CancerDiscover: an integrative pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data
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Akram Mohammed1,*, Greyson Biegert1,*, Jiri Adamec1 and Tomáš Helikar1
1Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
*These authors have contributed equally to this work
Tomáš Helikar, email: [email protected]
Keywords: open-source; cancer classification; gene expression; machine learning; cancer biomarker
Received: September 28, 2017 Accepted: December 09, 2017 Published: December 20, 2017
Accurate identification of cancer biomarkers and classification of cancer type and subtype from High Throughput Sequencing (HTS) data is a challenging problem because it requires manual processing of raw HTS data from various sequencing platforms, quality control, and normalization, which are both tedious and time-consuming. Machine learning techniques for cancer class prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. To date, great research efforts have been taken for cancer biomarker identification and cancer class prediction. However, currently available tools and pipelines lack flexibility in data preprocessing, running multiple feature selection methods and learning algorithms, therefore, developing a freely available and easy-to-use program is strongly demanded by researchers. Here, we propose CancerDiscover, an integrative open-source software pipeline that allows users to automatically and efficiently process large high-throughput raw datasets, normalize, and selects best performing features from multiple feature selection algorithms. Additionally, the integrative pipeline lets users apply different feature thresholds to identify cancer biomarkers and build various training models to distinguish different types and subtypes of cancer. The open-source software is available at https://github.com/HelikarLab/CancerDiscover and is free for use under the GPL3 license.
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