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About

Watshara Shoombuatong received his PhD in Computer science from Chiang Mai University. He was a postdoctoral researcher at the Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Thailand. His current research focuses on the development of computational intelligent models in order to apply quantitative structure–property relationship (QSAR) and proteochemometric (PCM) models.

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Skills

Experience

Associate Professor

Mahidol University (MU)

Nov-2022 to Present

Publication

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Journal : EXCLI Journal

Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, t...

  • dott image January, 2023

PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning

Despite the arsenal of existing cancer therapies, the ongoing recurrence and new cases of cancer pose a serious health concern that necessitates the development of new and effective treatmen...

  • dott image December, 2022

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease rec...

  • dott image November, 2022

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides

Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanis...

  • dott image November, 2022

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides

The blood-brain barrier (BBB) is the primary barrier with a highly selective semipermeable border between blood vascular endothelial cells and the central nervous system. Since BBB can preve...

  • dott image November, 2022

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides

Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanis...

  • dott image October, 2022

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides

The blood-brain barrier (BBB) is the primary barrier with a highly selective semipermeable border between blood vascular endothelial cells and the central nervous system. Since BBB can preve...

  • dott image September, 2022

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

Progesterone receptors (PRs) are implicated in various cancers since their presence/absence can determine clinical outcomes. The overstimulation of progesterone can facilitate oncogenesis an...

  • dott image September, 2022

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-co...

  • dott image September, 2022

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobi...