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You searched for +publisher:"Virginia Commonwealth University" +contributor:("Dr. Bridget McInnes"). Showing records 1 – 3 of 3 total matches.

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Virginia Commonwealth University

1. Carey, Howard J, III. EEG Interictal Spike Detection Using Artificial Neural Networks.

Degree: MS, Computer Science, 2016, Virginia Commonwealth University

Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved. Advisors/Committee Members: Dr. Milos Manic, Dr. Bridget McInnes, Dr. Ken Ono.

Subjects/Keywords: Artificial neural networks; machine learning; EEG; interictal spikes; epilepsy; Artificial Intelligence and Robotics; Neurology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Carey, Howard J, I. (2016). EEG Interictal Spike Detection Using Artificial Neural Networks. (Thesis). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/Y0F6-FF86 ; https://scholarscompass.vcu.edu/etd/4648

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Carey, Howard J, III. “EEG Interictal Spike Detection Using Artificial Neural Networks.” 2016. Thesis, Virginia Commonwealth University. Accessed August 13, 2020. https://doi.org/10.25772/Y0F6-FF86 ; https://scholarscompass.vcu.edu/etd/4648.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Carey, Howard J, III. “EEG Interictal Spike Detection Using Artificial Neural Networks.” 2016. Web. 13 Aug 2020.

Vancouver:

Carey, Howard J I. EEG Interictal Spike Detection Using Artificial Neural Networks. [Internet] [Thesis]. Virginia Commonwealth University; 2016. [cited 2020 Aug 13]. Available from: https://doi.org/10.25772/Y0F6-FF86 ; https://scholarscompass.vcu.edu/etd/4648.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Carey, Howard J I. EEG Interictal Spike Detection Using Artificial Neural Networks. [Thesis]. Virginia Commonwealth University; 2016. Available from: https://doi.org/10.25772/Y0F6-FF86 ; https://scholarscompass.vcu.edu/etd/4648

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Commonwealth University

2. Henry, Sam. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery.

Degree: PhD, Computer Science, 2019, Virginia Commonwealth University

The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems. Advisors/Committee Members: Dr. Bridget McInnes, Dr. Alberto Cano, Dr. Dayanjan S. Wijesinghe, Dr. Halil Kilicoglu, Dr. Than Dinh.

Subjects/Keywords: Literature Based Discovery; Semantic Association; Semantic Relatedness; Natural Language Processing; Data Mining; Text Processing; Text Mining; Other Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Henry, S. (2019). Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855

Chicago Manual of Style (16th Edition):

Henry, Sam. “Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery.” 2019. Doctoral Dissertation, Virginia Commonwealth University. Accessed August 13, 2020. https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855.

MLA Handbook (7th Edition):

Henry, Sam. “Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery.” 2019. Web. 13 Aug 2020.

Vancouver:

Henry S. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2019. [cited 2020 Aug 13]. Available from: https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855.

Council of Science Editors:

Henry S. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. [Doctoral Dissertation]. Virginia Commonwealth University; 2019. Available from: https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855


Virginia Commonwealth University

3. Rana, Pratip. Mathematical models of cellular signaling and supramolecular self-assembly.

Degree: PhD, Computer Science, 2020, Virginia Commonwealth University

<p id="x-docs-internal-guid-35cca163-7fff-34ea-64a1-9ed834579b09">Synthetic biologists endeavor to predict how the increasing complexity of multi-step signaling cascades impacts the fidelity of molecular signaling, whereby cellular state information is often transmitted with proteins diffusing by a pseudo-one-dimensional stochastic process. We address this problem by using a one-dimensional drift-diffusion model to derive an approximate lower bound on the degree of facilitation needed to achieve single-bit informational efficiency in signaling cascades as a function of their length. We find that a universal curve of the Shannon-Hartley form describes the information transmitted by a signaling chain of arbitrary length and depends upon only a small number of physically measurable parameters. This enables our model to be used in conjunction with experimental measurements to aid in the selective design of biomolecular systems. Another important concept in the cellular world is molecular self-assembly. As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the classical thermodynamic and kinetics-based models. The models we propose use state probabilities as its fundamental objects and directly model the transition probabilities between the initial and final states of a trajectory. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We also investigated a larger complex self-assembly system, the heterotypic interactions between amyloid-beta and fatty acids by an independent ensemble kinetic simulation using an underlying differential equation-based system which was validated by biophysical experiments. Advisors/Committee Members: Dr. Preetam Ghosh, Dr. Bridget McInnes, Dr. Thang Dinh, Dr. Michael Mayo, Dr. Kevin Pilkiewicz.

Subjects/Keywords: Molecular Communication; Self Assembly; Differential Equation Model; Alzheimer; Information Theory; Molecular Signaling; Computational Biology; Systems and Communications

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rana, P. (2020). Mathematical models of cellular signaling and supramolecular self-assembly. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/6253

Chicago Manual of Style (16th Edition):

Rana, Pratip. “Mathematical models of cellular signaling and supramolecular self-assembly.” 2020. Doctoral Dissertation, Virginia Commonwealth University. Accessed August 13, 2020. https://scholarscompass.vcu.edu/etd/6253.

MLA Handbook (7th Edition):

Rana, Pratip. “Mathematical models of cellular signaling and supramolecular self-assembly.” 2020. Web. 13 Aug 2020.

Vancouver:

Rana P. Mathematical models of cellular signaling and supramolecular self-assembly. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2020. [cited 2020 Aug 13]. Available from: https://scholarscompass.vcu.edu/etd/6253.

Council of Science Editors:

Rana P. Mathematical models of cellular signaling and supramolecular self-assembly. [Doctoral Dissertation]. Virginia Commonwealth University; 2020. Available from: https://scholarscompass.vcu.edu/etd/6253

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