Python's Role in Predicting Type 2 Diabetes Using Insulin DNA Sequence

Python's Role in Predicting Type 2 Diabetes Using Insulin DNA Sequence

Aswathi Sasidharan, N. Arulkumar
Copyright: © 2023 |Pages: 13
DOI: 10.4018/978-1-6684-7100-5.ch014
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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.
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Predicting Type 2 Diabetes

Important for early intervention and disease prevention is the ability to predict type 2 diabetes. By identifying individuals at risk for developing type 2 diabetes, healthcare professionals can provide targeted interventions, such as lifestyle modifications or medication, to prevent or better manage the disease. Alterations in the insulin gene sequence can contribute to the development of type 2 diabetes, making the sequencing of insulin DNA a crucial component of predicting type 2 diabetes (Batista et al, 2020). Understanding the genetic basis of type 2 diabetes can lead to more effective interventions and therapies.

Predicting Type 2 Diabetes Using Insulin DNA Sequence

Multiple methods exist in which Python can contribute to research on predicting type 2 diabetes using insulin DNA sequences.

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    Data Administration and Analysis: NumPy, Pandas, and Scikit-learn are Python libraries that can preprocess and analyze DNA sequence data. These libraries provide data manipulation, purification, analysis functions, and methods essential for predicting type 2 diabetes based on the insulin DNA sequence (Li et al., 2023).

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    Automatic Learning: Python has become the most popular programming language for machine learning and artificial intelligence research due to its simplicity and easy learning syntax. Using Python's machine learning libraries like Scikit-learn, TensorFlow, and Keras, it is possible to develop predictive models for identifying DNA markers that could be used to predict type 2 diabetes (Li et al., 2021).

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    Visualization The powerful visualization libraries of Python, such as Matplotlib and Seaborn, can facilitate data visualization, such as DNA sequence data. With visualization, researchers can better comprehend and investigate the relationship between the numerous DNA sequences and type 2 diabetes.

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    Integration with other tools Python is easily compatible with standard bioinformatics tools for DNA sequence analysis, such as BLAST, EMBOSS, and ClustalW. These tools can be used to compare the DNA sequence data for insulin with other DNA sequences in a database to identify similarities and differences that could aid in predicting type 2 diabetes (Rather et al., 2023).

Python provides data analysis, machine learning, visualization, and integration with other bioinformatics tools, thereby playing a crucial role in research on predicting type 2 diabetes based on the insulin DNA sequence.

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