AIMC Topic: Machine Learning

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Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 ...

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions.

Sensors (Basel, Switzerland)
The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system t...

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

BMJ open
OBJECTIVE: We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. ...

A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negative...

Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.

Journal of neural engineering
OBJECTIVE: Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electr...

Interpreting neural decoding models using grouped model reliance.

PLoS computational biology
Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, th...

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.

PloS one
Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research age...

A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, ...

Machine and deep learning approaches for cancer drug repurposing.

Seminars in cancer biology
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targ...

Predictive validity of radiographic signs of complete discoid lateral meniscus in children using machine learning techniques.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
The diagnostic utility of radiographic signs of complete discoid lateral meniscus remains controversial. This study aimed to investigate the diagnostic accuracy and determine which sign is most reliably detects the presence of a complete discoid late...