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Multi-Class Cancer Classification By Semi-Supervised Ellipsoid Artmap With Gene Expression Data, Rui Xu, Donald C. Wunsch, Georgios C. Anagnostopoulos
Multi-Class Cancer Classification By Semi-Supervised Ellipsoid Artmap With Gene Expression Data, Rui Xu, Donald C. Wunsch, Georgios C. Anagnostopoulos
Electrical and Computer Engineering Faculty Research & Creative Works
To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to binary classification, the discrimination of multiple tumor types is also important semi-supervised ellipsoid ARTMAP (ssEAM) is a novel neural network architecture rooted in adaptive resonance theory suitable for classification tasks. ssEAM can achieve fast, stable and finite learning and create hyper-ellipsoidal clusters inducing complex nonlinear decision boundaries. Here, we demonstrate the capability of ssEAM to discriminate …
Final Report: Engineering Design Of Stable Immobilized Enzymes For The Hydrolysis And Transesterification Of Triglycerides, Dr.Hossein Noureddini, Gustavo F. Larsen
Final Report: Engineering Design Of Stable Immobilized Enzymes For The Hydrolysis And Transesterification Of Triglycerides, Dr.Hossein Noureddini, Gustavo F. Larsen
Department of Chemical and Biomolecular Engineering: Funded Proposals
Enzyme Immobilization. Work will continue in the area of enzymatic transesterification reaction (biodiesel). Both methyl and ethyl esters will be used in this study. Unlike the chemical reaction where methanol has a clear advantage over ethanol, ethanol can be used as easily as methanol in the enzymatic reaction.
Sol/Gel Structure Modification. Work will concentrate on the effect of the vacuum procedure on pore size and distribution for the transesterification reaction. Additives such as glucose have been very effective in the hydrolysis reaction and will be explored further in the transesterification reaction.
Characterization. The developed material will be characterized for the …
Changes In Pulmonary Arterial Wall Mechanical Properties And Lumenal Architecture With Induced Vascular Remodeling, Robert C. Molthen, Amy Heinrich, Steven Thomas Haworth, Christopher A. Dawson
Changes In Pulmonary Arterial Wall Mechanical Properties And Lumenal Architecture With Induced Vascular Remodeling, Robert C. Molthen, Amy Heinrich, Steven Thomas Haworth, Christopher A. Dawson
Biomedical Engineering Faculty Research and Publications
To explore and quantify pulmonary arterial remodeling we used various methods including micro-CT, high-resolution 3-dimensional x-ray imaging, to examine the structure and function of intact pulmonary vessels in isolated rat lungs. The rat is commonly used as an animal model for studies of pulmonary hypertension (PH) and the accompanying vascular remodeling, where vascular remodeling has been defined primarily by changes in the vessel wall composition in response to hypertension inducing stimuli such as chronic hypoxic exposure (CHE) or monocrotaline (MCT) injection. Little information has been provided as to how such changes affect the vessel wall mechanical properties or the lumenal …
Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models, Rui Xu, Xiao Hu, Donald C. Wunsch
Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models, Rui Xu, Xiao Hu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.