Validation of biomarkers of aging
The search for biomarkers that quantify biological aging (particularly 'omic'-based
biomarkers) has intensified in recent years. Such biomarkers could predict aging-related …
biomarkers) has intensified in recent years. Such biomarkers could predict aging-related …
Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion
The ideal conditions for anaerobic digestion experiments with biochar addition are
challenging to thoroughly study due to different experimental purposes. Therefore, three tree …
challenging to thoroughly study due to different experimental purposes. Therefore, three tree …
A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease
SA Ebiaredoh-Mienye, TG Swart, E Esenogho… - Bioengineering, 2022 - mdpi.com
The high prevalence of chronic kidney disease (CKD) is a significant public health concern
globally. The condition has a high mortality rate, especially in developing countries. CKD …
globally. The condition has a high mortality rate, especially in developing countries. CKD …
[HTML][HTML] Air Quality Index prediction using machine learning for Ahmedabad city
NN Maltare, S Vahora - Digital Chemical Engineering, 2023 - Elsevier
Prediction of air pollution index may help in traffic routing and identifying serious pollutants.
Modeling of the complex relationships between these variables by sophisticated methods in …
Modeling of the complex relationships between these variables by sophisticated methods in …
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks. When data
are 'missing at random'there exist a range of tools and techniques to deal with the issue …
are 'missing at random'there exist a range of tools and techniques to deal with the issue …
Applications of artificial intelligence and machine learning in heart failure
T Averbuch, K Sullivan, A Sauer… - … Heart Journal-Digital …, 2022 - academic.oup.com
Abstract Machine learning (ML) is a sub-field of artificial intelligence that uses computer
algorithms to extract patterns from raw data, acquire knowledge without human input, and …
algorithms to extract patterns from raw data, acquire knowledge without human input, and …
Machine learning: its challenges and opportunities in plant system biology
Sequencing technologies are evolving at a rapid pace, enabling the generation of massive
amounts of data in multiple dimensions (eg, genomics, epigenomics, transcriptomic …
amounts of data in multiple dimensions (eg, genomics, epigenomics, transcriptomic …
Blackout missing data recovery in industrial time series based on masked-former hierarchical imputation framework
In industrial processes, frequent communication failures and information corruption may
result in the loss of entire blocks of industrial process data, which is also known as blackout …
result in the loss of entire blocks of industrial process data, which is also known as blackout …
Understanding and mitigating bias in imaging artificial intelligence
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model
development, with potential for exacerbating health disparities. However, bias in imaging AI …
development, with potential for exacerbating health disparities. However, bias in imaging AI …