Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis.

ABSTRACT:

Background

Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA).

Results

We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA. F

SUBMITTER: Gonzalez-Calabozo JM 

PROVIDER: S-EPMC5024470 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature