Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
Multiple Sclerosis (MS) is an autoimmune disease that severely impacts the central nervous system. Thanks to the evolutionary genetic information studied and published, researches have started to study MS from a genetic perspective. This paper provides a study of six single-nucleotide polymorphisms (SNPs)-MIR137HG (rs1625579), GAS5 (rs2067079), MIR3142HG (rs57095329), MIR146A (rs2910164), MIR155HG (rs767649) and IRAK1 (rs3027898)-and demonstrates their association with MS. This study was applied over an Egyptian dataset of 38 MS patients and 35 controls. Hence, different models were applied, Dominant, Recessive and Genotypic models along with Fisher's Exact method, Basic case-control analysis and Logistic regression analysis. This paper shows that the SNPs rs1625579, rs57095329, rs767649 and rs3027898 are associated with MS (p value < 0.05) according to all tested models except for Recessive model, that has add-in the relevance of rs1625579, rs57095329, rs2910164 and rs767649 with MS disease.
Alzheimer’s disease (AD) is an irreversible, progressive disorder that assaults the nerve cells of the brain. It is the most widely recognized kind of dementia among older adults. Apolipoprotein E (APOE), is one of the most common genetic risk factors for AD whose significant association with AD is observed in various genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation among individuals. SNPs related to many common diseases like AD. SNPs are recognized as significant biomarkers for this disease, they help in understanding and detecting the disease in its early stages. Detecting SNPs biomarkers associated to the disease with high classification accuracy leads to early prediction and diagnosis. Machine learning techniques are utilized to discover new biomarkers of the disease. Sequential minimal optimization (SMO) algorithm with different kernels, Naive Bayes (NB), tree augmented Naive Bayes (TAN) and K2 learning algorithm have been applied on all genetic data of Alzheimer’s disease neuroimaging initiative phase 1 (ADNI-1)/Whole genome sequencing (WGS) datasets. The highest classification accuracy was achieved using 500 SNPs based on the [Formula: see text]-value threshold ([Formula: see text]-value [Formula: see text]). In whole genome approach ADNI-1, results revealed that NB and K2 learning algorithms scored an overall accuracy of 98% and 98.40%, respectively. In whole genome approach WGS, NB and K2 learning algorithms scored an overall accuracy of 99.63% and 99.75%, respectively.
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