Computer Engineering Commons^™

Ennet: Inferring Large Gene Regulatory Networks From Expression Data Using Gradient Boosting, Janusz Slawek, Tomasz Arodź

Computer Science Publications

Background

The regulation of gene expression by transcription factors is a key determinant of cellular phenotypes. Deciphering genome-wide networks that capture which transcription factors regulate which genes is one of the major efforts towards understanding and accurate modeling of living systems. However, reverse-engineering the network from gene expression profiles remains a challenge, because the data are noisy, high dimensional and sparse, and the regulation is often obscured by indirect connections.

Results

We introduce a gene regulatory network inference algorithm ENNET, which reverse-engineers networks of transcriptional regulation from a variety of expression profiles with a superior accuracy compared to the state-of-the-art …

An Entropy-Based Automated Cell Nuclei Segmentation And Quantification: Application In Analysis Of Wound Healing Process, Varun Oswal, Ashwin Belle, Robert Diegelmann, Kayvan Najarian

Computer Science Publications

The segmentation and quantification of cell nuclei are two very significant tasks in the analysis of histological images. Accurate results of cell nuclei segmentation are often adapted to a variety of applications such as the detection of cancerous cell nuclei and the observation of overlapping cellular events occurring during wound healing process in the human body. In this paper, an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images has been presented. The proposed translational computation system aims to integrate clinical insight and computational analysis by identifying and segmenting objects of interest within histological …