Re:Pair Genomics Inc. is seeking to enhance its bioinformatics algorithms by integrating machine learning techniques to improve the accuracy and efficiency of genomic data analysis. The current algorithms, while effective, can benefit from the predictive power and adaptability of machine learning models. The project aims to identify specific areas within the existing bioinformatics pipeline where machine learning can be applied to optimize performance. Students will be tasked with researching and selecting appropriate machine learning models, training these models on existing genomic datasets, and evaluating their performance against current methods. The goal is to achieve a measurable improvement in data processing speed and accuracy, ultimately contributing to more precise genomic interpretations.

- A comprehensive report detailing the research and selection process of machine learning models suitable for bioinformatics applications.

- A prototype implementation of the selected machine learning model integrated into the existing bioinformatics pipeline.

- A performance evaluation comparing the enhanced algorithm with the current system, highlighting improvements in accuracy and processing speed.

- A presentation summarizing the project findings and demonstrating the enhanced algorithm's capabilities.