Development And Application of Artificial Neural Network and Deep Learning Frameworks for Information Processing

Resumen

Massive datasets of molecular sequences, medical images, and other structured information pose challenges of their utilization and interpretation through traditional data analysis methods. The current study aims to explore how Artificial Intelligence procedures like deep learning can improve predictive modeling and pattern recognition in healthcare analytics and information processing. This study proposes a two-stage deep learning model that combines long shortterm memory (LSTM), convolutional neural networks (CNNs), and natural language processing (NLP) techniques. This combination helps improve the accuracy of predictions The study also proposed the use of "SENIES," the DNA Shape Enhanced Two-Layer Deep Learning Predictor, a computational method used to identify enhancer regions within DNA sequences. The study used a scientific and exploratory methodology for the identification and characterization of the enhancer, utilizing a sample of active enhancers from a cohort of 9,000 cancer patients from a machine learning powered database. The data was analyzed through Mathew's correlation coefficient principle using steps like precision recall, specificity, and accuracy. This method is commonly used to evaluate categorization accuracy. The study found that when identified enhancers are placed next through AI-based evaluation to assess their characteristics, they can decipher their regulatory functions, and determine their relationships to the target genes. It was also concluded that the proposed models work well with different dataset sizes, making it flexible for various applications, leading to the integration of AI-driven biological data along with the recognition and prioritization of functional non-coding mutations, as an efficient method of cancer research. The study recommends incorporation of some of the additional AI-driven methods for better results and accurate predictions. Future research should focus on integrating predictive models for real-time data analysis, which would help improve the development and effectiveness of such models