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Full-Text Articles in Engineering
Physio-Chemical Modifications To Re-Engineer Small Extracellular Vesicles For Targeted Anticancer Therapeutics Delivery And Imaging, Rahmat Asfiya, Lei Xu, Anjugam Paramanantham, Kuanysh Kabytaev, Anna Chernatynskaya, Grace Mccully, Hu Yang, Akhil Srivastava
Physio-Chemical Modifications To Re-Engineer Small Extracellular Vesicles For Targeted Anticancer Therapeutics Delivery And Imaging, Rahmat Asfiya, Lei Xu, Anjugam Paramanantham, Kuanysh Kabytaev, Anna Chernatynskaya, Grace Mccully, Hu Yang, Akhil Srivastava
Chemical and Biochemical Engineering Faculty Research & Creative Works
Cancer theranostics developed through nanoengineering applications are essential for targeted oncologic interventions in the new era of personalized and precision medicine. Recently, small extracellular vesicles (sEVs) have emerged as an attractive nanoengineering platform for tumor-directed anticancer therapeutic delivery and imaging of malignant tumors. These natural nanoparticles have multiple advantages over synthetic nanoparticle-based delivery systems, such as intrinsic targeting ability, less immunogenicity, and a prolonged circulation time. Since the inception of sEVs as a viable replacement for liposomes (synthetic nanoparticles) as a drug delivery vehicle, many studies have attempted to further the therapeutic efficacy of sEVs. This article discusses engineering strategies …
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 …
Microwave Reflectometry As A Novel Diagnostic Tool For Detection Of Skin Cancers, Pratik Mehta, Kundan Chand, Deepak Narayanswamy, Daryl G. Beetner, R. Zoughi, William V. Stoecker
Microwave Reflectometry As A Novel Diagnostic Tool For Detection Of Skin Cancers, Pratik Mehta, Kundan Chand, Deepak Narayanswamy, Daryl G. Beetner, R. Zoughi, William V. Stoecker
Electrical and Computer Engineering Faculty Research & Creative Works
More than 1 000 000 people are diagnosed with skin cancer each year in the United States, and more than 10 000 people die from the disease. Methods such as visual inspection and dermoscopy are available for early detection of skin cancers, but improvement in accuracy is needed. This paper investigates the use of microwave reflectometry as a potential diagnostic tool for detection of skin cancers. Open-ended coaxial probes were used to measure microwave properties of skin. The influences of measurement parameters such as probe application pressure, power level, and variation in reflection properties of skin with location and hydration …
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 …
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 …
Detection Of Basal Cell Carcinoma Using Electrical Impedance And Neural Networks, Rohit Dua, Daryl G. Beetner, William V. Stoecker, Donald C. Wunsch
Detection Of Basal Cell Carcinoma Using Electrical Impedance And Neural Networks, Rohit Dua, Daryl G. Beetner, William V. Stoecker, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Variations in electrical impedance over frequency might be used to distinguish basal cell carcinoma (BCC) from benign skin lesions, although the patterns that separate the two are nonobvious. Artificial neural networks (ANNs) may be good pattern classifiers for this application. A preliminary study to show the potential of neural networks to distinguish benign from malignant skin lesions using electrical impedance is presented. Electrical impedance was measured in vivo from 1 kHz to 1 MHz at five virtual depths on 18 BCC and 16 benign or premalignant lesions. A feed-forward neural network was trained using back propagation to classify these lesions. …
Probabilistic Neural Networks For Multi-Class Tissue Discrimination With Gene Expression Data, Rui Xu, Donald C. Wunsch
Probabilistic Neural Networks For Multi-Class Tissue Discrimination With Gene Expression Data, Rui Xu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
With the emergence and rapid advancement of DNA microarray technologies, construction of gene expression profiles for different cancer types has already become a promising means for cancer diagnosis and treatment. Most previous research has focused on binary classification. Here, we use a probabilistic neural network (PNN) for multi-classification of cancer data. The experimental results demonstrate the effectiveness of the PNN in addressing gene expression data.
Tissue Classification Through Analysis Of Gene Expression Data Using A New Family Of Art Architectures, Rui Xu, Georgios C. Anagnostopoulos, Donald C. Wunsch
Tissue Classification Through Analysis Of Gene Expression Data Using A New Family Of Art Architectures, Rui Xu, Georgios C. Anagnostopoulos, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Correct classification is crucial to cancer diagnosis and treatment. We demonstrate that a new family of neural network architectures-Ellipsoid ART and ARTMAP (EA/EAM)-can cluster and classify tissues successfully through analysis of gene expression data generated by DNA microarray experiments