Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.

ABSTRACT: A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and

SUBMITTER: Gupta M 

PROVIDER: S-EPMC8337154 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature