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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Efficient mining of interesting patterns in large biological sequences.

Md Mamunur Rashid¹, Md Rezaul Karim, Byeong-Soo Jeong

¹Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, Yongin 446-701, Korea.

Genomics & Informatics

|October 30, 2012

Summary

This study introduces a novel measure for identifying significant patterns in biological sequences, moving beyond simple frequency counts. An efficient method finds these informative patterns in DNA sequence analysis within acceptable computational time.

Keywords:

DNA sequence index-based method information gain pattern mining

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Published on: June 28, 2018

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Pattern discovery in biological sequences is a core challenge.
Traditional methods rely heavily on pattern frequency.
Infrequent patterns can hold significant biological information.

Purpose of the Study:

To propose a new measure for identifying biologically informative patterns.
To develop an efficient algorithm for mining these patterns.

Main Methods:

Introduced a novel "interestingness" measure for biological patterns.
Developed an index-based method for pattern mining.
Conducted experimental validation.

Main Results:

The proposed measure effectively identifies informative patterns.
The index-based method efficiently mines these patterns.
Experimental results confirm computational feasibility.

Conclusions:

The new measure provides a more biologically relevant approach to pattern discovery.
The efficient mining method facilitates the analysis of large biological datasets.
This work advances the field of computational biology by enabling deeper insights into sequence data.