Genomics Inform.  2003 Dec;1(2):65-74.

Data Mining for High Dimensional Data in Drug Discovery and Development

• Lee KR
• Park DC
• Lin X
• Eslava S

Abstract

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

Keyword

data mining; high dimensional data, genomics; proteomics, pharmacogenomics

MeSH Terms

Classification
Data Mining*
Dataset
Decision Trees
Disease Management
Drug Discovery*
Genomics
Proteomics
Retrospective Studies
Statistics as Topic