regulatory NetworksGenotypeHumansKidney DiseasesPhenotypePrognosisclinical outcomesGene regulationpredictionsSNP

Identifiers

PMID26215861
PMCPMC4518206
OpenAlexW1930670493

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.