The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Python's Role in Predicting Type 2 Diabetes Using Insulin DNA Sequence
Abstract
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.
Related Content
|
Rashmi Gupta, Jeetendra Kumar, Suvarna Sharma.
© 2026.
32 pages.
|
|
Yashodeep Bharat Deshmukh, Abhishek Mukhopadhyay.
© 2026.
42 pages.
|
|
Suriya Murugan, Anandakumar Haldorai.
© 2026.
20 pages.
|
|
Meetu Malhotra, Rahul Awasthy.
© 2026.
34 pages.
|
|
Ismail Lamaakal, Bentaleb Youssef, Yassine Maleh, Ibrahim Ouahbi, Khalid El Makkaoui.
© 2026.
34 pages.
|
|
Muthmainnah Muthmainnah, Besse Darmawati, Abd. Rasyid, Sutejo Sutejo, Sri Haryatmo, Nurweni Saptawuryandari, Ahmad Al Yakin, Ismail Lamaakal.
© 2026.
28 pages.
|
|
Wasswa Shafik.
© 2026.
32 pages.
|
|
|