Text Mining for Building Biomedical Networks Using Cancer as a Case Study

Abstract

In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the interaction data is available through scientific literature. This has become a challenge with the notable increase in scientific literature being published, as it is hard for human curators to track all recent discoveries without using efficient tools to help them identify these interactions in an automatic way. This can be surpassed by using text mining approaches which are capable of extracting knowledge from scientific documents. One of the most important tasks in text mining for biological network building is relation extraction, which identifies relations between the entities of interest. Many interaction databases already use text mining systems, and the development of these tools will lead to more reliable networks, as well as the possibility to personalize the networks by selecting the desired relations. This review will focus on different approaches of automatic information extraction from biomedical text that can be used to enhance existing networks or create new ones, such as deep learning state-of-the-art approaches, focusing on cancer disease as a case-study.

A framework for considering prior information in network‐based approaches to omics data analysis

“…Despite this herculean effort, these manually curated databases cannot keep up with the rate of scientific production given the available resources. To support manual curation efforts, multiple natural language processing and crowd sourcing approaches to extract computable models from scientific literature have been developed [19], and recent large language models offer great promise in expanding these efforts [20].…”

A framework for considering prior information in network-based approaches to --omics data analysis

“…Despite this herculean effort, these manually curated databases cannot keep up with the rate of scientific production given the available resources. To support manual curation efforts, multiple natural language processing (NLP) and crowd sourcing approaches to extract computable models from scientific literature have been developed 10 , and recent language learning models (LLMs) offer great promise in expanding these efforts 11 . Additionally, in the case where there is very little existing literature about a system, networks can be inferred de novo or by expanding existing models 12 .…”

Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care

“…In addition to the use of genetic changes, the clinical use of a tumor mutation burden (TMB), microsatellite instability (MSI), and mutational signature patterns in cancers was reported by Bødker et al [ 1 ]. We previously created an in-house bioinformatic pipeline for data processing and analysis [ 2 ]. The Cancer Next-Generation Sequencing (NGS) Laboratory at the National Cheng Kung University Hospital (NCKUH is a hospital in North District, Tainan, Taiwan) provided the genome analysis workflow.…”