Evidence map›Paper›PMID 30649721›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2019

Biological Interpretation of Complex Genomic Data.

Kathleen M Fisch

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase.Database : the journal of biological databases and curation · 2022
    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

1 author.

Kathleen M FischDepartment of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA. KFisch@ucsd.edu.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
NCATS NIH HHS UL1 TR001442
6 · The paper itself

Abstract

Tumor genomic profiling involves analyzing many data types to produce a molecular profile of a tumor. Many of these analyses result in a prioritized list of genes or variants for further study. Interpretation of these lists relies upon annotating and extracting biological meaning through literature and manually curated knowledge bases. This chapter will describe several of these approaches including gene annotation, variant annotation, clinical annotation, functional enrichment analyses, and network analyses. Taken together or individually, these analyses will result in a biological understanding of complex genomic data to improve clinical decision making.

Indexed as

Molecular Sequence AnnotationSoftwareComputational BiologyGenomicsHumansNeoplasmsSequence Analysis, DNABioinformaticsComputational biologyFunctional enrichmentGenomic interpretationNetwork analysisPathway analysisVariant annotation

Identifiers

PMID30649721
PMCPMC7269365

What OpenQuestion holds

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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.