Despite recent efforts to improve the scoring performance of scoring functions, accurately predicting the binding affinity is still a challenging task. Therefore, different approaches were tried to improve the prediction performance of four scoring functions (x-score, vina, autodock, and rf-score) by substituting the linear regression model of classical scoring function by random forest to examine the performance improvement if an additive functional form is not imposed, and by combining different scoring functions into hybrid ones. The datasets were derived from the PDBbind-CN database version 2016. When evaluating the original scoring functions on the generic dataset, rf-score has outperformed classical scoring functions, which shows the superiority of descriptor-based scoring functions. Substituting linear regression as a linear model by random forest as a nonlinear model had largely improved the scoring performance of autodock and vina while x-score had only a slight performance increase. All hybrid scoring functions had only a slight improvement-if any-on both of the combined scoring functions, which is not worth the slower calculation time.

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Intrusion detection systems (IDS) are important to protect our systems and networks from attacks and malicious behaviors. In this paper, we propose a new hybrid intrusion detection system by using accelerated genetic algorithm and rough set theory (AGAAR) for data feature reduction, and genetic programming with local search (GPLS) for data classification. The AGAAR method is used to select the most relevant attributes that can represent an intrusion detection dataset. In order to improve the performance of GPLS classifier, a new local search strategy is used with genetic programming operators. The main target of using local search strategy is to discover the better solution from the current. The results shown later indicate that classification accuracy improved from 75.98% to 81.44% after using AGAAR attribute reduction for the NSL-KDD dataset. The classification accuracies have been compared with others algorithms and shown that the proposed method can be one of the competitive classifiers for IDS.

Neurexin1 (NRXN1) gene is playing an important role in synaptic formation, plasticity and maturity. Studies have reported non-synonymous SNPs in NRXN1 in patient with mental disorders. The current work is applying computational tools on recoded NRXN1 SNPs in mental disorder patients. The aim of the work is to identify deleterious SNPs, determine damaged protein features (function, stability) and recognise potential protein regions for future research. The effect on protein function is predicted by PROVEAN, SIFT and PolyPhen-2 while protein stability is predicted by MUpro and I-Mutant2.0. Prediction results have identified 2 SNPs to be deleterious by all tools. Higher deleterious results in the stability tools with the percentages of 72%, 78% than the function tools with 25%, 41% and 47%. Agreement percentage of deleterious prediction between stability tools was 56% while 12.5% in the function tools. The identified regions of NRXN1 for future research are SP and LNS4.