AIMC Topic: Machine Learning

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A comparative study of forest methods for time-to-event data: variable selection and predictive performance.

BMC medical research methodology
BACKGROUND: As a hot method in machine learning field, the forests approach is an attractive alternative approach to Cox model. Random survival forests (RSF) methodology is the most popular survival forests method, whereas its drawbacks exist such as...

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependenc...

Advances in understanding the specificity function of transporters by machine learning.

Computers in biology and medicine
Understanding the underlying molecular mechanism of transporter activity is one of the major discussions in structural biology. A transporter can exclusively transport one ion (specific transporter) or multiple ions (general transporter). This study ...

Stacked Ensemble Machine Learning for Range-Separation Parameters.

The journal of physical chemistry letters
Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the none...

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.

Medical sciences (Basel, Switzerland)
BACKGROUND: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters.

Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks.

Sensors (Basel, Switzerland)
Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking pa...

Learning Hierarchical Representations with Spike-and-Slab Inception Network.

Sensors (Basel, Switzerland)
Recently, deep convolutional neural networks (CNN) with inception modules have attracted much attention due to their excellent performances on diverse domains. Nevertheless, the basic CNN can only capture a univariate feature, which is essentially li...

Editorial: Special Issue "Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems".

Sensors (Basel, Switzerland)
Artificial intelligence (AI), together with robotics, sensors, sensor networks, internet of things (IoT) and machine/deep learning modeling, has reached the forefront towards the goal of increased efficiency in a multitude of application and purpose ...

Visual explanations from spiking neural networks using inter-spike intervals.

Scientific reports
By emulating biological features in brain, Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional deep learning. To make SNNs ubiquitous, a 'visual explanation' technique for analysing and explaining the internal spike b...

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...