AIMC Topic: Machine Learning

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A document level neural model integrated domain knowledge for chemical-induced disease relations.

BMC bioinformatics
BACKGROUND: The effective combination of texts and knowledge may improve performances of natural language processing tasks. For the recognition of chemical-induced disease (CID) relations which may span sentence boundaries in an article, although exi...

Abstract concept learning in a simple neural network inspired by the insect brain.

PLoS computational biology
The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly h...

Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.

International ophthalmology
PURPOSE: In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM).

Endoflip vs high-definition manometry in the assessment of fecal incontinence: A data-driven unsupervised comparison.

Neurogastroenterology and motility
BACKGROUND: How much anal sphincter dysfunction contributes to fecal incontinence (FI) is not clear. High-definition anorectal manometry (HDAM) and functional luminal imaging probe (Endoflip) are two new techniques to study anal sphincter function.

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).

Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms.

Sensors (Basel, Switzerland)
Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of ...

Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks.

PloS one
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable im...

Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection.

IEEE/ACM transactions on computational biology and bioinformatics
To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial s...

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Journal of biomechanics
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning t...

CGPS: A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways.

Journal of genetics and genomics = Yi chuan xue bao
Gene set enrichment (GSE) analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE to...