PSB 2019 Workshop on Text Mining and Visualization for Precision Medicine

Gonzalez-Hernandez, Graciela; Lu, Zhiyong; Leaman, Robert; Weissenbacher, Davy; Boland, Mary Regina; Chen, Yong; Du, Jingcheng; Fluck, Juliane; Greene, Casey S.; Holmes, John L; Kashyap, Aditya; Nielsen, Ruby I.; Ouyang, Zhengqing; Schaaf, Sebastian; Taroni, Jaclyn N.; Cui, Tao; Zhang, Yuping; Liu, Hongfang

doi:10.1142/9789813279827_0042

Cited by 1 publication

(2 citation statements)

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“…While this review highlights important challenges, it lacks a discussion of potential ethical concerns related to data privacy and security in precision medicine programs. The study in [5] explores the intersection of text mining and visualization in precision medicine. Their work sheds light on the role of text mining in extracting and presenting valuable information from biomedical literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the rapidly evolving landscape of precision medicine, a groundbreaking transformation is underway, promising to revolutionize healthcare by tailoring treatments to individuals based on their unique genetic profiles [1][2][3][4][5][6][7][8]. This paradigm shift represents a departure from the traditional one-size-fitsall approach in medicine and holds immense potential to enhance the effectiveness of disease treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

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

Sivamanikanta,

Ravinder

2024

IJACSA

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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.

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Section: Literature Reviewmentioning

confidence: 99%