Roberto Visintainer

TL;DR: P predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans are generated.

TL;DR: RNA-seq outperforms microarray in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts, and classifiers to predict MOAs perform similarly when developed using data from either platform.

TL;DR: A novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines is introduced.

TL;DR: MLP as mentioned in this paper is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries, which provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency.

TL;DR: The Hamming-Ipsen-Mikhailov (HIM) distance is introduced, a novel metric to quantitatively measure the difference between two graphs sharing the same vertices, to overcome the drawbacks affecting the two components when considered separately.