Fast and Accurate Gene Prediction Using GPU-Accelerated ML Techniques

EasyChair Preprint no. 14024

Abstract

Gene prediction plays a crucial role in deciphering genomic sequences and understanding biological functions. Traditional methods often face challenges in balancing speed and accuracy, particularly as genomic data scales exponentially. This abstract proposes a novel approach leveraging GPU-accelerated machine learning (ML) techniques to enhance the efficiency and precision of gene prediction.

By harnessing the parallel processing capabilities of GPUs, this study aims to accelerate gene prediction algorithms, thereby reducing computational time without compromising predictive accuracy. The integration of ML models, optimized for GPU architectures, promises to address the computational bottleneck inherent in genomic data analysis.

Key objectives include the development of GPU-accelerated models capable of handling large-scale genomic datasets and the evaluation of their performance against traditional CPU-based methods. Evaluation metrics will focus on accuracy, speed, and scalability, demonstrating the potential of GPU-enhanced techniques in advancing genomic research.

Keyphrases: Central Processing Units, Graphics Processing Units, machine learning

@Booklet{EasyChair:14024,
  author = {Abi Cit},
  title = {Fast and Accurate Gene Prediction Using GPU-Accelerated ML Techniques},
  howpublished = {EasyChair Preprint no. 14024},

  year = {EasyChair, 2024}}