AIMC Topic: Machine Learning

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Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Journal of medical systems
Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic disorders, or mental disorders. It is important to determine the cause and treatment of dementia as ear...

Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms.

Scientific reports
Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neu...

TooT-T: discrimination of transport proteins from non-transport proteins.

BMC bioinformatics
BACKGROUND: Membrane transport proteins (transporters) play an essential role in every living cell by transporting hydrophilic molecules across the hydrophobic membranes. While the sequences of many membrane proteins are known, their structure and fu...

Learning-based local-to-global landmark annotation for automatic 3D cephalometry.

Physics in medicine and biology
The annotation of three-dimensional (3D) cephalometric landmarks in 3D computerized tomography (CT) has become an essential part of cephalometric analysis, which is used for diagnosis, surgical planning, and treatment evaluation. The automation of 3D...

A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy.

Physics in medicine and biology
To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planni...

Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance.

PloS one
Incorporating expert knowledge at the time machine learning models are trained holds promise for producing models that are easier to interpret. The main objectives of this study were to use a feature engineering approach to incorporate clinical exper...

Machine learning volumetry of ischemic brain lesions on CT after thrombectomy-prospective diagnostic accuracy study in ischemic stroke patients.

Neuroradiology
PURPOSE: Ischemic lesion volume (ILV) is an important radiological predictor of functional outcome in patients with anterior circulation stroke. Our aim was to assess the agreement between automated ILV measurements on NCCT using the Brainomix softwa...

Age estimation using bloodstain miRNAs based on massive parallel sequencing and machine learning: A pilot study.

Forensic science international. Genetics
Age estimation is one of the most important components in the practice of forensic science, especially for body fluids or stains at crime scenes. Recent studies have focused on the application of DNA methylation for chronological age determination in...

iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation.

Analytical biochemistry
In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompts for novel and effective treatment. Cancer immunotherapy represents a promising venue for treatment...

Electronic skins and machine learning for intelligent soft robots.

Science robotics
Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for ...