Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.

Background: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide.

Methods: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer.

Querying large graphs in biomedicine with colored graphs and decomposition.

In graph networks, graph structural analytics such as betweenness centrality has played an important role in finding the most central vertices in graph data. Hence, betweenness centrality has been heavily applied to discover the most important genes with respect to multiple diseases in biomedicine research. Considering color as a property of graph data to represent different categories for the nodes and edges in the graph, we may investigate the betweenness centrality of each colored subgraph composed of a specific color.

Text mining in a literature review of urothelial cancer using topic model.

Background: Urothelial cancer (UC) includes carcinomas of the bladder, ureters, and renal pelvis. New treatments and biomarkers of UC emerged in this decade. To identify the key information in a vast amount of literature can be challenging.

Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis.

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer.

A model comparison study of the flowering time regulatory network in Arabidopsis.

Background: Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study.

BioVision: an application for the automated image analysis of histological sections.

We describe a computer application, "BioVision", that can be trained to quickly and effectively classify and quantify user definable histological objects (e.g., senile plaques, neurofibrillary tangles) within single or double-labeled immunocytochemically stained sections.