Machine learning and artificial intelligence (ML/AI) have become important research tools in molecular medicine and chemistry. Their rise and recent success in drug discovery promises a rapid progression of development pipelines while reshaping how f...
Journal of biopharmaceutical statistics
Jul 25, 2020
Perception of treatment effect (TE) in cosmetics is multifaceted and influenced by multiple parameters that need to be considered simultaneously when evaluating TE. Here we provide a global approach to predicting TE perception using Random Forest (RF...
Continuum robot arms, with their hyper-redundant continuously deformable bodies, show great promise in applications deemed impossible for traditional rigid robot arms with discrete links and joints, such as navigating tight corners without getting st...
Increasing automation calls for evaluating the effectiveness and intelligence of automated vehicles. This paper proposes a framework for quantitatively evaluating the intelligence of automated vehicles. Firstly, we establish the evaluation environmen...
With the tremendous development of artificial intelligence, many machine learning algorithms have been applied to the diagnosis of human cancers. Recently, rather than predicting categorical variables (e.g., stages and subtypes) as in cancer diagnosi...
INTRODUCTION: Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.
The advantages of automatically recognition of fundamental tissues using computer vision techniques are well known, but one of its main limitations is that sometimes it is not possible to classify correctly an image because the visual information is ...
BACKGROUND: Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statist...
Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial seman...
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