Machine Learning Approach for Cancer Entities Association and Classification
This addresses the laborious and time-consuming task of hypothesis formulation in cancer research, though it appears incremental as it applies existing NLP methods to biomedical data.
The study tackled the problem of manually screening large volumes of cancer research articles by using NLP techniques like Named Entity Recognition and text classification to extract and categorize cancer-related entities, resulting in a system that simplifies data categorization and article screening.
According to the World Health Organization (WHO), cancer is the second leading cause of death globally. Scientific research on different types of cancers grows at an ever-increasing rate, publishing large volumes of research articles every year. The insight information and the knowledge of the drug, diagnostics, risk, symptoms, treatments, etc., related to genes are significant factors that help explore and advance the cancer research progression. Manual screening of such a large volume of articles is very laborious and time-consuming to formulate any hypothesis. The study uses the two most non-trivial NLP, Natural Language Processing functions, Entity Recognition, and text classification to discover knowledge from biomedical literature. Named Entity Recognition (NER) recognizes and extracts the predefined entities related to cancer from unstructured text with the support of a user-friendly interface and built-in dictionaries. Text classification helps to explore the insights into the text and simplifies data categorization, querying, and article screening. Machine learning classifiers are also used to build the classification model and Structured Query Languages (SQL) is used to identify the hidden relations that may lead to significant predictions.