Measuring Health-Related Quality of Life
Clinicians and policymakers are recognizing the importance of measuring health-related quality of life (HRQL) to inform patient management and policy decisions. Self- or intervi...
Explore 739 academic publications
Clinicians and policymakers are recognizing the importance of measuring health-related quality of life (HRQL) to inform patient management and policy decisions. Self- or intervi...
The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Scie...
Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variabl...
A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep ...
Abstract Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. ...
Overwhelming evidence now indicates that the quality of reporting of randomized, controlled trials (RCTs) is less than optimal. Recent methodologic analyses indicate that inadeq...
These recommendations are based on the following: (1) a formal review and analysis of the recently published world literature on the topic [Medline search up to June 2011]; (2) ...
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (...
These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early an...
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a ...
Bounding box regression is the crucial step in object detection. In existing methods, while ℓn-norm loss is widely adopted for bounding box regression, it is not tailored to the...
Abstract Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly,...