Associating functional groups to multiple clinical types using combined t-test scores and contingency-based measures: a study on breast cancer genes

Stemming from the need to score relations between functional groups of genes and multiple clinical types associated with a tumour, this study proposes to use contingency-based measures to quantify such relations. It aims at reflecting a relative measure of association within a specific set of functi...

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Published inInternational journal of computational biology and drug design Vol. 5; no. 3-4; p. 261
Main Authors Yousri, Noha A, Elkaffash, Dalal M
Format Journal Article
LanguageEnglish
Published England 2012
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Abstract Stemming from the need to score relations between functional groups of genes and multiple clinical types associated with a tumour, this study proposes to use contingency-based measures to quantify such relations. It aims at reflecting a relative measure of association within a specific set of functional groups, and a specific set of clinical statuses. The proposed methodology is based on extracting features (scores) from expression sets that relate genes to multiple cancer subtypes (clinical statuses), and use those features (scores) to associate cancer subtypes with functional groups. It proposes combining t-test scores at several levels of cancer statuses' differentiation to calculate such gene features. It also proposes using contingency based measures as Jaccard and F-measure to associate gene functional groups to multiple cancer subtypes/statuses. Variations from the original Jaccard measure are proposed to reflect scores of genes' relations to classes/groups rather than using binary relations. The core objective of the experimental study is to identify the functional categories of genes that mark the change in lymph node status under each of oestrogen receptor positive and negative statuses in breast cancer expression sets.
AbstractList Stemming from the need to score relations between functional groups of genes and multiple clinical types associated with a tumour, this study proposes to use contingency-based measures to quantify such relations. It aims at reflecting a relative measure of association within a specific set of functional groups, and a specific set of clinical statuses. The proposed methodology is based on extracting features (scores) from expression sets that relate genes to multiple cancer subtypes (clinical statuses), and use those features (scores) to associate cancer subtypes with functional groups. It proposes combining t-test scores at several levels of cancer statuses' differentiation to calculate such gene features. It also proposes using contingency based measures as Jaccard and F-measure to associate gene functional groups to multiple cancer subtypes/statuses. Variations from the original Jaccard measure are proposed to reflect scores of genes' relations to classes/groups rather than using binary relations. The core objective of the experimental study is to identify the functional categories of genes that mark the change in lymph node status under each of oestrogen receptor positive and negative statuses in breast cancer expression sets.
Author Yousri, Noha A
Elkaffash, Dalal M
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  email: noha.yousri@alexu.edu.eg
  organization: Computer and System Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt. noha.yousri@alexu.edu.eg
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  givenname: Dalal M
  surname: Elkaffash
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/23013653$$D View this record in MEDLINE/PubMed
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Snippet Stemming from the need to score relations between functional groups of genes and multiple clinical types associated with a tumour, this study proposes to use...
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StartPage 261
SubjectTerms Breast Neoplasms - genetics
Breast Neoplasms - pathology
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Lymph Nodes - pathology
Lymphatic Metastasis - genetics
Receptors, Estrogen - genetics
Title Associating functional groups to multiple clinical types using combined t-test scores and contingency-based measures: a study on breast cancer genes
URI https://www.ncbi.nlm.nih.gov/pubmed/23013653
Volume 5
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