AIMC Topic: Machine Learning

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Fully Convolutional Network-Based Multifocus Image Fusion.

Neural computation
As the optical lenses for cameras always have limited depth of field, the captured images with the same scene are not all in focus. Multifocus image fusion is an efficient technology that can synthesize an all-in-focus image using several partially f...

Reinforced Adversarial Neural Computer for de Novo Molecular Design.

Journal of chemical information and modeling
In silico modeling is a crucial milestone in modern drug design and development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we p...

Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum.

Journal of medical Internet research
BACKGROUND: Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or "moderators") may participate within these forums to offer guidan...

Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.

Journal of evaluation in clinical practice
RATIONALE, AIMS, AND OBJECTIVES: Interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time, and the intervention is expected to "interrupt" the level and/or trend of th...

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

Scientific reports
Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal sam...

Predicting human protein function with multi-task deep neural networks.

PloS one
Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One majo...

Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Clinical radiology
AIM: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation.

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Methods (San Diego, Calif.)
Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare...

Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach.

Neuroscience
A large number of studies have demonstrated costly punishment to unfair events across human societies. However, individuals exhibit a large heterogeneity in costly punishment decisions, whereas the neuropsychological substrates underlying the heterog...

Marginal Structural Models with Counterfactual Effect Modifiers.

The international journal of biostatistics
UNLABELLED: In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers ...