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Publication Metadata only Quantitative structure-property relationship study of spectral properties of green fluorescent protein with support vector machine(2013-01-05) Chanin Nantasenamat; Kakanand Srungboonmee; Saksiri Jamsak; Natta Tansila; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul; Mahidol University; Prince of Songkla University. Such descriptors were mapped onto a higher dimensional space via kernel functions (e.g. linear, polynomial and radial basis function kernels) and learning is then performed using SVM. The predicted spectral properties were well correlated with their experimentalPublication Metadata only Data mining of magnetocardiograms for prediction of ischemic heart disease(2010-12-01) Yosawin Kangwanariyakul; Chanin Nantasenamat; Tanawut Tantimongcolwat; Thanakorn Naenna; Mahidol University, for BPNN and 0.0470 and 0.8495, respectively, for BNN. This indicated that BPNN was the best classification model, BNN was the best performing model with sensitivity of 96.65%, and SVM employing the radial basis function kernel displayed the highestPublication Open Access Classification of P-glycoprotein-interacting compounds using machine-learning methods(2015-07) Watshara Shoombuatong; Apilak Worachartcheewan; Veda Prachayasittikul; Chanin Nantasenamat; Virapong Prachayasittikul; Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical Informaticsfunction of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable modelsPublication Open Access Predicting the Oligomeric States of Fluorescent Proteins(2015) Saw Simeon; Watshara Shoombuatong; Likit Preeyanon; Virapong Prachayasittikul; Chanin Nantasenamat; Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical Informatics; Mahidol University. Faculty of Medical Technology. Department of Clinical Microbiology and Applied TechnologyCurrently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering to create monomeric FPs by saving time and money. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from their amino acid sequences. An exhaustive dataset consisting of 397 unique FP oligomeric states was compiled from the literature. FP were described by 3 classes of protein descriptors including amino acid composition, dipeptide composition and physicochemical properties. The oligomeric states of FP was predicted using decision tree (DT) algorithm and results demonstrated that DT provided robust performance with accuracies in ranges of 79.97-81.72% and 80.76-82.63% for the internal (e.g. 10-fold cross-validation) and external sets, respectively. This approach was also benchmarked with other common machine learning algorithms such as artificial neural network, support vector machine and random forest. A thorough analysis of amino acid sequence features was conducted to provide informative insights into FP oligomerization, which may aid in engineering novel monomeric fluorescent proteins. The following differentiating characteristics of monomeric and oligomeric fluorescent proteins were derived from DT: (i) substitution of any amino acid to Glu led to the reduction of aggregated proteins and (ii) oligomerization of FP appears to be stabilized by several hydrophobic contacts.Publication Open Access A unified proteochemometric model for prediction of inhibition of cytochrome P450 isoforms(2013-06) Maris Lapins; Apilak Worachartcheewan; Ola Spjuth; Valentin Georgiev; Virapong Prachayasittikul; Chanin Nantasenamat; Jarl E. S. Wikberg; Center of Data Mining and Biomedical Informatics; Department of Clinical Microbiology and Applied TechnologyA unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.Publication Open Access Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification(2015-10-05) Veda Prachayasittikul; Ratchanok Pingaew; Nuttapat Anuwongcharoen; Apilak Worachartcheewan; Chanin Nantasenamat; Supaluk Prachayasittikul; Somsak Ruchirawat; Virapong Prachayasittikul; Mahidol University. Faculty of Medical Technology. Department of Clinical Microbiology and Applied Technology; Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical InformaticsConsiderable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quantitative structure–activity relationships (QSAR) is well-known as a powerful computational tool to facilitate the discovery of potential compounds. In this study, a series of thirty-two 1,2,3-triazole derivatives (1–32) together with their experimentally measured cytotoxic activities against four cancer cell lines i.e., HuCCA-1, HepG2, A549 and MOLT-3 were used for QSAR analysis. Four QSAR models were successfully constructed with acceptable predictive performance affording RCV ranging from 0.5958 to 0.8957 and RMSE CV ranging from 0.2070 to 0.4526. An additional set of 64 structurally modified triazole compounds (1A–1R, 2A–2R, 7A–7R and 8A–8R) were constructed in silico and their predicted cytotoxic activities were obtained using the constructed QSAR models. The study suggested crucial moieties and certain properties essential for potent anticancer activity and highlighted a series of promising compounds (21, 28, 32, 1P, 8G, 8N and 8Q) for further development as novel triazole-based anticancer agents.