Sequence classification and similarity search play crucial roles in analyzing genomic sequences, enabling researchers to uncover patterns, relationships, and functional characteristics within vast amounts of genetic data. It also enables the interpretation of genetic information, functional annotation, comparative genomics, disease diagnosis, drug discovery, and advancing the understanding of complex biological systems.
This research project presents an overview of sequence classification and similarity search methods specifically designed for fungal sequences. It explores the techniques of machine learning algorithms, highlighting their strengths, limitations, and applications in genomic sequence analysis.
The research discusses the challenges associated with genomic sequence classification and similarity search, such as handling large-scale datasets, addressing sequence variations, and considering computational efficiency. Furthermore, it explores emerging trends and advancements in the field, such as deep learning models and graph-based methods, and their potential impact on enhancing sequence analysis capabilities.
The insights provided in this project aim to assist researchers in selecting appropriate deep-learning approaches for effective fungal sequence classification and similarity search in genomic studies.
Keywords
Convolutional Neural Network, Fully Connected Network, Fungal Classification, ITS Region, K-mer, Machine Learning