World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
5672
World Ranking
11240
National Ranking
187

Alberto Ferrer publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Alberto Ferrer sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 111 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Alberto Ferrer D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Alberto Ferrer sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Alberto Ferrer is affiliated with the Universitat Politècnica de València in Spain. Their research spans multiple fields, including molecular biology, analytical chemistry, control and systems engineering, statistics, probability and uncertainty, and radiology, nuclear medicine and imaging.

The scientist's research topics cover several specialized areas:

  • Spectroscopy and Chemometric Analyses
  • Fault Detection and Control Systems
  • Advanced Statistical Process Monitoring
  • Mineral Processing and Grinding
  • Bioinformatics and Genomic Networks
  • Gene expression and cancer classification
  • Long-Term Effects of COVID-19

Their publication record includes papers published in prominent venues with a focus on chemometrics, bioinformatics, and process optimization. Frequent publication venues include:

  • Chemometrics and Intelligent Laboratory Systems
  • Journal of Chemometrics
  • Zenodo (CERN European Organization for Nuclear Research)
  • Quality Engineering
  • SSRN Electronic Journal

Notable recent papers authored or co-authored by Ferrer include:

  • Multivariate Six Sigma: A Case Study in Industry 4.0 (2020, Processes)
  • Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment (2021, Communications Biology)
  • MultiBaC: an R package to remove batch effects in multi-omic experiments (2022, Bioinformatics)
  • A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6 (2020, Scientific Data)
  • Improved formulation of the latent variable model inversion-based optimization problem for quality by design applications (2020, Journal of Chemometrics)

Ferrer frequently collaborates with several co-authors, including:

  • Joan Borràs-Ferrís
  • J.M. Prats-Montalbán
  • Alba González-Cebrián
  • Raffaele Vitale
  • Carl Duchesne

The scientist also has contributed to academic book publications, such as:

  • Estadística aplicada al ámbito sanitario (2022), published by Editorial Universitat Politècnica de València eBooks

Best Publications

  • Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

    Sonia Tarazona;Pedro Furió-Tarí;David Turrà;Antonio Di Pietro

  • maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

    Ana Conesa;María José Nueda;Alberto Ferrer;Manuel Talón

  • Multivariate image analysis: a review with applications

    J.M. Prats-Montalbán;A. de Juan;A. Ferrer

  • Dealing with missing data in MSPC: several methods, different interpretations, some examples

    Francisco Arteaga;Alberto Ferrer

  • NOIseq: a RNA-seq differential expression method robust for sequencing depth biases

    Sonia Tarazona;Fernando García;Alberto Ferrer;Joaquín Dopazo

  • Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

    Alberto Ferrer

  • Near infrared hyperspectral imaging for forensic analysis of document forgery

    Carolina S. Silva;Maria Fernanda Pimentel;Ricardo S. Honorato;Celio Pasquini

  • PCA model building with missing data: New proposals and a comparative study

    A. Folch-Fortuny;F. Arteaga;A. Ferrer

  • Data understanding with PCA: Structural and Variance Information plots

    José Camacho;Jesús Picó;Alberto Ferrer

  • Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA

    María José Nueda;Ana Conesa;Johan A. Westerhuis;Huub C. J. Hoefsloot

  • Pixel classification methods for identifying and quantifying leaf surface injury from digital images

    Ole Mathis Opstad Kruse;José Manuel Prats-Montalbán;Ulf Geir Indahl;Knut Kvaal

  • Missing Data Imputation Toolbox for MATLAB

    Abel Folch-Fortuny;Francisco Arteaga;Alberto Ferrer

  • The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter?

    J. Camacho;J. Picó;A. Ferrer

  • Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping

    J.M. González-Martínez;A. Ferrer;J.A. Westerhuis

  • VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

    A. Folch-Fortuny;J.M. Prats-Montalbán;S. Cubero;J. Blasco

  • Multivariate SPC of a sequencing batch reactor for wastewater treatment

    D. Aguado;A. Ferrer;J. Ferrer;A. Seco

  • Multi‐phase analysis framework for handling batch process data

    José Camacho;Jesús Picó;Alberto Ferrer

  • Integration of statistical and engineering process control in a continuous polymerization process

    Carmen Capilla;Alberto Ferrer;Rafael Romero;Angel Hualda

  • ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments

    Maria j. Nueda;Alberto Ferrer;Ana Conesa

  • Framework for regression-based missing data imputation methods in on-line MSPC

    Francisco Arteaga;Alberto Ferrer

  • Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool

    Guillermo Quintás;Nuria Portillo;Juan Carlos García-Cañaveras;José Vicente Castell

  • Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects

    José Camacho;Alberto Ferrer

Frequent Co-Authors

Ana Conesa
Ana Conesa University of Florida
Joaquín Dopazo
Joaquín Dopazo Institute of Biomedicine of Seville
Johan A. Westerhuis
Johan A. Westerhuis University of Amsterdam
Santiago F. Elena
Santiago F. Elena Santa Fe Institute
Aurora Seco
Aurora Seco University of Valencia
Age K. Smilde
Age K. Smilde University of Amsterdam
José Ferrer
José Ferrer Universitat Politècnica de València
José Blasco
José Blasco University of Valencia
Huub C. J. Hoefsloot
Huub C. J. Hoefsloot University of Amsterdam
Manuel Talón
Manuel Talón Instituto Valenciano de Investigaciones Agrarias

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