BIOMEDICAL TEXT MINING AND ITS APPLICATION IN CANCER RESEARCH

Henry Ikediego118026

OVERVIEW

Objectives

Introduction

Biomedical text mining phases and tasks

Data sets and tools for biomedical text mining

Application of biomedical text mining in cancer research

Cancer systems biology research with text mining approach

Future work and challenges

Conclusions

OBJECTIVES

After completing this presentation you should :

Know the phases and tasks in biomedical text mining.

Know the application of biomedical text mining in

cancer research.

The challenges of cancer research in biomedical text

mining

INTRODUCTION

Cancer is a malignant disease that has caused so

many deaths. The immense body and rapid growth of

biomedical text on cancer has led to the appearance

of large number of text mining techniques aimed at

extracting unique knowledge from scientific text.

Biomedical text mining on cancer research is

computationally automatic and high-throughput in

nature.

BIOMEDICAL TEXT MINING PHASES AND TASKS

The goal of text mining is to derive implicit knowledge that

hides in unstructured text and present it in an explicit form.

There are four phases in biomedical text mining:

Information retrieval: this aims at getting desired text on a

certain topic.

Information extraction: this system is used to extract

predefined types of information such as relation extraction.

Knowledge extraction: this system helps to extract

important knowledge from texts.

Hypothesis generation: this system infer unknown

biomedical facts based on texts.

GENERAL TASKS IN BIOMEDICAL TEXT MINING

There are four general tasks of biomedical text mining and they include:

Information retrieval

Named entity recognition and relation

extraction

Knowledge discovery

Hypothesis generation

CONVENTIONAL PHASES AND TASKS INVOLVED IN BIOMEDICAL TEXT MINING

DATA SETS AND TOOLS FOR BIOMEDICAL TEXT MINING

Examples of data set and tools used for biomedical text mining:

PubMed: this is one of the best known biomedical databases and

it contains more than 20 million citations on biomedical articles.

Textpresso: this uses an ontology, returns searching goals for

classes of biological concept (e.g., gene, cell), classes of

relations of objects (e.g., association, regulation), and related

description (biological process).

GoPubMed: this classifies literature abstracts according to a

Gene ontology and shows the ontology terms that are related to

the query words.

ETC.

APPLICATION OF BIOMEDICAL TEXT MINING IN CANCER RESEARCH

As a complex disease, cancer is related to a

large number of genes and proteins.

Biomedical researchers are interested in

mining cancer-related genes and proteins

from the literature to study cancer

diagnostics, treatment, and prevention.

CANCER SYSTEMS BIOLOGY RESEARCH WITH TEXT MINING APPROACH

Researchers tend to understand complex biological

systems from a systems biology viewpoint. Systems

biology-based networks cab be constructed by

aggregating previously reported associations from the

literature or various databases.

Generally, the conventional flow of text mining based

cancer systems biology research is text acquisition,

bio-entity terms recognition, complex relation

extraction, new knowledge discovery, and hypothesis

generation.

AN ILLUSTRATION OF A TEXT MINING-ASSISTED CANCER STUDY WORKFLOW FROM A SYSTEMS BIOLOGY VIEWPOINT.

FUTURE WORK AND CHALLENGES

Challenges:

Application of biomedical text mining technologies in

the personalized medicine development.

Complex of cancer molecular mechanisms.

Application of text mining techniques in translational

medicine research.

The integration of the text information at molecule,

cell, tissue, organ. Individual and even population

levels to understand the complex biological systems.

The de-noising and testing of text mining results.

CONCLUSIONS

There are huge body of biomedical text and their rapid

growth makes it impossible for researchers to address

the information manually. Researchers can use

biomedical text mining to discover new knowledge.

Text mining has been used widely in cancer research.

However to fully utilize text mining , it is still

necessary to develop new methods for full text mining

and for highly complex text, as well as platforms for

integrating other biomedical knowledge bases.