Evidence map›Paper›PMID 30423088›Full record

ArticleBioinformatics (Oxford, England)2018

Hierarchical HotNet: identifying hierarchies of altered subnetworks.

Matthew A Reyna, Mark D M Leiserson, Benjamin J Raphael

Open access · bronzeAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed
7.7field-weighted citation impact, top 2% 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

79 citing papers in PubMed, 154 citations in OpenAlex.

  1. Disentangling Heterogeneous Molecular Networks for Multi-Omics-Driven Cancer Driver Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. A Network-Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. Article

19 more citing papers are in PubMed but not listed here.

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 1 country.

Matthew A ReynaDepartment of Computer Science, Princeton University, Princeton, NJ, USA.
Mark D M LeisersonDepartment of Computer Science, University of Maryland, College Park, MD, USA.
Benjamin J RaphaelDepartment of Computer Science, Princeton University, Princeton, NJ, USA.
Princeton University · USUniversity of Maryland, College Park · US

Funding

Pathway and Network Integration of Cancer Genomics and Clinical DataU24CA211000 · NCI · PRINCETON UNIVERSITY · PI RAPHAEL, BENJAMIN, YEH, JEN JEN · 2016 to 2020
$1.7M
Computational Characterization of Genetic HeterogeneityR01HG007069 · NHGRI · PRINCETON UNIVERSITY · PI RAPHAEL, BENJAMIN · 2013 to 2015
$1.1M
NCI NIH HHS U24 CA211000NHGRI NIH HHS R01 HG007069
6 · The paper itself

Abstract

Motivation: The analysis of high-dimensional 'omics data is often informed by the use of biological interaction networks. For example, protein-protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an 'omics dataset and are topologically close (e.g. connected) on an interaction network. Results: We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different 'omics datasets. Availability and implementation: http://github.com/raphael-group/hierarchical-hotnet. Supplementary information: Supplementary material are available at Bioinformatics online.

Indexed as

AlgorithmsGene Regulatory NetworksHumansNeoplasmsOncogenesProtein Interaction Maps

Identifiers

PMID30423088
PMCPMC6129270
OpenAlexW2890112540

What OpenQuestion holds

Textmetadata
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.