AIMC Topic: Machine Learning

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Deep learning uncertainty quantification for clinical text classification.

Journal of biomedical informatics
INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the st...

Hold out the genome: a roadmap to solving the cis-regulatory code.

Nature
Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The 'cis-regulatory code' - how cells interpret DNA sequences to determine when, where and how much genes should be expressed - has proven ...

Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

PLoS computational biology
When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have u...

MLcps: machine learning cumulative performance score for classification problems.

GigaScience
BACKGROUND: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performa...

The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior.

Perspectives on psychological science : a journal of the Association for Psychological Science
More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden-but serious-problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts click...

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.

NeuroImage
This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decad...

Human-Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors.

Sensors (Basel, Switzerland)
Human-robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a ...

Application of Artificial Intelligence or machine learning in risk sharing agreements for pharmacotherapy risk management.

Journal of integrative bioinformatics
Applications of Artificial Intelligence in medical informatics solutions risk sharing have social value. At a time of ever-increasing cost for the provision of medicines to citizens, there is a need to restrain the growth of health care costs. The se...

Prediction model of subacromial pain syndrome in assembly workers using shoulder range of motion and muscle strength based on support vector machine.

Ergonomics
Subacromial pain syndrome (SAPS) is the most common upper-extremity musculoskeletal problem among workers. In this study, a machine learning model was built to predict and classify the presence or absence of SAPS in assembly workers with shoulder joi...

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics.

Journal of chemical information and modeling
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps...