AIMC Topic: Machine Learning

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Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks.

Journal of chemical information and modeling
Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN ) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes...

Prediction of future cognitive impairment among the community elderly: A machine-learning based approach.

Scientific reports
The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairm...

Using Machine Learning to Predict Sensorineural Hearing Loss Based on Perilymph Micro RNA Expression Profile.

Scientific reports
Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacologica...

Deep convolutional neural networks for accurate somatic mutation detection.

Nature communications
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...

Architectures and accuracy of artificial neural network for disease classification from omics data.

BMC genomics
BACKGROUND: Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as ...

IARNN-Based Semantic-Containing Double-Level Embedding Bi-LSTM for Question-and-Answer Matching.

Computational intelligence and neuroscience
We propose a novel end-to-end approach, namely, the semantic-containing double-level embedding Bi-LSTM model (SCDE-Bi-LSTM), to solve the three key problems of Q&A matching in the Chinese medical field. In the similarity calculation of the Q&A core m...

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques.

European journal of neurology
BACKGROUND AND PURPOSE: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the ...

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.

Biological psychiatry. Cognitive neuroscience and neuroimaging
The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identif...

Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood.

Planta
Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to arrive at species-level identification of wood. DNA barcoding is a promising tool to combat illegal ...