Detection and Analysis of Gene Functioning and Manipulation using Deep Learning
Jameel Ahmed Bhutto, Venkateswarlu Sunkari, Amit Madhukar Patil, B. Vanathi, S Loganayagi, Manjushree Nayak
Abstract
In the field of genomics, the analysis of gene functioning and manipulation is critical for understanding the underlying mechanisms of genetic diseases and developing effective treatments. Deep learning algorithms have shown great potential in processing large volumes of genomic data and predicting gene function. However, deep learning models require large amounts of labeled data for training, which can be a challenge in genomics where data labeling is time-consuming and expensive. To get over this restriction, deep learning and evolutionary algorithms have been used to optimize model hyperparameters and boost accuracy. Through recurrent candidate selection and modification, genetic algorithms apply evolutionary biology principles to optimize complicated solutions. By using this method, the quantity of labeled data needed to train deep learning models can be greatly decreased, and gene function predictions can be made more accurately. Overall, deep learning and genetic algorithms provide a potent tool for the investigation and modulation of gene function. These methods could result in quicker drug development, more precise gene function predictions, and personalized therapy. To ensure the ethical use of these technologies, it is important to recognize and resolve the ethical issues raised by genetic alteration. Furthermore, by deciding on the best guide RNAs for the editing process, genetic algorithms can be utilized to optimize gene editing research like CRISPR-Cas9 gene editing. This strategy can improve the effectiveness and precision of gene editing while minimizing unwanted side effects and potential ethical issues.