@article{Hakim201716,
title = {Neuron-specific splicing},
author = {N H A Hakim and B Y Majlis and H Suzuki and T Tsukahara},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014435502&doi=10.5582%2fbst.2016.01169&partnerID=40&md5=8a5044dbf3b905fc2553520a048bcd59},
doi = {10.5582/bst.2016.01169},
issn = {18817815},
year = {2017},
date = {2017-01-01},
journal = {BioScience Trends},
volume = {11},
number = {1},
pages = {16-22},
publisher = {International Advancement Center for Medicine and Health Research Co., Ltd.},
abstract = {During pre-mRNA splicing events, introns are removed from the pre-mRNA, and the remaining exons are connected together to form a single continuous molecule. Alternative splicing is a common mechanism for the regulation of gene expression in eukaryotes. More than 90% of human genes are known to undergo alternative splicing. The most common type of alternative splicing is exon skipping, which is also known as cassette exon. Other known alternative splicing events include alternative 5' splice sites, alternative 3' splice sites, intron retention, and mutually exclusive exons. Alternative splicing events are controlled by regulatory proteins responsible for both positive and negative regulation. In this review, we focus on neuronal splicing regulators and discuss several notable regulators in depth. In addition, we have also included an example of splicing regulation mediated by the RBFox protein family. Lastly, as previous studies have shown that a number of splicing factors are associated with neuronal diseases such as Alzheime's disease (AD) and Autism spectrum disorder (ASD), here we consider their importance in neuronal diseases wherein the underlying mechanisms have yet to be elucidated.},
note = {cited By 0},
keywords = {Alternative RNA Splicing, Alternative Splicing, Animals, Antibody Specificity, Biological, Biological Model, Diseases, Genetics, Human, Metabolism, Models, Nerve Cell, Neurons, Organ Specificity, RNA Splicing},
pubstate = {published},
tppubtype = {article}
}

@article{Bhat2014851,
title = {Automated diagnosis of autism: In search of a mathematical marker},
author = {S Bhat and U R Acharya and H Adeli and G M Bairy and A Adeli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925286949&doi=10.1515%2frevneuro-2014-0036&partnerID=40&md5=04858a5c9860e9027e3113835ca2e11f},
doi = {10.1515/revneuro-2014-0036},
issn = {03341763},
year = {2014},
date = {2014-01-01},
journal = {Reviews in the Neurosciences},
volume = {25},
number = {6},
pages = {851-861},
publisher = {Walter de Gruyter GmbH},
abstract = {Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH.},
note = {cited By 34},
keywords = {Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis},
pubstate = {published},
tppubtype = {article}
}