Andrew Marete

Seasoned data science researcher specializing in Computational Biology, excelling in machine learning, statistical programming, and advanced coding in R and Python. Actively manages and mentors junior data scientists, driving their professional growth. Passionate about leveraging data science and business analytics to achieve impactful results, dedicated to advancing a career in this dynamic field.

• A Meta-Analysis Including Pre-Selected Sequence Variants Associated with Seven Traits in Three French Dairy Cattle Populations (2018)., doi.org/10.3389/fgene.2018.00522, In this study, we performed an initial linear regression on three populations, then compared the result using a meta-analysis to get the genomic regions private within a population or common between the three populations. This study highlighted the importance of combining data from many data sources.
• A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds (2018)., doi.org/10.1371/journal.pone.0199931, In this study, we looked for gene ontologies affecting udder health in five bovine populations. Using weighted gene co-expression networks based on partial correlation and information theory approaches, we identified ten candidate genes that can be used to improve mammary gland health.
• Identification of long non-coding RNA associated with bovine Johne's disease using a combination of neural networks and logistic regression (2021)., Frontiers in Veterinary Science 8 (2021): 209, This study identified Johne's disease genomic signatures that environmental changes might activate and, in the future, may be targeted for removal to reduce the prevalence of the disease. For the data modelling, I used a combination of linear regression and recurrent neural networks to train the model.