Prof. Carolina Ruiz

Computer Science Department

Bioinformatics and Computational Biology Program

WPI

WELCOME TO

BCB4003/CS4803

BCB503/CS583

BIOLOGICAL AND BIOMEDICAL DATABASE MINING

WHY THIS COURSE?

Biological and BiomedicalResearch Problems

Genome 1980’s-1990’sSequencing, sequence analysis, …

Proteome 1990’s-2000’s

Protein structure, protein-protein interactions, protein pathways

Central dogma: DNA (trascription) RNA (translation) Protein

Transcriptomemid 1990’s-2000’s Gene expression,

DNA/RNA microarrays

Biological Function

2000’s

Applications 2000’sOrganism-organism interactions

Organism-environment interactionsGenome-wide association studies

Cancer therapiesDrug development

THIS ALL HAS GENERATED …

• Data• Massive datasets and databases of sequence, gene, gene

expression, protein, biological function, clinical information, …

• Text• Annotations in data sources, abstracts (e.g., Medline), research

articles, medical literature (e.g., PubMed, NCBI Bookshelf, Google Scholar), patients records, …

• Ontologies• Description of terms and their relationship

• (e.g., Gene Ontology)

CURRENT CHALLENGES

• To make sense of and put to use all this information.

• How? Computational tools and techniques are needed to help humans in integrating, summarizing, understanding, and taking advantage of accumulated information• Data mining• Text mining• Data and text mining together

“Non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data [text]” (Fayyad et al., 1996)

• Raw Data [Text] Data [Text] Mining

• Patterns

• Analytical Patterns (rules, decision trees)

• Statistical Patterns (data distribution)

• Visual Patterns

Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. "From Data Mining to Knowledge Discovery in Databases" AAAI Magazine, pp. 37-54. Fall 1996.

WHAT IS DATA [TEXT] MINING?OR MORE GENERALLY, KNOWLEDGE DISCOVERY IN DATABASES (KDD)

DATA MINING METHODS IN BIOINFORMATICS

• Clustering

• Sequence Mining

• Bayesian Methods

• Expectation Maximization (EM)

• Gibbs Sampling

• Hidden Markov Models

• Kernel methods

• Support Vector Machines

TEXT MINING IN BIOINFORMATICS• Document indexing

• Information retrieval

• Lexical analysis (Sentence tokenization, Word tokenization, Stemming, Stop word removal)

• Semantic analysis

• Query processing

• Text classification

• Text clustering

• Text summarization

• (Semi-) Automatic curation of literature repositories

• Knowledge discovery from text, hypothesis generation

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West

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DATA/TEXT MINING PROCESS (KDD)

information sources

data analysisdata mining• analytical• statistical• visual

models

model/patterns deployment• prediction

• decision supportnew data

data management• databases

• data warehouses“good” model

model/patternevaluation• quantitative• qualitative

data “pre”-processing

• noisy/missing data • feature selection

cleaneddata

data

PUTTING ALL TOGETHER …

• Data / Text / Information Integration• Mining over data and text combined

• Visualization

• Other real-world issues• Developing tools and techniques that are

efficient, scalable, and user friendly

• Biology and Biomedicine

• Contributes domain knowledge

• Machine Learning (AI)

• Contributes (semi-)automatic induction of empirical laws from observations & experimentation

• Statistics

• Contributes language, framework, and techniques

• Pattern Recognition

• Contributes pattern extraction and pattern matching techniques

• Natural Language Processing (AI) Computational Linguistics• Contributes text analysis techniques

• Databases• Contributes efficient data storage, data

cleansing, and data access techniques

• Data Visualization• Contributes visual data displays and

data exploration

• High Performance Comp.• Contributes techniques to efficiently

handling complexity

• Signal processing

• Image Processing …

INTERDISCIPLINARY TECHNIQUES COME FROM MULTIPLE FIELDS

QUESTIONS?

* Images in this presentation were downloaded from Google images