Hybrid Workflow Net Based Architecture for Modeling Huntington's Disease
27 Sep 2011-pp 319-323
TL;DR: To enhance the effectiveness of the bio pathway model, the verification process for the model with the help of automata is associated and the performance analysis of the Huntington's disease model is tried by modeling the functioning of the model based on automata.
Abstract: In order to understand and predict the behaviour of any system modeling is essential. Hence the research on modeling and simulation of complex biological systems has drawn the attention of the researchers of this field today. In this respect we have tried the characterization of Huntington's pathways through Hybrid workflow net. To enhance the effectiveness of the bio pathway model we have associated the verification process for the model with the help of automata. We have tried the performance analysis of the Huntington's disease model by modeling the functioning of the model based on automata.
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01 Jan 2016
TL;DR: A new classifier fusion strategy is proposed and presented that combines classification algorithms and rules to measure specific behaviors in people with neurodegenerative diseases to diagnose this fatal disease at an earlier stage.
Abstract: It is generally believed that early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future This chapter presents a strategic framework for the early diagnosis of neurodegenerative disease from gait discrimination to neural synchronization Here, we propose and present a new classifier fusion strategy that combines classification algorithms and rules (voting, product, mean, median, maximum, and minimum) to measure specific behaviors in people with neurodegenerative diseases On the other hand, it is now evident that electroencephalographic (EEG) signals of patients with Alzheimer disease usually have less synchronization than those of healthy subjects Computing neural synchronization of EEG signals to detect any perturbation will help diagnose this fatal disease at an earlier stage Three neural synchrony measurement techniques, phase synchrony, magnitude-squared coherence, and cross-correlation, are applied to analyze three different databases of mild Alzheimer disease patients and healthy subjects to compare the right and left temporal lobe of the brain with the rest of the brain area Results are compared using Mann–Whitney U statistical test
8 citations
01 Feb 2015
TL;DR: A new classifier fusion strategy that combines classification algorithms and rules voting, product, mean, median, maximum and minimum to measure specific behaviours in people suffering with neurodegenerative diseases is proposed.
Abstract: People in developed countries are living longer, and this has resulted in the prevalence of age-related diseases like Alzheimer's and dementia. Many believe that the early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future. This paper considers this idea and proposes a new classifier fusion strategy that combines classification algorithms and rules voting, product, mean, median, maximum and minimum to measure specific behaviours in people suffering with neurodegenerative diseases. More specifically, the fusion strategy analyses the stride-to-stride intervals in gait and its correlation with neurological functions. This approach is compared with base level classifiers a single classification algorithm using a set of feature vectors associated with gait patterns obtained from neurodegenerative patients and healthy people. The results show that the fusion strategy improves classification. Our experiments successfully show that a fusion strategy generates better results and classifies subjects more accurately than base level classifiers.
7 citations
Cites background from "Hybrid Workflow Net Based Architect..."
...When the PolyQ region generates more sections of glutamine, a mutant Huntington protein is produced, which is the actual cause of Huntington’s disease (Neela and Rangarajan, 2011)....
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01 Jan 2014
TL;DR: This research work aimed at analysing the gait signals as well as EEG signals, separately, for early detection/diagnosis of Alzheimer’s disease, and proposed and developed a new classifier fusion strategy that generates better results and classifies subjects more accurately than base-level classifiers.
Abstract: Given the fact that people, especially in advanced countries, are living longer due to the advancements in medical sciences which resulted in the prevalence of age-related diseases like Alzheimer’s and dementia. The occurrence of such diseases continues to increase and ultimately the cost of caring for these groups will become unsustainable. Addressing this issue has reached a critical point and failing to provide a strategic way forward will negatively affect patients, national health services and society as a whole.Three distinctive development stages of neurodegenerative diseases (Retrogenesis, Cognitive Impairment and Gait Impairment) motivated me to divide this research work into two main parts. To fully achieve the purpose of early detection/diagnosis, I aimed at analysing the gait signals as well as EEG signals, separately, as both of these signals severely get affected by any neurological disease.The first part of this research work focuses on the discrimination analysis of gait signals of different neurodegenerative diseases (Parkinson’s, Huntington, and Amyotrophic Lateral Sclerosis) and also of control subjects. This involves relevant feature extraction, solving the issues of imbalanced datasets and missing entries and lastly classification of multiclass datasets. For the classification and discrimination of gait signals, eleven (11) classifiers are selected representing linear, non-linear and Bayes normal classification techniques. Results revealed that three classifiers have provided us with higher accuracy rate which are UDC, LDC and PARZEN with 65%, 62.5% and 60% accuracy, respectively. Further, I proposed and developed a new classifier fusion strategy that combined classification algorithms with combining rules (voting, product, mean, median, maximum and minimum). It generates better results and classifies subjects more accurately than base-level classifiers.The last part of this research work is based on the rectification and computation of EEG signals of mild Alzheimer’s disease patients and control subjects. To detect the perturbation in EEG signals of Alzheimer’s patients, three neural synchrony measurement techniques; phase synchrony, magnitude squared coherence and cross correlation are applied on three different databases of mild Alzheimer’s disease (MiAD) patients and healthy subjects. I have compared right and left temporal parts of brain with rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s. Two novel methods are proposed to compute the neural synchronization of the brain; Average synchrony measure and PCA based synchrony measure. These techniques are evaluated for three different datasets of MiAD patients and control subjects using the Wilcoxon ranksum test (Mann-Whitney U test). Results demonstrated that PCA based method helped us to find more significant features that can be used as biomarkers for the early diagnosis of Alzheimer’s.
2 citations
References
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01 Apr 1989
TL;DR: The author proceeds with introductory modeling examples, behavioral and structural properties, three methods of analysis, subclasses of Petri nets and their analysis, and one section is devoted to marked graphs, the concurrent system model most amenable to analysis.
Abstract: Starts with a brief review of the history and the application areas considered in the literature. The author then proceeds with introductory modeling examples, behavioral and structural properties, three methods of analysis, subclasses of Petri nets and their analysis. In particular, one section is devoted to marked graphs, the concurrent system model most amenable to analysis. Introductory discussions on stochastic nets with their application to performance modeling, and on high-level nets with their application to logic programming, are provided. Also included are recent results on reachability criteria. Suggestions are provided for further reading on many subject areas of Petri nets. >
10,755 citations
"Hybrid Workflow Net Based Architect..." refers background in this paper
...INTRODUCTION Petri nets are highly useful in the analysis of complex biological systems integrating both qualitative and quantitative studies [4]....
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TL;DR: Alur et al. as discussed by the authors proposed timed automata to model the behavior of real-time systems over time, and showed that the universality problem and the language inclusion problem are solvable only for the deterministic automata: both problems are undecidable (II i-hard) in the non-deterministic case and PSPACE-complete in deterministic case.
7,096 citations
01 Jan 1998
870 citations
"Hybrid Workflow Net Based Architect..." refers methods in this paper
...Nagasaki et al. [12], [ 13 ], [14] we have created Hybrid W.F....
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TL;DR: The results suggest that by generation of truncated polyglutamine-containing proteins, caspase cleavage may represent a common step in the pathogenesis of each of these neurodegenerative diseases.
592 citations
"Hybrid Workflow Net Based Architect..." refers background or methods in this paper
...[12], [13], [14] we have created Hybrid W....
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...Caspase-3 cleaves both normal Huntington and diseased Huntington protein [14]....
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TL;DR: Results support the idea that sequential proteolysis by caspase 3 and calpain may regulate huntingtin function at membranes and produce N-terminal mutant fragments that aggregate and cause cellular dysfunction in HD.
Abstract: The Huntington's disease (HD) mutation is a polyglutamine expansion in the N-terminal region of huntingtin (N-htt). How neurons die in HD is unclear. Mutant N-htt aggregates in neurons in the HD brain; expression of mutant N-htt in vitro causes cell death. Other in vitro studies show that proteolysis by caspase 3 could be important in regulating mutant N-htt function, but there has been no direct evidence for caspase 3-cleaved N-htt fragments in brain. Here, we show that N-htt fragments consistent with the size produced by caspase 3 cleavage in vitro are resident in the cortex, striatum, and cerebellum of normal and adult onset HD brain and are similar in size to the fragments seen after exogenous expression of human huntingtin in mouse clonal striatal neurons. HD brain extracts treated with active caspase 3 had increased levels of N-htt fragments. Compared with the full-length huntingtin, the caspase 3-cleaved N-htt fragments, especially the mutant fragment, preferentially segregated with the membrane fraction. Partial proteolysis of the human caspase 3-cleaved N-htt fragment by calpain occurred in vitro and resulted in smaller N-terminal products; products of similar size appeared when mouse brain protein extracts were treated with calpain. Results support the idea that sequential proteolysis by caspase 3 and calpain may regulate huntingtin function at membranes and produce N-terminal mutant fragments that aggregate and cause cellular dysfunction in HD.
405 citations
"Hybrid Workflow Net Based Architect..." refers background in this paper
...This type of protein is named as mutant Huntington protein [10] which causes Huntington disease....
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