Epitope and T-cell Reactivity Prediction Using Machine Learning Approaches

13301甲第3953号博士（工学）金沢大学博士論文要旨Abstrac

Epitope and T-cell Reactivity Prediction Using Machine Learning Approaches

13301甲第3953号博士（工学）金沢大学博士論文本文Ful

A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions

Definitions of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) in this study. (DOCX 18 kb

Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission

This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place

Additional file 11: Figure S1. of A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions

The number of sequences categorized by their source organisms. (PDF 837 kb