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Grant PID2021-126051OB-C43 funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe".

Analysis of institutional authors

Badias, AlbertoCorresponding AuthorSaucedo, LuisAuthorSanz, Miguel AngelAuthorBenítez, José MaríaAuthorMontans, FranciscoAuthor

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August 25, 2024
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Learning cell migration mechanisms using machine learning

Publicated to:Results In Engineering. 22 102295- - 2024-06-01 22(), DOI: 10.1016/j.rineng.2024.102295

Authors: Olalla, Juan; Badias, Alberto; Saucedo, Luis; Sanz, Miguel Angel; Benitez, Jose Maria; Montans, Francisco

Affiliations

Univ Florida, Herbert Wertheim Coll Engn, Dept Mech & Aerosp Engn, Gainesville, FL 32611 USA - Author
Univ Politecn Madrid, ETS Ingn Aeronaut & Espacio, Pza Cardenal Cisneros 3, Madrid 28040, Spain - Author

Abstract

Cell movement is known to be fundamental to many processes that take part in the overall functioning of life itself. Regeneration of damaged tissue, tumor growth or the creation of life itself through embryogenesis rely mainly on the individual and collective movement of cells. Due to the importance of such phenomena, increasing research activity has been dedicated to them, but many are the questions that still remain, specially when focusing on the movement of clusters and networks of cells as a whole. We present a new approach to take advantage of Machine Learning tools (Artificial Neural Networks) in order to predict the position and velocity of a cell in a certain moment in time, given the properties of said cell and the environment surrounding it. The results show a high level of accuracy for the analyzed video sequence, which implies that the studied properties greatly influence the decisions taken by cells regarding its movement.

Keywords

Artificial intelligenceArtificial neural networkArtificial neural networksCell migrationCell movementCellsCytologyLearning cellsLearning toolMachine learningMachine-learningMigration mechanismsNeural networksNew approachesPropertyResearch activitiesTissue regenerationTumor growth

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Results In Engineering 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, 2024 there are still no calculated indicators, but in 2023, it was in position 6/175, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Multidisciplinary. Notably, the journal is positioned above the 90th percentile.

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 2025-10-09:

  • Scopus: 1

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 2025-10-09:

  • 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: 5 (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.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.

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 (Olalla, Juan) and Last Author (MONTANS LEAL, FRANCISCO JAVIER).

the author responsible for correspondence tasks has been BADIAS HERBERA, ALBERTO.