Pathway-Based Feature Selection Algorithm for Cancer Microarray Data

Bandyopadhyay, Nirmalya; Kahveci, Tamer; Goodison, Steve; Sun, Yijun; Ranka, Sanjay

doi:10.1155/2009/532989

Cited by 18 publications

(9 citation statements)

References 55 publications

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“…A linear classifier can be inferred by penalising the regression coefficients based on network information. The biological Pathway-Based Feature Selection (BPFS) algorithm [99] also utilizes pathway information for microarray classification. It uses SVMs to calculate the marginal classification power of the genes and puts those genes in a separate set.…”

Section: Prior Knowledgementioning

confidence: 99%

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Hira

Gillies

2015

Advances in Bioinformatics

770

472

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We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.

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Section: Prior Knowledgementioning

confidence: 99%

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Hira

Gillies

2015

Advances in Bioinformatics

770

472

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“…A large number of computational approaches aim at classifying distinct diseases from genomic data, relying on different theoretical frameworks and distinct kinds of source data. For instance, several algorithms make use of SVMs, e.g., (56)(57)(58), some others rely on the information on pathways, e.g., (27)(28)(29)(59)(60)(61) or on distinct approaches, e.g., (62).…”

Section: Comparison With Other Techniquesmentioning

confidence: 99%

Pathway-based classification of breast cancer subtypes

et al. 2017

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Cancer heterogeneity represents a major hurdle in the development of effective theranostic strategies, as it prevents to devise unique and maximally efficient diagnostic, prognostic and therapeutic procedures even for patients affected by the same tumor type. Computational techniques can nowadays leverage the huge and ever increasing amount of (epi)genomic data to tackle this problem, therefore providing new and valuable instruments for decision support to biologists and pathologists, in the broad sphere of precision medicine. In this context, we here introduce a novel cancer subtype classifier from gene expression data and we apply it to two different Breast Cancer datasets, from TCGA and GEO repositories. The classifier is based on Support Vector Machines and relies on the information about the relevant pathways involved in breast cancer development to reduce the huge variable space. Among the main results, we show that the classifier accuracy is preserved at excellent values even when the variable space is reduced by a 20-fold, hence providing a precious tool for cancer patient profiling even in case of limited experimental resources.

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“…The application of this approach to breast [27,55,56] and prostate [38,57] cancer has enabled the derivation of improved molecular signatures and has also demonstrated the feasibility of utilizing both clinical and genetic information simultaneously for more accurate cancer prognosis. We continue to refine and improve the efficiency of our feature selection algorithms and to combine these with established algorithms to design optimal computational strategies for the derivation of molecular signatures from gene expression data [47,58,59]. Finally, it is likely that the current microarray formats will soon be superseded by new technologies [60], such as next-generation sequencing.…”

Section: Prostate Cancer Prognostic Signaturesmentioning

confidence: 99%

Derivation of Cancer Diagnostic and Prognostic Signatures from Gene Expression Data

Goodison¹,

Sun

Urquidi

2010

Bioanalysis

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The ability to compare genome-wide expression profiles in human tissue samples has the potential to add an invaluable molecular pathology aspect to the detection and evaluation of multiple diseases. Applications include initial diagnosis, evaluation of disease subtype, monitoring of response to therapy and the prediction of disease recurrence. The derivation of molecular signatures that can predict tumor recurrence in breast cancer has been a particularly intense area of investigation and a number of studies have shown that molecular signatures can outperform currently used clinicopathologic factors in predicting relapse in this disease. However, many of these predictive models have been derived using relatively simple computational algorithms and whether these models are at a stage of development worthy of large-cohort clinical trial validation is currently a subject of debate. In this review, we focus on the derivation of optimal molecular signatures from high-dimensional data and discuss some of the expected future developments in the field.

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Pathway-Based Feature Selection Algorithm for Cancer Microarray Data

Cited by 18 publications

References 55 publications

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Pathway-based classification of breast cancer subtypes

Derivation of Cancer Diagnostic and Prognostic Signatures from Gene Expression Data

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