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Analysis of institutional authors

Isidro Y Sánchez JCorresponding Author

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August 10, 2020
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Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices

Publicated to: Frontiers in Plant Science. 11 (947): 947- - 2020-07-14 11(947), DOI: 10.3389/fpls.2020.00947

Authors:

Akdemir, D; Knox, R; Sánchez, JIY
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Affiliations

Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria - Author
SCRDC-CRDSW - Author
Swift Current Res & Dev Ctr, SCRDC CRDSW, Swift Current, SK, Canada - Author
Univ Coll Dublin, Agr & Food Sci Ctr, Anim & Crop Sci Div, Dublin, Ireland - Author
Univ Politecn Madrid UPM, Inst Nacl Invest & Tecnol Agr & Alimentaria INIA, UPM INIA, Ctr Biotecnol & Genom Plantas CBGP, Campus Montegancedo UPM, Madrid, Spain - Author
University College Dublin - Author
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Abstract

© Copyright © 2020 Akdemir, Knox and Isidro y Sánchez. Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and complexity of these datasets bring new challenges but also an opportunity to use the data available as a whole. Detailed phenotype data, combined with increasing amounts of genomic data, have an enormous potential to accelerate the identification of key traits to improve our understanding of quantitative genetics. Data harmonization enables cross-national and international comparative research, facilitating the extraction of new scientific knowledge. In this paper, we address the complex issue of combining high dimensional and unbalanced omics data. More specifically, we propose a covariance-based method for combining partial datasets in the genotype to phenotype spectrum. This method can be used to combine partially overlapping relationship/covariance matrices. Here, we show with applications that our approach might be advantageous to feature imputation based approaches; we demonstrate how this method can be used in genomic prediction using heterogeneous marker data and also how to combine the data from multiple phenotypic experiments to make inferences about previously unobserved trait relationships. Our results demonstrate that it is possible to harmonize datasets to improve available information across gene-banks, data repositories, or other data resources.
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Keywords

AccuracyAssociationCovariance estimationExpectation-maximizationGenomic selectionGenotype imputationHaplotype-phase inferenceInteroperabilityMixed modelsMulti-omicsMultiple kernel learningPedigreePhenomicsPlantPredictionRegression

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Frontiers in Plant Science 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, 2020, it was in position 17/235, thus managing to position itself as a Q1 (Primer Cuartil), in the category Plant Sciences. 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 2026-04-26:

  • WoS: 10
  • Scopus: 10
  • Europe PMC: 3
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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 2026-04-26:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 29.
  • 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: 29 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 14.
  • The number of mentions on the social network Facebook: 1 (Altmetric).
  • The number of mentions on the social network X (formerly Twitter): 16 (Altmetric).

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/86505/

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: 170
  • Downloads: 53
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Canada; Eire; United Kingdom.

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 (ISIDRO SANCHEZ, JULIO).

the author responsible for correspondence tasks has been ISIDRO SANCHEZ, JULIO.

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