Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making
and revolutionizing the field of health care. A major open challenge that AI will need to …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Type 1 conventional dendritic cells (cDC1) can support T cell responses within tumors but
whether this determines protective versus ineffective anti-cancer immunity is poorly …

Complex biomedical data generated during clinical, omics and mechanism-based
experiments have increasingly been exploited through cloud-and visualization-based data …

Analysis of big data by machine learning offers considerable advantages for assimilation
and evaluation of large amounts of complex health-care data. However, to effectively use …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …

The optimization of therapeutic antibodies is time-intensive and resource-demanding,
largely because of the low-throughput screening of full-length antibodies (approximately 1× …