iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions.

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.

A structural equation modeling approach for the adoption of cloud computing to enhance the Malaysian healthcare sector.

The investments and costs of infrastructure, communication, medical-related equipments, and software within the global healthcare ecosystem portray a rather significant increase. The emergence of this proliferation is then expected to grow. As a result, information and cross-system communication became challenging due to the detached independent systems and subsystems which are not connected. The overall model fit expending over a sample size of 320 were tested with structural equation modelling (SEM) using AMOS 20.0 as the modelling tool. SPSS 20.0 is used to analyse the descriptive statistics and dimension reliability. Results of the study show that system utilisation and system impact dimension influences the overall level of services of the healthcare providers. In addition to that, the findings also suggest that systems integration and security plays a pivotal role for IT resources in healthcare organisations. Through this study, a basis for investigation on the need to improvise the Malaysian healthcare ecosystem and the introduction of a cloud computing platform to host the national healthcare information exchange has been successfully established.