AIMC Topic: Machine Learning

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Quantifying uncertainty in machine learning classifiers for medical imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical...

Application of machine learning based methods in exposure-response analysis.

Journal of pharmacokinetics and pharmacodynamics
Robust estimation of exposure response analysis relies on correct specification of the model structure with traditional parametric approach. However, the assumptions of the handcrafted model may not always hold or verifiable. Here, we conducted a sim...

Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients.

The Journal of surgical research
INTRODUCTION: Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have ...

Development of Various Diabetes Prediction Models Using Machine Learning Techniques.

Diabetes & metabolism journal
BACKGROUND: There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.

EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Journal of molecular biology
Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of ev...

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events.

Journal of chemical theory and computation
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to ...

Emergence of Direction-Selective Retinal Cell Types in Task-Optimized Deep Learning Models.

Journal of computational biology : a journal of computational molecular cell biology
Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. P...

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions.

Sensors (Basel, Switzerland)
With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like a...

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance.

Sensors (Basel, Switzerland)
This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and t...

Comparative Study of Classification Algorithms for Various DNA Microarray Data.

Genes
Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of ...