Integrating Convolutional Neural Networks for Genomic Sequence Analysis: Deep Learning Applications in Genomics and Bioinformatics

This paper explores the genomic sequence analysis, more specifically the gene prediction and identification of regulatory element using convolutional neural network (CNN). The model was trained and evaluated on approximately 2000 DNA sequences that are labelled with information about the gene regions. The CNN architecture was designed and implemented using Python, TensorFlow, and Keras in an effort to preserve the raw sequence data and subsequently learn the hierarchical features from that data. Accuracy: 85%: Precision: 0. 88: Recall: 0. 82: F1-score: 0. 85 – These all show good results when the model is tried to differentiate between gene and non-gene areas of a chromosome. Furthermore, the interpretability analysis indicated that some sequences of the DNA, including the TATA Box and Enhancer motifs, played significant roles in the model predictions their importance scores between 0 and 1 was observed. 78 to 0. 92. Attention scores identified landmarks, within the genomic sequences, which were important to distinguish target gene regions: these attending scores were within a range of 0. 75 to 0. 95. It shows that CNNs are useful for genomic sequence analysis and introduces a metric for interpretability that can help to bridge the gap between black-box models and domain experts.