2024/11/15 by Shafayat Ahmed, Muhit Islam Emon, Nazifa Ahmed Moumi +1 · 1 voice
Computer Science · Biochemistry, Genetics and Molecular Biology · #Algorithms and Data Compression #Gene expression and cancer classification #Machine Learning in Bioinformatics
paper · pdf · doi:10.1101/2024.11.13.623463
openalex publication_date 2024/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14
Abstract Developing effective machine learning models for classifications of biological sequences depends heavily on the quality of the training and test datasets split. Existing tools are either computationally expensive, unable to maintain the desired level of similarity between the training and test datasets, or unable to retain training-test ratio stratification. Here, we present Fast-Part, a fast and accurate sequence data partitioning tool that ensures strict homology separation between the training and test datasets and the best possible training: test stratification ratio, and at the same time, is computationally fast. Fast-Part demonstrates rapid and accurate partitioning performance across diverse protein sequence datasets and maintains strict partitioning compared to the existing tools. Fast-Part can handle massive datasets and maintain strict homology partitioning.