Digital Commons Network^™

Treatment For Adolescents With Depression Study (Tads): Safety Results., Graham Emslie, Christopher J. Kratochvil, Benedetto Vitiello, Susan Silva, Taryn Mayes, Steven Mcnulty, Elizabeth Weller, Bruce Waslick, Charles Casat, John Walkup, Sanjeev Pathak, Paul Rohde, Kelly Posner, John March, The Columbia Suicidality Classification Group, Tads Team

Journal Articles: Psychiatry

OBJECTIVE: To compare the rates of physical, psychiatric, and suicide-related events in adolescents with MDD treated with fluoxetine alone (FLX), cognitive-behavioral therapy (CBT), combination treatment (COMB), or placebo (PBO).

METHOD: Safety assessments included adverse events (AEs) collected by spontaneous report, as well as systematic measures for specific physical and psychiatric symptoms. Suicidal ideation and suicidal behavior were systematically assessed by self- and clinician reports. Suicidal events were also reanalyzed by the Columbia Group and expert raters using the Columbia-Classification Algorithm for Suicidal Assessment used in the U.S. Food and Drug Administration reclassification effort.

RESULTS: Depressed adolescents reported high rates of …

Bifurcation And Singularity Analysis Of A Molecular Network For The Induction Of Long-Term Memory, Hao Song, Paul Smolen, Evyatar Av-Ron, Douglas A. Baxter, John H H. Byrne

Journal Articles

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying …

Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie

Dartmouth Scholarship

The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.

Laboratory Diagnosis Of Tuberculosis In Primary Care, David Brett-Major, Thomas E. Walsh

Journal Articles: Epidemiology

No abstract provided.

Using Citation Data To Improve Retrieval From Medline., Elmer V Bernstam, Jorge R Herskovic, Yindalon Aphinyanaphongs, Constantin F Aliferis, Madurai G Sriram, William R Hersh

Journal Articles

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. …

Automatic Colonic Polyp Detection Using Multiobjective Evolutionary Techniques, Jiang Li, Adam Huang, Jianhua Yao, Ingmar Bitter, Nicholas Petrick, Ronald M. Summers, Perry J. Pickhardt, J. Richard Choi

Electrical & Computer Engineering Faculty Publications

Colonie polyps appear like elliptical protrusions on the inner wall of the colon. Curvature based features for colonie polyp detection have proved to be successful in several computer-aided diagnostic CT colonography (CTC) systems. Some simple thresholds are set for those features for creating initial polyp candidates, sophisticated classification scheme are then applied on these polyp candidates to reduce false positives. There are two objective functions, the number of missed polyps and false positive rate, that need to be minimized when setting those thresholds. These two objectives conflict and it is usually difficult to optimize them both by a gradient search. …

Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …