Deep Learning Approaches in Genomic Analysis: A Review of DNA Sequence Classification Techniques
Authors:- Vishakha Nerkar, Dr. Vinod Kimbahune
Abstract-In bioinformatics, DNA sequence classification poses many challenges due to its inherent complexity and volatility. In this paper, the difficulties in applying deep learning techniques to DNA sequence classification are examined. Variable sequence lengths, complex data representation, and the requirement for efficient feature extraction are all highlighted by the analysis. Moreover, when developing a model, factors like uneven data distributions, interpretability issues, and the possibility of overfitting must be carefully considered. Deep learning in genomic analysis has tremendous potential, but there are still many unanswered questions. Using transfer learning and genomics domain expertise can help overcome some of these obstacles. Despite these challenges, applying deep learning methods could greatly improve our comprehension of genetic data and how it relates to health and illness. Researchers can move the field toward transformative work by taking on these obstacles. Discoveries in genomic medicine and beyond.
