Machine Learning Genomics


PI: Miguel Pérez-Enciso (Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.)

Research Activities:

My current interests include the application of machine learning technologies to genomics in plants, livestock and humans. Most of the genes that are of socioeconomic importance, e.g., genes affecting disease susceptibility or that makes wheat increase yield, are very difficult to find because they are influenced by many genes of small effect. My main area of research is to develop statistical and computational tools that help us to identify these genes and in applying novel machine learning tools like deep learning to new phenotypes such as image or video for precision Agriculture. A topic of particular interest is combining different sources of molecular information, including complete genome sequence, to predict genetic merit. I am also concerned with studying how domestication and artificial selection have shaped the pattern of genetic variation in cultivated species.

Additional info:



Scheme of a Convolutional Neuron Network (CNN), a deep learning algorithm usually employed in image analysis. (a) Simple scheme of a one-dimension (1D) convolutional operation. (b) Full representation of a 1D convolutional neural network for a SNP-matrix. The convolution outputs are represented in yellow. Pooling layers after convolutional operations combining the output of the previous layer at certain locations into a single neuron are represented in green. The final output is a standard network. Source: M Perez-Enciso and LM Zingaretti. A Guide on Deep Learning for Complex Trait Genomic Prediction. Genes 2019, 10, 553;





Centro de Biotecnología y Genómica de Plantas UPM – INIA Parque Científico y Tecnológico de la U.P.M. Campus de Montegancedo
Autopista M-40, Km 38 - 28223 Pozuelo de Alarcón (Madrid) Tel.: +34 91 0679100 ext. 79100  Fax: +34 91 7157721. Localización y Contacto

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