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Supervised Machine Learning

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Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

IEEE/ACM transactions on computational biology and bioinformatics
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, i...

Predicting and understanding human action decisions during skillful joint-action using supervised machine learning and explainable-AI.

Scientific reports
This study investigated the utility of supervised machine learning (SML) and explainable artificial intelligence (AI) techniques for modeling and understanding human decision-making during multiagent task performance. Long short-term memory (LSTM) ne...

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

Journal of neural engineering
. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detectio...

Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning.

Annals of biomedical engineering
Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level fe...

Domain Adaptation Methods for Lab-to-Field Human Context Recognition.

Sensors (Basel, Switzerland)
Human context recognition (HCR) using sensor data is a crucial task in Context-Aware (CA) applications in domains such as healthcare and security. Supervised machine learning HCR models are trained using smartphone HCR datasets that are scripted or g...

Weakly supervised histopathology image segmentation with self-attention.

Medical image analysis
Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming and l...

Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variabi...

Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

PLoS computational biology
Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables...

NLS: An accurate and yet easy-to-interpret prediction method.

Neural networks : the official journal of the International Neural Network Society
Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status of state of the art and super-human level in some applications. However, the employment rate of ML models in real-li...

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning.

Journal of visualized experiments : JoVE
The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this ...