Life Sciences Commons^™

Cross-Talk Between Clinical And Host-Response Parameters Of Periodontitis In Smokers, Radha Nagarajan, Craig S. Miller, Dolph R. Dawson Iii, Mohanad Al-Sabbagh, Jeffrey L. Ebersole

Institute for Biomedical Informatics Faculty Publications

Background and Objective

Periodontal diseases are a major public health concern leading to tooth loss and have also been shown to be associated with several chronic systemic diseases. Smoking is a major risk factor for the development of numerous systemic diseases, as well as periodontitis. While it is clear that smokers have a significantly enhanced risk for developing periodontitis leading to tooth loss, the population varies regarding susceptibility to disease associated with smoking. This investigation focused on identifying differences in four broad sets of variables, consisting of: (i) host‐response molecules; (ii) periodontal clinical parameters; (iii) antibody responses to periodontal pathogens …

Integrated Biomarker Profiling Of Smokers With Periodontitis, Radhakrishnan Nagarajan, Mohanad Al-Sabbagh, Dolph Dawson Iii, Jeffrey L. Ebersole

Institute for Biomedical Informatics Faculty Publications

Background

In the context of precision medicine, understanding patient‐specific variation is an important step in developing targeted and patient‐tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups.

Aim

The goal of this study was to discern biological response profiles of periodontitis and non‐periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers).

Material & Methods …

Predicting Disease-Related Genes Using Integrated Biomedical Networks, Jiajie Peng, Kun Bai, Xuequn Shang, Guohua Wang, Hansheng Xue, Shuilin Jin, Liang Cheng, Yadong Wang, Jin Chen

Institute for Biomedical Informatics Faculty Publications

Background: Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.

Results: We propose a new network-based disease …