Paper Title
Neuromorphic Computing for Next-Generation Bioinformatics: Energy-Efficient AI for Personalized Healthcare

Abstract
This paper delves into how the application of neuromorphic computing, which draws its inspiration from the human brain for its architecture and event-driven processes, along with its ability to consume immensely low amounts of energy is transforming Bioinformatics. Teaming up bioinformatics with neuromorphic computational systems has enabled real-time and personalized healthcare. This paper also explores the use of neuromorphic technologies in genomic data analyses, wearable healthcare devices, disease prediction (cancer and neurodegenerative diseases), and disease modeling (neurodegenerative diseases). Various case studies showcase increased processing speed and decreased energy requirement over traditional deep learning models. The paper also throws light upon the challenges that still persist with the technology in the field of bioinformatics and healthcare and tries to propose some measures to mitigate these problems such as multi-omics integration and ethical risks. Various solutions such as federated learning and explainable AI have been proposed. By the amalgamation of neuromorphic computing with bioinformatics, a scalable, real-time and more robust future can be pictured in the field of precision medicine. Keywords - Neuromorphic Computing, Bioinformatics, Spiking Neural Networks, Wearable Health Devices, Federated Learning.