Species translatable blood gene signature as a marker of exposure to smoking: Computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology Challenge

• Published in: Computational toxicology Vol. 5; pp. 25 - 30
• Main Authors: Saraç, Ömer Sinan, Kumar, Rahul, Dhanda, Sandeep Kumar, Balcı, Ali Tuğrul, Bilgen, İsmail, Romero, Roberto, Tarca, Adi L.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2018
• Subjects: Computational challenge; Predictive modeling; Smoking biomarker; Species-translatable gene signature; Systems toxicology; Species-translatable gene signature; sbv; LASSO; IMPROVER; LDA; Computational challenge; Smoking biomarker; Predictive modeling; Systems toxicology; species-translatable gene signature; computational challenge; smoking biomarker; predictive modeling

•Common blood mRNAs predict current exposure to cigarette smoke in mouse and human.•Organism specific signature is more accurate for prediction of current exposure.•Best predictive methods found in previous challenges are proven again to be robust. Crowdsourcing has been used to address computationa...