B IOMEDICAL T EXT M INING AND ITS A PPLICATION IN C ANCER R ESEARCH Henry Ikediego

O VERVIEW  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

O BJECTIVES 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

I NTRODUCTION 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.

B IOMEDICAL T EXT M INING P HASES AND T ASKS 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.

G ENERAL 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

D ATA 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.

A PPLICATION OF B IOMEDICAL T EXT M INING IN C ANCER R ESEARCH  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.

C ANCER S YSTEMS B IOLOGY R ESEARCH WITH T EXT M INING A PPROACH  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.

A N 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.

C ONCLUSIONS  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.