Nehal M. Ali

TL;DR: The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks and outperformed SVM, Na・・ve Bayes and RNTN that were published in other works using English datasets.

TL;DR: In this article, the authors presented a complete predictive model by combining consecutive transcriptomic data preprocessing procedures, followed by the proposed KmerFIDF method as a feature extraction method and linear discriminant analysis for dimensionality reduction.

TL;DR: It is shown 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 rs16 25579, pgAS5, MIR3142HG, mIR155HG andrs767649 with MS disease.

TL;DR: Two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis via transcriptomic fragmentation and LSTM deep learning are introduced.

TL;DR: In this article, a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning network results for IMDB dataset consists of 50k movie reviews files, which was initially pre-processed using Word2Vec and word embedding was applied accordingly.