Cited by 3Gene expression and cancer classificationBioinformatics and Genomic NetworksBreast Cancer Treatment Studies

Tumor stratification by a novel graph-regularized bi-clique finding algorithm

Ahmadi Adl, Amin, Qian, Xiaoning·2015·Computational Biology and Chemistry

Abstract

Due to involved disease mechanisms, many complex diseases such as cancer, demonstrate significant heterogeneity with varying behaviors, including different survival time, treatment responses, and recurrence rates. The aim of tumor stratification is to identify disease subtypes, which is an important first step towards precision medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to implement computational methods, including traditional clustering and bi-clustering algorithms, to systematically analyze high-throughput molecular measurements to identify tumor subtypes as well as their corresponding associated biomarkers. In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene-gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.

Integrity signals

Registry

Crossref · 10.1016/j.compbiolchem.2015.02.010

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers.

    “Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers [18–20].”

Cites

Flagged first, then by citations

2 of the 46 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Ahmadi Adl, A., & Qian, X.
Affiliations
University of South Florida (US)
Venue
Computational Biology and Chemistry
Publisher
Elsevier BV
Published
2015-08
Volume
57
Pages
3-11
ISSN
1476-9271 (ISSN-L)
OpenAlex
W1997975056
Cited by
3
References
46
Citations by year
2017: 1 · 2016: 2
FWCI
0.43
Type
journal article
Language
en
Open access
closed
Locations
  • Computational Biology and Chemistry (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
tumor stratification, tumor subtypes, graph regularization, bipartite graphs, gene interaction networks, biomarker discovery, tumor heterogeneity, biclustering, genomic data
MeSH terms
Algorithms, Humans, Neoplasms, Computational Biology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review