INTRODUCTION: Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decisi...
International journal of biological macromolecules
Jun 8, 2023
Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Un...
The increasing penetration of renewable energy sources tends to redirect the power systems community's interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons...
Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined wit...
Chemical communications (Cambridge, England)
Jun 8, 2023
ITIC-series nonfullerene organic photovoltaics (NF OPVs) have realized the simultaneous increases of the short-circuit current density () and open-circuit voltage (), called the positive correlation between and , which could improve the power conver...
Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a represen...
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical...
The increasing scientific attention given to precision medicine based on real-world data has led to many recent studies clarifying the relationships between treatment effects and patient characteristics. However, this is challenging because of ubiqui...
Several binary molecular fingerprints were compressed using an autoencoder neural network. We analyzed the impact of compression on fingerprint performance in downstream classification and regression tasks. Classifiers trained on compressed fingerpri...
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