Improving Genetic Variant Calling Using Deep Convolutional Neural Networks and Ensemble Learning

Genomic research has been altered by high-throughput sequencing technologies, making genetic variant identification critical for understanding inherited disorders and developing personalised treatments. However, conventional variant calling methods frequently exhibit low sensitivity and high false-positive rates, particularly in areas with sparse sequencing coverage or complex genomic architecture. To improve the accuracy and resilience of variant identification, A novel approach is proposed that combines ensemble learning and Deep Convolutional Neural Networks (DCNNs). First, the raw sequencing data is pre-processed to obtain variant features and high-quality read alignments. Next, a DCNN architecture is built with multiple layers and filters to capture complex genetic patterns. To ensure that the model is robust and generalizable, it is trained on a large and diverse dataset. Ensemble learning uses methods such as boosting and bagging to remove biases and increase predictive accuracy by merging predictions from many DCNN models. The test findings show significant improvements over the most advanced techniques, including a 22 % decrease in false-positive rates, an 18% increase in sensitivity, and a 15% gain in precision, particularly in areas with limited coverage. This approach is an effective strategy to precisely identify variations and make educated therapy decisions.