FKMU: K-Means Under-Sampling for Data Imbalance in Predicting TF-Target Genes Interactions

Identifying interactions between transcription factors (TFs) and target genes is critical for understanding molecular mechanisms in biology and disease. Traditional experimental approaches are often costly and not scalable. We introduce FKMU, a K-means-based under-sampling method designed to address data imbalance in predicting TF-target interactions. By selecting low-frequency TF samples within each cluster and optimizing the balance ratio to 1:1 between known and unknown samples, FKMU significantly improves prediction accuracy for unobserved interactions. Integrated with a deep learning model that uses random walk sampling and skip-gram embeddings, FKMU achieves an average AUC of 0.9388 ± 0.0045 through five-fold cross-validation, outperforming state-of-the-art methods. This approach facilitates accurate and large-scale predictions of TF-target interactions, providing a robust tool for molecular biology research.

@article{Le2024,
  title     = {FKMU: K-Means Under-Sampling for Data Imbalance in Predicting TF-Target Genes Interactions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Thanh Tuoi Le and Xuan Tho Dang},
  doi       = {10.14569/IJACSA.2024.0151222},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151222}
}