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Overexpression Of Dcf1 Inhibits Glioma Through Destruction Of Mitochondria And Activation Of Apoptosis Pathway, Yuqiong Xie, Qang Li, Qingbo Yang
Overexpression Of Dcf1 Inhibits Glioma Through Destruction Of Mitochondria And Activation Of Apoptosis Pathway, Yuqiong Xie, Qang Li, Qingbo Yang
Electrical and Computer Engineering Faculty Research & Creative Works
Gliomas are the most common brain tumors affecting the central nervous system and are associated with a high mortality rate. DCF1 is a membrane protein that was previously found to play a role in neural stem cell differentiation. In the present study, we found that overexpression of dcf1 significantly inhibited cell proliferation, migration, and invasion and dramatically promoted apoptosis in the glioblastoma U251 cell line. DCF1 deletion mutations in the functional region showed that the complete structure of DCF1 was necessary for apoptosis. Furthermore, significantly lower tumorigenicity was observed in athymic nude mice by transplanting U251 cells overexpressing dcf1. To …
Clustering Of High-Dimensional Gene Expression Data With Feature Filtering Methods And Diffusion Maps, Rui Xu, Steven Damelin, Boaz Nadler, Donald C. Wunsch
Clustering Of High-Dimensional Gene Expression Data With Feature Filtering Methods And Diffusion Maps, Rui Xu, Steven Damelin, Boaz Nadler, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
The importance of gene expression data in cancer diagnosis and treatment by now has been widely recognized by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality, due to the overwhelming number of measures of gene expression levels versus the small number of samples. Here, we use a two-step method to reduce the dimension of gene expression data. At first, we extract a subset of genes based on the statistical characteristics of their corresponding gene expression measurements. For further dimensionality reduction, we then apply diffusion maps, …
Applications Of Diffusion Maps In Gene Expression Data-Based Cancer Diagnosis Analysis, Rui Xu, Donald C. Wunsch, Steven Damelin
Applications Of Diffusion Maps In Gene Expression Data-Based Cancer Diagnosis Analysis, Rui Xu, Donald C. Wunsch, Steven Damelin
Electrical and Computer Engineering Faculty Research & Creative Works
Early detection of a tumor's site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. One of the major problems in cancer type recognition-oriented gene expression data analysis is the overwhelming number of measures of gene expression levels versus the small number of samples, which causes the curse of dimension issue. Here, we use diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain …
Gene Expression Data For Dlbcl Cancer Survival Prediction With A Combination Of Machine Learning Technologies, Rui Xu, Xindi Cai, Donald C. Wunsch
Gene Expression Data For Dlbcl Cancer Survival Prediction With A Combination Of Machine Learning Technologies, Rui Xu, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Gene expression profiles have become an important and promising way for cancer prognosis and treatment. In addition to their application in cancer class prediction and discovery, gene expression data can be used for the prediction of patient survival. Here, we use particle swarm optimization (PSO) to address one of the major challenges in gene expression data analysis, the curse of dimensionality, in order to discriminate high risk patients from low risk patients. A discrete binary version of PSO is used for gene selection and dimensionality reduction, and a probabilistic neural network (PNN) is implemented as the classifier. The experimental results …
Gene Regulatory Networks Inference With Recurrent Neural Network Models, Rui Xu, Donald C. Wunsch
Gene Regulatory Networks Inference With Recurrent Neural Network Models, Rui Xu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts from artificial intelligence and machine learning. Here, we use a recurrent neural network (RNN), trained with particle swarm optimization (PSO), to investigate the behaviors of regulatory networks. The experimental results, on a synthetic data set and a real data set, show that the proposed model and algorithm can effectively capture the dynamics of …
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 …
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.
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Electrical and Computer Engineering Faculty Research & Creative Works
Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes 1-22 and the X chromosome for females. Many genetic abnormalities are directly linked to structural and/or numerical aberrations of chromosomes within metaphase spreads. Cells with the Philadelphia chromosome contain an abnormal chromosome for class 9 and for class 22, leaving a single normal chromosome for each class. A data-driven homologue matching technique is applied to recognizing normal chromosomes from classes 9 and 22. Homologue …
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Electrical and Computer Engineering Faculty Research & Creative Works
Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the …