Hard Data Analytics Problems Make for Better Data Analysis Algorithms: Bioinformatics as an Example.

ABSTRACT: Data mining and knowledge discovery techniques have greatly progressed in the last decade. They are now able to handle larger and larger datasets, process heterogeneous information, integrate complex metadata, and extract and visualize new knowledge. Often these advances were driven by new challenges arising from real-world domains, with biology and biotechnology a prime source of diverse and hard (e.g., high volume, high throughput, high variety, and high noise) data analytics problems. The aim of this article is to show the broad spectrum of data mining tasks and challenges present in biological data, and how these challenges have driven us over the years to design new data mining and knowledge discovery procedures for biodata. This is illustrated with the help of two kinds of case studi

SUBMITTER: Bacardit J 

PROVIDER: S-EPMC4174911 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature