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4D printing: Fundamentals, materials, applications and challenges
Abstract 4D printing refers to single-material or multi-material printing of a device or object
that can be transformed from a 1D strand into pre-programed 3D shape, from a 2D surface …
that can be transformed from a 1D strand into pre-programed 3D shape, from a 2D surface …
[HTML][HTML] A comprehensive review on biocompatible Mg-based alloys as temporary orthopaedic implants: Current status, challenges, and future prospects
Mg and its alloys are drawing huge attention since the last two decades as a viable option
for temporary implants applications. A commendable progress has already been made in …
for temporary implants applications. A commendable progress has already been made in …
A multimodal deep learning framework for predicting drug–drug interaction events
Abstract Motivation Drug–drug interactions (DDIs) are one of the major concerns in
pharmaceutical research. Many machine learning based methods have been proposed for …
pharmaceutical research. Many machine learning based methods have been proposed for …
The 2020 skyrmionics roadmap
The notion of non-trivial topological winding in condensed matter systems represents a
major area of present-day theoretical and experimental research. Magnetic materials offer a …
major area of present-day theoretical and experimental research. Magnetic materials offer a …
Modeling polypharmacy side effects with graph convolutional networks
Motivation The use of drug combinations, termed polypharmacy, is common to treat patients
with complex diseases or co-existing conditions. However, a major consequence of …
with complex diseases or co-existing conditions. However, a major consequence of …
Graph embedding on biomedical networks: methods, applications and evaluations
Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …
node representations, has drawn increasing attention in recent years. To date, most recent …
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
AI in health: state of the art, challenges, and future directions
Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad
range of disciplines in recent years, including health. The increase in computer hardware …
range of disciplines in recent years, including health. The increase in computer hardware …
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …
learning models have established their usefulness in biomedical applications, especially in …
Deep learning improves prediction of drug–drug and drug–food interactions
Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …
interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug …