Surveys

Machine Scoring of Medical Students' Written Clinical Reasoning: Initial Validity Evidence.

PURPOSE: Developing medical students' clinical reasoning requires a structured longitudinal curricul...

Machine learning for perturbational single-cell omics.

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes...

Developing a standardized protocol for computational sentiment analysis research using health-related social media data.

OBJECTIVE: Sentiment analysis is a popular tool for analyzing health-related social media content. H...

Pretraining model for biological sequence data.

With the development of high-throughput sequencing technology, biological sequence data reflecting l...

Reducing Bias in Healthcare Artificial Intelligence.

Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.

The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex...

Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.

STUDY OBJECTIVES: The objective of this study was to evaluate interrater reliability between manual ...

Artificial intelligence, bias, and patients' perspectives.

Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches.

Unstructured medical text labeling technologies are expected to be highly demanded since the interes...

Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches.

Gene expression data provide the expression levels of tens of thousands of genes from several hundre...

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

The significance of pan-cancer categories has recently been recognized as widespread in cancer resea...

Research on the influence of attention and emotion of tea drinkers based on artificial neural network.

Tea can help to regulate the mood of human. Based on the influence of tea on people's mood and atten...

Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

IMPORTANCE: Because more than one-third of people making nonfatal suicide attempts do not receive me...

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.

The development of deep sequencing technologies has led to the discovery of novel transcripts. Many ...

Regression on imperfect class labels derived by unsupervised clustering.

Outcome regressed on class labels identified by unsupervised clustering is custom in many applicatio...

The risk of racial bias while tracking influenza-related content on social media using machine learning.

OBJECTIVE: Machine learning is used to understand and track influenza-related content on social medi...

Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence.

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to exp...

Addressing Artificial Intelligence Bias in Retinal Diagnostics.

PURPOSE: This study evaluated generative methods to potentially mitigate artificial intelligence (AI...

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

The task of predicting the interactions between drugs and targets plays a key role in the process of...

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A review, recently published in this journal by Fang (2019), showed that methods trained for the pre...

A survey on adverse drug reaction studies: data, tasks and machine learning methods.

MOTIVATION: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug disc...