Publicaciones (65) Publicaciones de Carlos Fernández Lozano Ver datos de investigación referenciados.

2023

  1. Music Recommendation System Based on Ratings Obtained from Amazon

    VI Congreso XoveTIC: impulsando el talento científico

2021

  1. A review on machine learning approaches and trends in drug discovery

    Computational and Structural Biotechnology Journal, Vol. 19, pp. 4538-4558

  2. Bioinformatic tools for research in CRC

    Foundations of Colorectal Cancer (Elsevier), pp. 231-247

  3. Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data

    Studies in health technology and informatics, Vol. 281, pp. 382-386

  4. Machine Learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes

    Expert Systems with Applications, Vol. 185

  5. Machine learning analysis of TCGA cancer data

    PeerJ Computer Science, Vol. 7, pp. 1-47

  6. Random forest-based prediction of stroke outcome

    Scientific Reports, Vol. 11, Núm. 1

2020

  1. Comparison of outlier-tolerant models for measuring visual complexity

    Entropy, Vol. 22, Núm. 4

  2. Covid-19 projections for Spain using forecast combinations

    BEIO, Boletín de Estadística e Investigación Operativa, Vol. 36, Núm. 2, pp. 99-125

  3. Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

    PeerJ Computer Science, Vol. 6, pp. 1-21

  4. MCDcalc: Markov chain molecular descriptors calculator for medicinal chemistry

    Current Topics in Medicinal Chemistry, Vol. 20, Núm. 4, pp. 305-317

  5. Molecular docking and machine learning analysis of Abemaciclib in colon cancer

    BMC Molecular and Cell Biology, Vol. 21, Núm. 1

  6. Population subset selection for the use of a validation dataset for overfitting control in genetic programming

    Journal of Experimental and Theoretical Artificial Intelligence, Vol. 32, Núm. 2, pp. 243-271

  7. Transfer learning features for predicting aesthetics through a novel hybrid machine learning method

    Neural Computing and Applications, Vol. 32, Núm. 10, pp. 5889-5900