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Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis.

Nabil Ibtehaz¹, S M Shakhawat Hossain Sourav², Md Shamsuzzoha Bayzid¹

¹Department of CSE, BUET, ECE Building, West Palasi, Dhaka, 1205, Bangladesh.

The Protein Journal

|March 28, 2023

Summary

This summary is machine-generated.

Researchers developed Align-gram, a novel k-mer embedding method, to improve protein sequence analysis using deep learning. This approach enhances the performance of models like LSTM and CNN for various biological applications.

Keywords:

Deep learning Protein sequence analysis Skip-gram model Word embedding k-mer

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing has led to a massive increase in biological sequence data.
Protein sequences are fundamental to understanding biological functions and processes.
Deep learning and Natural Language Processing (NLP) techniques are increasingly applied to biological data analysis.

Purpose of the Study:

To investigate the applicability of Skip-gram models for protein sequence analysis.
To incorporate biological insights into k-mer embedding methods.
To develop and evaluate a novel embedding scheme for improved protein sequence representation.

Main Methods:

Proposed a novel k-mer embedding scheme named Align-gram.
Mapped similar k-mers to adjacent positions in a vector space.
Experimented with Align-gram embeddings using Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models, including DeepGoPlus.

Main Results:

Align-gram effectively maps similar k-mers together in a vector space.
Embeddings derived from Align-gram demonstrated superior performance in deep learning model training and performance.
The proposed method showed potential for diverse deep learning applications in protein sequence analysis.

Conclusions:

Align-gram offers a biologically informed approach to k-mer embedding for protein sequences.
This novel embedding scheme enhances the effectiveness of deep learning models for protein analysis.
Align-gram shows promise for advancing various computational biology and bioinformatics applications.