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Analysis of institutional authors

Bastico MCorresponding AuthorFernandez-Garcia AAuthorBelmonte-Hernández AAuthorMayoral SuAuthor

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May 1, 2023
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DrOGA: an artificial intelligence solution for driver-status prediction of genomics mutations in precision cancer medicine

Publicated to: IEEE Access. 11 37378-37391 - 2023-01-01 11(), DOI: 10.1109/ACCESS.2023.3266983

Authors:

Bastico, M; Fernández-García, A; Belmonte-Hernández, A; Mayoral, SU
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Affiliations

Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Sistemas Informat ETSISI, Madrid 28031, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Telecomunicac, Madrid 28040, Spain - Author
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Abstract

Precision cancer medicine suggests that better cancer treatments would be possible guiding therapies by tumor’s genomics alterations. This hypothesis boosted exome sequencing studies, collection of cancer variants databases and developing of statistical and Machine Learning-driven methods for alterations’ analysis. In order to extract relevant information from huge exome sequencing data, accurate methods to distinguish driver and neutral or passengers mutations are vital. Nevertheless, traditional variant classification methods have often low precision in favour of higher recall. Here, we propose several traditional Machine Learning and new Deep Learning techniques to finely classify driver somatic non-synonymous mutations based on a 70-features annotation, derived from medical and statistical tools. We collected and annotated a complete database containing driver and neutral alterations from various public data sources. Our framework, called Driver-Oriented Genomics Analysis (DrOGA), presents the best performances compared to individual and other ensemble methods on our data. Explainable Artificial Intelligence is used to provide visual and clinical explanation of the results, with a particular focus on the most relevant annotations. This analysis and the proposed tool, along with the collected database and the feature engineering pipeline suggested, can help the study of genomics alterations in human cancers allowing precision oncology targeted therapies based on personal data from next-generation sequencing.
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Keywords

annotationsartificial intelligenceassociationbioinformaticsbiomarkerscancerdatabasedatabasesdeep learningdriver-status predictionexplainable aiframeworkguidelinesimpactmachine learningmutationoncologypathogenicitypoint mutationsprecision cancer medicineprecision engineeringpredictive modelssequence variantssequential analysisAnnotationsArtificial intelligenceBioinformaticsBiomarkersCancerDatabasesDeep learningDriver-status predictionExplainable aiGenomicsJoint-consensus-recommendationMachine learningMutationOncologyPrecision cancer medicinePrecision engineeringPredictive modelsSequential analysis

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.2. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.38 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-01, the following number of citations:

  • WoS: 6
  • Scopus: 8
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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 2026-04-01:

  • 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: 32 (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:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/79699/

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: 211
  • Downloads: 89
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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: First Author (BASTICO, MATTEO) and Last Author (URIBE MAYORAL, SILVIA ALBA).

the author responsible for correspondence tasks has been BASTICO, MATTEO.

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Project objectives

El presente trabajo tiene como objetivos principales: analizar las alteraciones genómicas tumorales mediante técnicas de Machine Learning y Deep Learning; desarrollar métodos precisos para clasificar mutaciones somáticas no sinónimas en conductoras y neutrales basados en una anotación de 70 características; construir y anotar una base de datos completa de mutaciones conductoras y neutrales a partir de fuentes públicas; evaluar el rendimiento del marco Driver-Oriented Genomics Analysis (DrOGA) frente a métodos individuales y ensamblados; y aplicar inteligencia artificial explicable para ofrecer interpretaciones visuales y clínicas de los resultados, enfatizando las anotaciones más relevantes, con el fin de facilitar terapias oncológicas de precisión basadas en datos personales de secuenciación de nueva generación.
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Most relevant results

Los resultados más relevantes del estudio incluyen: la propuesta de técnicas tradicionales de Machine Learning y nuevas técnicas de Deep Learning para clasificar con precisión mutaciones somáticas no sinónimas conductoras, utilizando una anotación de 70 características derivadas de herramientas médicas y estadísticas; la recopilación y anotación de una base de datos completa con mutaciones conductoras y neutrales procedentes de diversas fuentes públicas; la demostración de que el marco Driver-Oriented Genomics Analysis (DrOGA) supera en rendimiento a métodos individuales y otros métodos en conjunto en los datos analizados; y la aplicación de Inteligencia Artificial Explicable para ofrecer explicaciones visuales y clínicas centradas en las anotaciones más relevantes.
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