AIMC Topic: Machine Learning

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Microbiome Tools for Forensic Science.

Trends in biotechnology
Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, p...

DrugClust: A machine learning approach for drugs side effects prediction.

Computational biology and chemistry
BACKGROUND: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predi...

Adaptive feature selection using v-shaped binary particle swarm optimization.

PloS one
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features....

Using classification tree analysis to generate propensity score weights.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: In evaluating non-randomized interventions, propensity scores (PS) estimate the probability of assignment to the treatment group given observed characteristics. Machine learning algorithms have been proposed as an alte...

High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: The main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the p...

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

Scientific reports
There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients...

Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks.

Trends in ecology & evolution
We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would p...

Automated Identification of Diabetic Retinopathy Using Deep Learning.

Ophthalmology
PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The obje...

Correlates of sleep quality in midlife and beyond: a machine learning analysis.

Sleep medicine
OBJECTIVES: In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whe...

Representation learning via Dual-Autoencoder for recommendation.

Neural networks : the official journal of the International Neural Network Society
Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e...