Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.
Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and smal...
| Publicado en: | Journal of Biomedical Informatics Vol. 83; pp. 159 - 167 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Jul2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130691416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130691416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Jul2018 vid: 83 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 130691416 130691416 NLM29890313 10.1016/j.jbi.2018.06.003 NLM29890313 130691416 ppf: 159 ppct: 8 formats: tig: atl: Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection. aug: au: Belciug, Smaranda Gorunescu, Florin affil: Department of Computer Science, University of Craiova, Craiova 200585, Romania sug: subj: Algorithms Proteomics Neural Networks (Computer) Neoplasms Diagnosis Oligonucleotide Array Sequence Analysis Gene Expression Clinical Assessment Tools ab: Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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