Evidence map›Paper›PMID 33941233›Full record

ArticleBioData mining2021

A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism.

Maya Varma, Kelley M Paskov, Brianna S Chrisman, Min Woo Sun, Jae-Yoon Jung, Nate T Stockham, Peter Y Washington, Dennis P Wall

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioData mining, 2021. 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
–field-weighted citation impact, top 97% 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, 0 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

8 authors at 2 institutions in 1 country.

Maya VarmaDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-0693-7753
Kelley M PaskovDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Brianna S ChrismanDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Min Woo SunDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Jae-Yoon JungDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Nate T StockhamDepartment of Neuroscience, Stanford University, Stanford, CA, USA.
Peter Y WashingtonDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Dennis P WallDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA. dpwall@stanford.edu.
Stanford University · USStanford Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMachine learning approaches for predicting disease risk from high-dimensional whole genome sequence (WGS) data often result in unstable models that can be difficult to interpret, limiting the identification of putative sets of biomarkers. Here, we design and validate a graph-based methodology based on maximum flow, which leverages the presence of linkage disequilibrium (LD) to identify stable sets of variants associated with complex multigenic disorders.

resultsWe apply our method to a previously published logistic regression model trained to identify variants in simple repeat sequences associated with autism spectrum disorder (ASD); this L

conclusionOur method allows for the creation of machine learning models that can identify predictive variants. Our results help pave the way towards biomarker-based diagnosis methods for complex genetic disorders.

Indexed as

Feature selectionFeature stabilityLinkage disequilibriumMachine learningMaximum flowNetwork

Identifiers

PMID33941233
PMCPMC8091705
OpenAlexW3158604732

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.