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Analysis of institutional authors

Bielza, ConchaAuthorLarranaga, PedroAuthor

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June 9, 2019
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Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Publicated to: INFORMATION SCIENCES. 222 229-246 - 2013-02-10 222(), DOI: 10.1016/j.ins.2010.12.013

Authors:

García-Torres, M; Armañanzas, R; Bielza, C; Larrañaga, P
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Affiliations

Univ Pablo de Olavide, Area Lenguajes & Sistemas Informat, Seville 41013, Spain - Author
Univ Politecn Madrid, Computat Intelligence Grp, E-28660 Madrid, Spain - Author

Abstract

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers. (C) 2011 Elsevier Inc. All rights reserved.
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Keywords

CancerClassificationEnhanced laser-desorptionFeature subset selectionGenetic algorithmMass spectrometryMetaheuristicsPatternsSeldi-tofSerumSpectraStabilityStatistical comparisons

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal INFORMATION SCIENCES due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2013, it was in position 8/135, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-28:

  • Google Scholar: 17
  • WoS: 11
  • Scopus: 13
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-28:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 21 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/14036/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 404
  • Downloads: 451
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (LARRAÑAGA MUGICA, PEDRO MARIA).

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Awards linked to the item

This work has been partially supported by the projects TIN-68084-C02-00, TIN2010-20900-C04-04 and TIN2007-62626, Cajal Blue Brain and Consolider CSD2007-00018. Ruben Armananzas is supported by a Juan de la Cierva grant (Spanish Ministry of Science and Innovation). Part of the computer time was provided by the Centro Informatico Cientifico de Andalucia (CIC).
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