AIMC Topic: Machine Learning

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Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Scientific reports
Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors determine individual proneness to psychotic symptoms. Schizotypy is a multifaceted personality trait rela...

Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.

Scientific reports
Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and b...

An ensemble learning approach for modeling the systems biology of drug-induced injury.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being ...

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

PloS one
Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the...

Implementing a high-efficiency similarity analysis approach for firmware code.

PloS one
The rapid expansion of the open-source community has shortened the software development cycle, but the spread of vulnerabilities has been accelerated, especially in the field of the Internet of Things. In recent years, the frequency of attacks agains...

In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods.

Toxicology in vitro : an international journal published in association with BIBRA
In recent years, the decline of honey bees and the collapse of bee colonies have caught the attention of ecologists, and the use of pesticides is one of the main reasons for the decline. Therefore, ecological risk assessment of pesticides is essentia...

Machine learning monitoring for laser osteotomy.

Journal of biophotonics
This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser-induced acoustic emission, det...

A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer.

Cancer immunology research
A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are po...

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses.

Advances in therapy
INTRODUCTION: Administrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for body mass index (BMI), limits the sample sizes that can be analyzed to address certain research quest...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience
Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions present...