Python's role in predicting type 2 diabetes using insulin DNA sequence

Title

Python's role in predicting type 2 diabetes using insulin DNA sequence

Creator

Sasidharan A.; Arulkumar N.

Description

This chapter examines how Python can assist in predicting type 2 diabetes using insulin DNA sequences, given the substantial problem that biologists face in objectively evaluating diverse biological characteristics of DNA sequences. The chapter highlights Python's various libraries, such as NumPy, Pandas, and Scikit- learn, for data handling, analysis, and machine learning, as well as visualization tools, such as Matplotlib and Seaborn, to help researchers understand the relationship between different DNA sequences and type 2 diabetes. Additionally, Python's ease of integration with other bioinformatics tools, like BLAST, EMBOSS, and ClustalW, can help identify DNA markers that could aid in predicting type 2 diabetes. In addition, the initiative tries to identify unique gene variants of insulin protein that contribute to diabetes prognosis and investigates the risk factors connected with the discovered gene variants. In conclusion, Python's versatility and functionality make it a valuable tool for researchers studying insulin DNA sequences and type 2 diabetes prediction. 2023, IGI Global. All rights reserved.

Source

Advanced Applications of Python Data Structures and Algorithms, pp. 266-278.

Date

2023-01-01

Publisher

IGI Global

Coverage

Sasidharan A., CHRIST University (Deemed), India; Arulkumar N., CHRIST University (Deemed), India

Rights

Restricted Access

Relation

ISBN: 978-166847102-9; 978-166847100-5

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/978-1-6684-7100-5.ch014

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167741607&doi=10.4018%2f978-1-6684-7100-5.ch014&partnerID=40&md5=d270380975c6783377b1a1741fb122dd