AIMC Topic: Machine Learning

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Statistical stopping criteria for automated screening in systematic reviews.

Systematic reviews
Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising th...

Deep-learned spike representations and sorting via an ensemble of auto-encoders.

Neural networks : the official journal of the International Neural Network Society
Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the...

Generating photo-realistic training data to improve face recognition accuracy.

Neural networks : the official journal of the International Neural Network Society
Face recognition has become a widely adopted biometric in forensics, security and law enforcement thanks to the high accuracy achieved by systems based on convolutional neural networks (CNNs). However, to achieve good performance, CNNs need to be tra...

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets.

Cell systems
Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity-based memory. We find that severe acute respiratory syndrome coronavirus ...

Machine learning techniques for mitoses classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, thei...

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

Journal of chemical information and modeling
Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test sce...

A Customizable Analysis Flow in Integrative Multi-Omics.

Biomolecules
The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology...

Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning.

Biomolecules
Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including...

A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals' Quality.

Sensors (Basel, Switzerland)
Fixation time measures have been widely adopted in studies with infants and young children because they can successfully tap on their meaningful nonverbal behaviors. While recording preverbal children's behavior is relatively simple, analysis of coll...