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Results: 25
Multi-task learning with a natural metric for quantitative structure activity relationship learning.
Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N van Rijn, Jeremy Besnard, Richard Bickerton, Crina Grosan, Larisa Soldatova, Ross D King
Jul 07, 2020
The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed...
PREDICTING ABSENTEEISM OF FEMALE STUDENTS IN ALABAMA
Funmilola Okelana
Jan 01, 0001
Abstract
Students are chronically absent when they miss at least 15 days of the school year. Past researchers have identified income and environment as factors that affect school absenteeism. Alabama is a poor state with a high...
Published by: Harrisburg University of Science and Technology
Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine-Learning Approach
This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the...
Random Forest Smart demand metering water demand forecasting /dk/atira/pure/subjectarea/asjc/2200/2205 name=Civil and Structural Engineering /dk/atira/pure/subjectarea/asjc/3300/3305 name=Geography, Planning and Development /dk/atira/pure/subjectarea/asjc/2300/2312 name=Water Science and Technology /dk/atira/pure/subjectarea/asjc/2300/2308 name=Management, Monitoring, Policy and Law
Optimal Bayesian design for model discrimination via classification.
Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded...
Predicting factors for survival of breast cancer patients using machine learning techniques.
Mogana Darshini Ganggayah, Nur Aishah Taib, Yip Cheng Har, Pietro Lio, Sarinder Kaur Dhillon
Apr 05, 2019
Identification and characterization of Coronaviridae genomes from Vietnamese bats and rats based on conserved protein domains.
The Coronaviridae family of viruses encompasses a group of pathogens with a zoonotic potential as observed from previous outbreaks of the severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome...
Optimal Bayesian design for model discrimination via classification.
UNLABELLED: Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is...
Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.
Cheng-Yu Tsai, Huei-Tyng Huang, Hsueh-Chien Cheng, Jieni Wang, Ping-Jung Duh, Wen-Hua Hsu, Marc Stettler, Yi-Chun Kuan, Yin-Tzu Lin, Chia-Rung Hsu, Kang-Yun Lee, Jiunn-Horng Kang, Dean Wu, Hsin-Chien Lee, Cheng-Jung Wu, Arnab Majumdar, Wen-Te Liu
Dec 08, 2022
An evaluation of machine-learning for predicting phenotype
Abstract: In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal...
An evaluation of machine-learning for predicting phenotype
Abstract: In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal...
PECLIDES Neuro
Neurodegenerative diseases such as Alzheimer's and Parkinson's impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases' progression. Correct diagnosis often...
Identifying Cancer Drivers Using DRIVE
Ionut Dragomir, Adnan Akbar, John W Cassidy, Nirmesh Patel, Harry W Clifford, Gianmarco Contino
Jun 05, 2021
Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the...
Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine-Learning Approach
This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the...
Random Forest Smart demand metering water demand forecasting /dk/atira/pure/subjectarea/asjc/2200/2205 name=Civil and Structural Engineering /dk/atira/pure/subjectarea/asjc/3300/3305 name=Geography, Planning and Development /dk/atira/pure/subjectarea/asjc/2300/2312 name=Water Science and Technology /dk/atira/pure/subjectarea/asjc/2300/2308 name=Management, Monitoring, Policy and Law
An evaluation of machine-learning for predicting phenotype
In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal...
KekuleScope
The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applications often require...
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data
Chao Wu, Zhen Wang, Simon Hu, Julien Lepine, Xiaoxiang Na, Daniel Ainalis, Marc Stettler
Sep 30, 2020
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented...
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.
Chao Wu, Zhen Wang, Simon Hu, Julien Lepine, Xiaoxiang Na, Daniel Ainalis, Marc Stettler
Dec 14, 2020
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented...
Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning
Lifang Xie, Siheng Luo, Yangyang Liu, Xuejun Ruan, Kedong Gong, Qiuyue Ge, Kejian Li, Ventsislav Kolev Valev, Guokun Liu, Liwu Zhang
Nov 21, 2023
The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics...
Kekulescope
The application of convolutional neural networks (ConvNets) to harness
high-content screening images or 2D compound representations is gaining
increasing attention in drug discovery. However, existing applications often
require...
A comparison of machine learning and Bayesian modelling for molecular serotyping.
BACKGROUND: Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease....
Optimal Bayesian design for model discrimination via classification.
Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded...
Random Forest Bayesian model selection Approximate Bayesian computation Classification and regression tree Continuous-time Markov process Simulation-based Bayesian experimental design
Published by: Statistics and computing
Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.
Cheng-Yu Tsai, Huei-Tyng Huang, Hsueh-Chien Cheng, Jieni Wang, Ping-Jung Duh, Wen-Hua Hsu, Marc Stettler, Yi-Chun Kuan, Yin-Tzu Lin, Chia-Rung Hsu, Kang-Yun Lee, Jiunn-Horng Kang, Dean Wu, Hsin-Chien Lee, Cheng-Jung Wu, Arnab Majumdar, Wen-Te Liu
Jan 04, 2023
Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning...
Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning
Lifang Xie, Siheng Luo, Yangyang Liu, Xuejun Ruan, Kedong Gong, Qiuyue Ge, Kejian Li, Ventsislav Kolev Valev, Guokun Liu, Liwu Zhang
Nov 21, 2023
The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics...