Parkinson's disease (PD), a neurodegenerative disorder, affects millions of people and has gained attention because of its clinical roles affecting behaviors related to motor and nonmotor symptoms. Although studies on PD from various aspects are becoming popular, few rely on predictive systems modeling approaches. Using Biochemical Systems Theory (BST), this article attempts to model and characterize dopaminergic cell death and understand pathophysiology of progression of PD. PD pathways were modeled using stochastic differential equations incorporating law of mass action, and initial concentrations for the modeled proteins were obtained from literature. Simulations suggest that dopamine levels were reduced significantly due to an increase in dopaminergic quinones and 3,4-dihydroxyphenylacetaldehyde (DOPAL) relating to imbalances compared to control during PD progression. Associating to clinically observed PD-related cell death, simulations show abnormal parkin and reactive oxygen species levels with an increase in neurofibrillary tangles. While relating molecular mechanistic roles, the BST modeling helps predicting dopaminergic cell death processes involved in the progression of PD and provides a predictive understanding of neuronal dysfunction for translational neuroscience.

NeuroinformaticsNatural LanguageProcessing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques. Although developments of automated keyword extraction methods have made differences in the quality of data selection, the efficacy of the Natural Language Processing (NLP) methods using verified keywords remain a challenge. In this paper, we studied the role of text classification and document clustering algorithms on datasets, where features were obtained by mapping to manually verified MESH terms published by National Library of Medicine (NLM). In this study, NLP data classification involved comparing Stechniques and unsupervised learning was performed with 6 clustering algorithms. Most classification techniques except meta-based algorithms namely stacking and vote, allowed 90% or higher training accuracy. Test accuracy was high (=>95%) probably due to limited test dataset. Logistic Model Trees had 30-fold higher runtime compared to other classification algorithms including Naive Bayes, AdaBoost, Hoeffding Tree. Grouped error rate in clustering was 0-4%. Runtime-wise, clustering was faster than classification algorithms on MESH-mapped NLP data suggesting clustering methods as adequate towards Medline-related datasets and text-mining big data analytic systems.

<strong>With the rapid growth in the numbers of scientific publications in domains such as neuroscience and medicine, visually interlinking documents in online databases such as PubMed with the purpose of indicating the context of a query results can improve the multi-disciplinary relevance of the search results. Translational medicine and systems biology rely on studies relating basic sciences to applications, often going through multiple disciplinary domains. This paper focuses on the design and development of a new scientific document visualization platform, which allows inferring translational aspects in biosciences within published articles using machine learning and natural language processing (NLP) methods. From online databases, this software platform effectively extracted relationship connections between multiple sub-domains within neuroscience derived from abstracts related to user query. In our current implementation, the document visualization platform employs two clustering algorithms namely Suffix Tree Clustering (STC) and LINGO. Clustering quality was improved by mapping top-ranked cluster labels derived from an UMLS-Metathesaurus using a scoring function. To avoid non-clustered documents, an iterative scheme, called auto-clustering was developed and this allowed mapping previously uncategorized documents during the initial grouping process to relevant clusters. The efficacy of this document clustering and visualization platform was evaluated by expert-based validation of clustering results obtained with unique search terms.  Compared to normal clustering, auto-clustering demonstrated better efficacy by generating larger numbers of unique and relevant cluster labels. Using this implementation, a Parkinson’s disease systems theory model was developed and studies based on user queries related to neuroscience and oncology have been showcased as applications.</strong>

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Understanding functional role of spike bursts in the brain circuits is vital in analyzing coding of sensory information. Information coding in neurons or brain cells happen as spikes or action potentials and excitatory post-synaptic potentials (EPSPs). Information transmission at the Mossy fiber-Granule cell synaptic relay is crucial to understand mechanisms of signal coding in the cerebellum. We analyzed spiking in granule cells via a detailed computational model and computed the spiking-potentiation contributing to signal recoding in granular layer. Plasticity is simulated in the granule cell model by changing the intrinsic excitability and release probability of the cells.Excitatory post synaptic potentials and spikes on varying Golgi cell (GoC) inhibition and Mossy fiber(MF) excitation were analyzed simultaneously with the effect of induced plasticity changes based on the timing and amplitude of the postsynaptic signals. It is found that a set of EPSPs reaching maximum threshold amplitude are converted to less number of high amplitude EPSPs or spikes. Exploring the EPSP-spike complex in granular neurons reveal possible mechanisms and quantification of information encoding in individual neurons of the cerebellar granular layer. Therefore, our study is potentially an important estimation of cerebellar function.