AIMC Topic: Machine Learning

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Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.

Methods (San Diego, Calif.)
Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-...

Geographic origin discrimination of pork from different Chinese regions using mineral elements analysis assisted by machine learning techniques.

Food chemistry
Porkis thelargest-producedandmost-consumedmeat intheworld, and the food market globalization has increased public attention to food origin. Therefore, advanced techniques are required to determine the geographical origin of pork. This study investiga...

Leveraging maximum entropy and correlation on latent factors for learning representations.

Neural networks : the official journal of the International Neural Network Society
Many tasks involve learning representations from matrices, and Non-negative Matrix Factorization (NMF) has been widely used due to its excellent interpretability. Through factorization, sample vectors are reconstructed as additive combinations of lat...

Stroke prognostication for discharge planning with machine learning: A derivation study.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data t...

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

Computers in biology and medicine
INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing h...

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.

Sensors (Basel, Switzerland)
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows compr...

Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning methods for overall survival classification of Glioblastoma multiforme patients, and to robustify mod...

Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rare...

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except...