Cultural Competence

Considerations for the Ethical Implementation of Psychological Assessment Through Social Media via Machine Learning.

The ubiquity of social media usage has led to exciting new technologies such as machine learning. Ma...

Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach.

Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at l...

Stacking models for nearly optimal link prediction in complex networks.

Most real-world networks are incompletely observed. Algorithms that can accurately predict which lin...

Multi-level feature aggregation network for instrument identification of endoscopic images.

Identification of surgical instruments is crucial in understanding surgical scenarios and providing ...

CD-NuSS: A Web Server for the Automated Secondary Structural Characterization of the Nucleic Acids from Circular Dichroism Spectra Using Extreme Gradient Boosting Decision-Tree, Neural Network and Kohonen Algorithms.

Nucleic acids exhibit a repertoire of conformational preference depending on the sequence and enviro...

Bias and ethical considerations in machine learning and the automation of perioperative risk assessment.

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem.

BACKGROUND: Automated segmentation of anatomical structures is a crucial step in image analysis. For...

Evolution and impact of bias in human and machine learning algorithm interaction.

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictio...

Photoautotrophic picoplankton - a review on their occurrence, role and diversity in Lake Balaton.

Occurrence of the smallest phototrophic microorganisms (photoautotrophic picoplankton, APP) in Lake ...

Improved myocardial perfusion PET imaging using artificial neural networks.

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of pati...

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

The study of complex microbial communities typically entails high-throughput sequencing and downstre...

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

This systematic review analyses and describes the application and diagnostic accuracy of Artificial ...

Investigating object compositionality in Generative Adversarial Networks.

Deep generative models seek to recover the process with which the observed data was generated. They ...

Hiding a plane with a pixel: examining shape-bias in CNNs and the benefit of building in biological constraints.

When deep convolutional neural networks (CNNs) are trained "end-to-end" on raw data, some of the fea...

Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with t...

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

Engineering gene and protein sequences with defined functional properties is a major goal of synthet...

Many-objective BAT algorithm.

In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. ...

Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks.

Chemical representations derived from deep learning are emerging as a powerful tool in areas such as...

Emerging Pharmacotherapy and Health Care Needs of Patients in the Age of Artificial Intelligence and Digitalization.

Advances in the application of artificial intelligence, digitization, technology, iCloud computing, ...

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

The efficiency of disease prevention and medical care service necessitated the prediction of inciden...

Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, ...