AIMC Topic: Machine Learning

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Statistical and machine learning approaches to predicting protein-ligand interactions.

Current opinion in structural biology
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to des...

Note on the quadratic penalties in elastic weight consolidation.

Proceedings of the National Academy of Sciences of the United States of America

Reply to Huszár: The elastic weight consolidation penalty is empirically valid.

Proceedings of the National Academy of Sciences of the United States of America

Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

Scientific reports
Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing...

Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing.

IEEE transactions on bio-medical engineering
OBJECTIVE: The aim of this research was to develop a swallowing assessment method to help prevent aspiration pneumonia. The method uses simple sensors to monitor swallowing function during an individual's daily life.

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

Academic radiology
RATIONALE AND OBJECTIVES: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV) not explained by other causes. W...

Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning.

IEEE transactions on medical imaging
Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the throug...

Maternal exposure to ambient PM during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models.

The Science of the total environment
Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM) on CHDs are inconsistent. We used ...

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer.

European journal of clinical investigation
BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine ...

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology ...