Physical Sciences and Mathematics Commons^™

Expression Of The Microrna-143/145 Cluster Is Decreased In Hepatitis B Virus-Associated Hepatocellular Carcinoma And May Serve As A Biomarker For Tumorigenesis In Patients With Chronic Hepatitis B, Qi Zhao, Xiangfei Sun, Chao Liu, Tao Li, Juan Cui, Chengyong Qin

School of Computing: Faculty Publications

The aims of the present study were to identify the expression profile of microRNA (miR)‑143/145 in hepatitis B virus (HBV)‑associated hepatocellular carcinoma (HCC), explore its association with prognosis and investigate whether the serum miR‑143/145 expression levels may serve as a diagnostic indicator of HBV‑associated HCC. The microRNA (miRNA) chromatin immunoprecipitation dataset was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus databases, and analyzed using the Wilcoxon signed‑rank test. It was observed that the expression of miR‑143 and miR‑145 was decreased 1.5‑fold in HBV‑associated HCC samples compared with non‑tumor tissue in the TCGA and the GSE22058 datasets …

Articulatory Distinctiveness Of Vowels And Consonants: A Data-Driven Approach, Jun Wang, Jordan R. Green, Ashok Samal, Yana Yunusova

School of Computing: Faculty Publications

Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach.

Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were collected. First, classification accuracies were obtained using 2 complementary approaches: (a) Procrustes analysis and (b) a support vector machine. Procrustes distance was then used to measure the articulatory distinctiveness among vowels and consonants. Finally, the distance (distinctiveness) matrices of different vowel pairs and consonant pairs were used to derive articulatory vowel and consonant spaces …

Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi

School of Computing: Faculty Publications

This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …