Big data in health care is a fast-growing field and a new paradigm that is transforming case-
based studies to large-scale, data-driven research. As big data is dependent on the …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

In recent years, machine learning (ML) has become a key enabling technology for the
sciences and industry. Especially through improvements in methodology, the availability of …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …

Abstract Machine learning is as growing as fast as concepts such as Big data and the field of
data science in general. The purpose of the systematic review was to analyze scholarly …

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 …

Over the past several decades the dramatic increase in the availability of computational
resources, coupled with the maturation of machine learning, has profoundly impacted …

Metal–organic frameworks (MOFs) have emerged as one of the most widely investigated
materials in catalysis mainly due to their excellent component tunability, high surface area …