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Zeb, A.,Park, C.,Son, M.,Rampogu, S.,Alam, S. I.,Park, S. J.,Lee, K. W. Imperial College Press 2018 JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOG Vol.16 No.3
<P>Proteins deacetylation by Histone deacetylase 6 (HDAC6) has been shown in various human chronic diseases like neurodegenerative diseases and cancer, and hence is an important therapeutic target. Since, the existing inhibitors have hydroxamate group, and are not HDAC6-selective, therefore, this study has designed to investigate non-hydroxamate HDAC6 inhibitors. Ligand-based pharmacophore was generated from 26 training set compounds of HDAC6 inhibitors. The statistical parameters of pharmacophore (Hypo1) included lowest total cost of 115.63, highest cost difference of 135.00, lowest RMSD of 0.70 and the highest correlation of 0.98. The pharmacophore was validated by Fischer's Randomization and Test Set validation, and used as screening tool for chemical databases. The screened compounds were filtered by fit value (> 10.00), estimated Inhibitory Concentration (IC50) (<0.459), Lipinski's Rule of Five and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Descriptors to identify drug-like compounds. Furthermore, the drug-like compounds were docked into the active site of HDAC6. The best docked compounds were selected having gold fitness score > 66.46 and chemscore < -28.31, and hydrogen bond interaction with catalytic active residues. Finally, three inhibitors having sulfamoyl group were selected by Molecular Dynamic (MD) simulation, which showed stable root mean square deviation (RMSD) (1.6-1.9 angstrom), lowest potential energy (< -6.3 x 10(5) kJ/mol), and hydrogen bonding with catalytic active residues of HDAC6.</P>
BAYESIAN NETWORK LEARNING WITH FEATURE ABSTRACTION FOR GENE-DRUG DEPENDENCY ANALYSIS
CHANG, JEONG-HO,HWANG, KYU-BAEK,JUNE OH, S.,ZHANG, BYOUNG-TAK Imperial College Press 2005 JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOG Vol.3 No.1
<P>Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset (http://discover.nci.nih.gov) consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.</P>
Inference of large-scale gene regulatory networks using regression-based network approach.
Kim, Haseong,Lee, Jae K,Park, Taesung Imperial College Press ; World Scientific Publishi 2009 JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOG Vol.7 No.4
<P>The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crescentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.</P>
Parameter pattern discovery in nonlinear dynamic model for EEGs analysis
Kim, S.-H.,Faloutsos, C.,Yang, H.-J.,Lee, S.-W. Imperial College Press 2016 Journal of integrative neuroscience Vol.15 No.3
<P>We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg-Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model.</P>
An improved method for predicting interactions between virus and human proteins
Kim, Byungmin,Alguwaizani, Saud,Zhou, Xiang,Huang, De-Shuang,Park, Byunkyu,Han, Kyungsook Imperial College Press 2017 JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOG Vol.15 No.1
<P>The interaction of virus proteins with host proteins plays a key role in viral infection and consequent pathogenesis. Many computational methods have been proposed to predict protein-protein interactions (PPIs), but most of the computational methods are intended for PPIs within a species rather than PPIs across different species such as virus-host PPIs. We developed a method that represents key features of virus and human proteins of variable length into a feature vector of fixed length. The key features include the relative frequency of amino acid triplets (RFAT), the frequency difference of amino acid triplets (FDAT) between virus and host proteins, and amino acid composition (AC). We constructed several support vector machine (SVM) models to evaluate our method and to compare our method with others on PPIs between human and two types of viruses: human papillomaviruses (HPV) and hepatitis C virus (HCV). Comparison of our method to others with same datasets of HPV-human PPIs and HCV-human PPIs showed that the performance of our method is significantly higher than others in all performance measures. Using the SVM model with gene ontology (GO) annotations of proteins, we predicted new HPV-human PPIs. We believe our approach will be useful in predicting heterogeneous PPIs.</P>
An improved preprocessing algorithm for haplotype inference by pure parsimony.
Choi, Mun-Ho,Kang, Seung-Ho,Lim, Hyeong-Seok Imperial College Press ; World Scientific Publishi 2014 Journal of Bioinformatics and Computational Biolog Vol.12 No.4
<P>The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform a haplotype-based association test with disease. Given a set of genotypes from a population, the process of recovering the haplotypes, which explain the genotypes, is called haplotype inference (HI). We propose an improved preprocessing method for solving the haplotype inference by pure parsimony (HIPP), which excludes a large amount of redundant haplotypes by detecting some groups of haplotypes that are dispensable for optimal solutions. The method uses only inclusion relations between groups of haplotypes but dramatically reduces the number of candidate haplotypes; therefore, it causes the computational time and memory reduction of real HIPP solvers. The proposed method can be easily coupled with a wide range of optimization methods which consider a set of candidate haplotypes explicitly. For the simulated and well-known benchmark datasets, the experimental results show that our method coupled with a classical exact HIPP solver run much faster than the state-of-the-art solver and can solve a large number of instances that were so far unaffordable in a reasonable time.</P>