AIMC Topic: Machine Learning

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Machine learning based automated dynamic quantification of left heart chamber volumes.

European heart journal. Cardiovascular Imaging
AIMS: Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-syst...

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2.

Journal of animal science
This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate r...

Memory and mental time travel in humans and social robots.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in ...

Text-mined fossil biodiversity dynamics using machine learning.

Proceedings. Biological sciences
Documented occurrences of fossil taxa are the empirical foundation for understanding large-scale biodiversity changes and evolutionary dynamics in deep time. The fossil record contains vast amounts of understudied taxa. Yet the compilation of huge vo...

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

Biomedizinische Technik. Biomedical engineering
PURPOSE: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance ...

WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

Nucleic acids research
N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction fram...

EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

Nucleic acids research
The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose...

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

Bioinformatics (Oxford, England)
MOTIVATION: As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modification systems. For better understanding of their functional mechanisms, it is fundamentally important ...

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Bioinformatics (Oxford, England)
MOTIVATION: Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the ove...