AIMC Topic: Machine Learning

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Analyzing the Importance of Broker Identities in the Limit Order Book Through Deep Learning.

Big data
Limit order books (LOBs) have been widely adopted as a trading mechanism in global securities markets, and the degree of LOB transparency is one of the most studied topics in market design. In the past, this issue was mainly researched through the co...

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Chembiochem : a European journal of chemical biology
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved h...

Deep learning for the prediction of treatment response in depression.

Journal of affective disorders
BACKGROUND: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized ps...

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification.

Medical image analysis
The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular ...

Classifying the superfamily of small heat shock proteins by using g-gap dipeptide compositions.

International journal of biological macromolecules
Small heat shock protein (sHSP) is a superfamily of molecular chaperone and is found from archaea to human. Recent researches have demonstrated that sHSPs participate in a series of biological processes and are even closely associated with serious di...

Harnessing adaptive novelty for automated generation of cancer treatments.

Bio Systems
Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However,...

Emotion Elicitation Under Audiovisual Stimuli Reception: Should Artificial Intelligence Consider the Gender Perspective?

International journal of environmental research and public health
Identification of emotions triggered by different sourced stimuli can be applied to automatic systems that help, relieve or protect vulnerable groups of population. The selection of the best stimuli allows to train these artificial intelligence-based...

Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission.

PloS one
A 400-estimator gradient boosting classifier was trained to predict survival probabilities of trauma patients. The National Trauma Data Bank (NTDB) provided 799233 complete patient records (778303 survivors and 20930 deaths) each containing 32 featur...

Genome-enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods.

Animal genetics
This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregna...