As mentioned in Short-Bio, my main interests are mostly related to programming, teaching and learning. If you are interested in getting a brief idea of Biology, Bioinformatics and Computational Biology and you don't belong to such fields (like me), here there is a very interesting file to start with: Computational Biology & Bioinformatics: A Gentle Overview.
My thesis document is titled "In-silico Protein-Protein Interaction Prediction using Network Analysis, Amino Acid Composition, and Functional Annotation". A sketch of what I plan to do is presented here.
The full details are updated on my ORCID profile, but the latest peer-reviewed publications are:
Sosa, C.C.; Clavijo-Buriticá, D.C.; García-Merchán, V.H.; López-Rozo, N.; Riccio-Rengifo, C.; Diaz, M.V.; Londoño, D.A.; Quimbaya, M.A. GOCompare: An R package to compare functional enrichment analysis between two species. Genomics 2023, Vol. 115, No. 1. https://doi.org/10.1016/j.ygeno.2022.110528
López-Rozo, N.; Ramirez-Castrillon, M.; Romero, M.; Finke, J.; Rocha, C. Gene Expression Datasets for Two Versions of the Saccharum spontaneum AP85-441 Genome. Data 2023, 8, 1. https://doi.org/10.3390/data8010001
Protein-Protein Interactions: Interactions between a pair of proteins rely upon chemical characteristics (pH, polarity) as well as on the 3D structure. These properties assess their possibility to dock adequately. I presented recently some of these results in SICO20 (~0:00:25)
Gene Co-expression Networks: When a gene transcription is active inside a cell, we say that it is expressing. Co-expression of genes refers traditionally to the phenomenon of two or more genes to have a related expression pattern, i.e. when gene A expresses, gene B also expresses proportionally. If applied to all (known) pairs of genes, this correlation can be seen as a network (graph), where the nodes are the genes and there is a link between two genes if they have a strong correlation. Some results were presented at the Taller Anual 2019 Del gen al cultivo.
Correlation Metrics (applied to biological data): Normally, the Pearson Correlation Coefficient (PCC) is widely used for many series of biological data in order to assess correlation among many different variables. However, recent research suggests that some relations among variables in biology might not behave as linear. Therefore, exploring alternatives to PCC is worth the try. Some results were presented at the Seminario Permanente Facultad de Ingeniería y Ciencias (2019/03).
Former research interests are:
Computational Fluid Dynamics (CFD)
Water Distribution Systems Modeling
Pedagogical Strategies for Higher Education