Preliminary Results
Report 1. As results of first meeting, we outlined different aspects of knowledge base prediction models. In that meeting we discussed and clarified the main sources of data associated with D. melanogaster populations and individuals, concerning sources of single nucleotide polymorphisms we defined sources and methods to determine new ones.
https://drive.google.com/open?id=1anhvcWCxkQoClJ2MkbsT-XltRNPoeK9vcZaNKl3MN9s
Report 2. Going inside of machine learning and expert knowledge, we discuss ommchrome pathway in Drosophila establishing relevant genes. In each gene we studied subcellular location and cellular function annotations as well as the key zones in the DNA sequence where functionalities and protein structures are codified.
https://drive.google.com/open?id=1T5woKQ-UHtRh3AcRKjNwuWzUhlz7C3-_6N-86dIe3yw
Report 3. In order to design the testing dataset for the SNPs-PHE prediction model, a deep study of single variants on available populations is made.
https://drive.google.com/open?id=1unh2QuOoqbqZe6Z5V4yF2SGr9c60KteGnkxp82w-MOQ
Report 4. Going inside formalization, here are some proposals for studying the relationship between SNPs, genes and eye color. The problem is cut in pieces: relation between SNPs and genes, and between genes and eye color.
https://drive.google.com/open?id=0B6xlqTURd1v5VlJmdTMzeFJ6aW8
Publications
- Flavio Spetale, Elizabeth Tapia, Javier Murillo, Flavia Krsticevic, Sergio Ponce, and Pilar Bulacio. Proper integration of feature subsets boost GO subcellular localization predictions. XXI Congreso Argentino de Bioingeniería y X Jornadas de Ingeniería Clínica, SABI 2017. Octubre 2017. In press.
- Flavia Krsticevic, Flavio Spetale, Elizabeth Tapia, Javier Murillo, and Pilar Bulacio. Predicting Cellular Component of Ommochrome Pathway eye genes in D. melanogaster based on Machine Learning. X Simpósio de Ecologia, Genética e Evolução de Drosophila. Novembro 2017.