Evidence map›Paper›PMID 39447572›Full record

ArticleCell reports methods2024

iSubGen generates integrative disease subtypes by pairwise similarity assessment.

Natalie S Fox, Mao Tian, Alexander L Markowitz, Syed Haider, Constance H Li, Paul C Boutros

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2024. 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. Review
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

6 authors.

Natalie S FoxDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.
Mao TianDepartment of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: maotian@mednet.ucla.edu.
Alexander L MarkowitzDepartment of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA.
Syed HaiderThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
Constance H LiDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.
Paul C BoutrosDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Urology, University of California, Los Angeles, Los Angeles, CA, USA; Broad Stem Cell Research Center, University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: pboutros@mednet.ucla.edu.

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
Advancing method benchmarking and data sharing through crowd-sourced competitions in cancer researchU24CA248265 · NCI · SAGE BIONETWORKS · PI BOUTROS, PAUL CHRISTOPHER, VARMA, SUSHEEL · 2020 to 2024
$4.1M
Virginia-UCLA-Toronto Biomarker Characterization CenterU2CCA271894 · NCI · OLD DOMINION UNIVERSITY · PI OLIVER John SEMMES · 2022 to 2026
$3.2M
Development of Protein Biomarkers in Post-DRE Urine for use in Liquid Biopsy of Prostate CancerU01CA214194 · NCI · EASTERN VIRGINIA MEDICAL SCHOOL · PI BOUTROS, PAUL CHRISTOPHER, KISLINGER, THOMAS · 2016 to 2021
$2.5M
NCI NIH HHS P30 CA016042NCI NIH HHS U01 CA214194NCI NIH HHS U24 CA248265NCI NIH HHS U2C CA271894
6 · The paper itself

Abstract

There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https://cran.r-project.org/web/packages/iSubGen.

Indexed as

AlgorithmsNeoplasmsComputational BiologyGene Expression ProfilingHumansProteomicsSoftwareTranscriptomealgorithmautoencoderbiomarkerscancer biologyCP: Cancer biologyCP: Systems biologydata correlationdata integrationmulti-omicspattern discoverysubtype discoverysystem biology

Identifiers

PMID39447572
PMCPMC11705582

What OpenQuestion holds

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LicenceCC BY-NC
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.