Identification of Cancer Hotspot Residues and Driver Mutations Using Machine Learning

Pandey, Medha; Paruchuri, Anoosha; Yesudhas, Dhanusha; Gromiha, M. Michael

doi:10.1142/9789811258589_0011

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, ethical considerations, including patient consent and data security, should be addressed in the implementation of AI-driven precision oncology. The study in [16] focuses on the identification of cancer hotspot residues and driver mutations using machine learning. Their work underscores the importance of machine learning in identifying critical genetic variations in cancer.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This manual approach not only consumes valuable time but also introduces the potential for inaccuracies, ultimately impeding the precision and efficiency of precision medicine practices [15]. In light of these challenges, there is an urgent need for innovative solutions that can alleviate the burden on healthcare professionals while simultaneously enhancing the accuracy of genetic variation classification within the precision medicine framework [16][17][18][19]. Here, the integration of advanced technologies, particularly machine learning, emerges as a promising avenue to address this critical issue.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification

Sivamanikanta,

Ravinder

2024

IJACSA

View full text Add to dashboard Cite

Precision medicine and genetic testing have the potential to revolutionize disease treatment by identifying driver mutations crucial for tumor growth in cancer genomes. However, clinical pathologists face the time-consuming and error-prone task of classifying genetic variations using Textual clinical literature. In this research paper, titled "Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification", we propose a solution to automate this process. We aim to develop a robust machine learning algorithm with a knowledge base foundation to streamline precision medicine. Our methods leverage advanced machine learning and natural language processing techniques, coupled with a comprehensive knowledge base that incorporates clinical and genetic data to inform mutation significance. We use text mining to extract relevant information from scientific literature, enhancing classification accuracy. Our results demonstrate significant improvements in efficiency and accuracy compared to manual methods. Our system excels at identifying driver mutations, reducing the burden on clinical pathologists and minimizing errors. Automating this critical aspect of precision medicine promises to empower healthcare professionals to make more precise treatment decisions, advancing the field and improving patient care.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification

Sivamanikanta,

Ravinder

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Current research in cancer is focused on studying the mutations in the cancer genomes to identify specific mutations in protein domains that may be associated with a poor prognosis [ 55 ]. Bioinformatics studies of mutations in protein domains in ovarian cancer also reveal therapeutic targets and prognostic indicators [ 56 ].…”

Section: Introductionmentioning

confidence: 99%

The importance of protein domain mutations in cancer therapy

Chitluri,

Emerson

2024

Heliyon

View full text Add to dashboard Cite

Identification of Cancer Hotspot Residues and Driver Mutations Using Machine Learning

Cited by 2 publications

References 0 publications

Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification

Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification

The importance of protein domain mutations in cancer therapy

Contact Info

Product

Resources

About