GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization.

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naive Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor.

The protein-binding potential of C2H2 zinc finger domains.

There are over 10,000 C2H2-type zinc finger (ZF) domains distributed among more than 1,000 ZF proteins in the human genome. These domains are frequently observed to be involved in sequence-specific DNA binding, and uncharacterized domains are typically assumed to facilitate DNA interactions. However, some ZFs also facilitate binding to proteins or RNA. Over 100 Cys2-His2 (C2H2) ZF-protein interactions have been described. We initially attempted a bioinformatics analysis to identify sequence features that would predict a DNA- or protein-binding function. These efforts were complicated by several issues, including uncertainties about the full functional capabilities of the ZFs. We therefore applied an unbiased approach to directly examine the potential for ZFs to facilitate DNA or protein interactions. The human OLF-1/EBF associated zinc finger (OAZ) protein was used as a model. The human O/E-1-associated zinc finger protein (hOAZ) contains 30 ZFs in 6 clusters, some of which have been previously indicated in DNA or protein interactions. DNA binding was assessed using a target site selection (CAST) assay, and protein binding was assessed using a yeast two-hybrid assay. We observed that clusters known to bind DNA could facilitate specific protein interactions, but clusters known to bind protein did not facilitate specific DNA interactions. Our primary conclusion is that DNA binding is a more restricted function of ZFs, and that their potential for mediating protein interactions is likely greater. These results suggest that the role of C2H2 ZF domains in protein interactions has probably been underestimated. The implication of these findings for the prediction of ZF function is discussed.