Our laboratory is dedicated to machine learning and bioinformatics for research in genomics and phenomics. Our work focuses on developing models of biological systems from large-scale genomic and phenomic data by designing and applying statistical, bioinformatics, and machine learning methods. We pursue two main goals: to model biological processes in agronomic and forestry species through bioinformatic methods, and to contribute to research in genomics and phenomics. Our studies involve genomic data, phenomic data, transcriptomic data, and the integration of these omics in multi-omics analyses. Our current research lines include:

• Identification of genetic regions (SNPs and candidate genes) associated with trait variation of economic importance in agroforestry species such as maize, wheat, and eucalyptus.
• Development and application of models to select superior individuals within a population at very early stages of a breeding cycle based on their genetic or phenotypic information.
• Describing the interaction of differentially expressed genes in roots and leaves of rootstocks (Lagenaria siceraria–watermelon) under drought conditions and their relationship with shoot and root tissue growth and development.

Biography

Bioinformatics Engineer and PhD in Science Carlos Maldonado currently works at the Center for Genomics and Bioinformatics, Universidad Mayor, Huechuraba, Chile. Dr. Maldonado began his research exploring the use of machine learning to classify individuals within a population based on genomic information from SNP and SSR molecular markers, as part of his undergraduate thesis. During his doctoral studies, Dr. Maldonado applied bioinformatics models (frequentist and Bayesian) to identify genomic regions associated with trait variation in tropical maize, successfully pinpointing the main determinants of leaf angle, plant lodging, and flowering time using both SNP- and haplotype-based GWAS. Before completing his doctorate, he collaborated on genomic selection studies using Bayesian alphabet methods and machine learning models in eucalyptus and maize. Currently, Dr. Maldonado is investigating variation in root traits associated with water uptake in Lagenaria siceraria genotypes under drought conditions, and the interaction of differentially expressed genes in roots and leaves of rootstocks (Lagenaria siceraria–watermelon) under drought conditions and their relationship with shoot and root tissue growth and development.

Selected publications

1. Maldonado, C., Mora-Poblete, F., Echeverria, C., Baettig, R., Torres-Díaz, C., Contreras-Soto, R.I., and do Amaral Júnior, A.T., 2022, A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations. Remote Sens-Basel, 14(12), 2898.
2. Contreras-Soto, R.I., Rafael, D.Z., Moiana, L. D., Maldonado, C., and Mora-Poblete, F., 2022, Variation in Root-Related Traits Is Associated With Water Uptake in Lagenaria siceraria Genotypes Under Water-Deficit Conditions. Front. Plant Sci., 13.
3. Maldonado, C., Mora-Poblete, F., Contreras-Soto, R.I., Ahmar, S., Chen, J.T., do Amaral Júnior, A.T., and Scapim, C.A., 2020, Genome-wide prediction of complex traits in two outcrossing plant species through Deep Learning and Bayesian Regularized Neural Network. Front. Plant Sci., 11, 593897.
4. Maldonado, C., Mora, F., Bertagna, F.A.B., Kuki, M.C., and Scapim, C.A., 2019, SNP-and haplotype-based GWAS of flowering-related traits in maize with network-assisted gene prioritization. Agronomy, 9(11), 725.
5. Maldonado, C., Mora, F., Scapim, C.A., and Coan, M, 2019, Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: Hap LA4. PloS one, 14(3), e0212925.

Full list of publications

https://www.researchgate.net/profile/Carlos-Maldonado-19

https://scholar.google.com/citations?user=P9Di38YAAAAJ