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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

Journal of cellular and molecular medicine
Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer...

Deep learning modeling using normal mammograms for predicting breast cancer risk.

Medical physics
PURPOSE: To investigate two deep learning-based modeling schemes for predicting short-term risk of developing breast cancer using prior normal screening digital mammograms in a case-control setting.

Assessment of factors affecting tourism satisfaction using K-nearest neighborhood and random forest models.

BMC research notes
OBJECTIVE: This study aimed to identify factors affecting the satisfaction of tourists traveling to the city of Hamadan as Asian urban tourism capital in 2018. The data a random sample of 300 tourists were collected using a designed questionnaire. We...

Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD.

Journal of neural engineering
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders. Studies have tried to find the neural correlations of ADHD with electroencephalography (EEG). Due to the heterogeneity in the ADHD popu...

Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology.

PloS one
Growth in open-source hardware designs combined with the low-cost of high performance optoelectronic and robotics components has supported a resurgence of in-house custom lab equipment development. We describe a low cost (below $700), open-source, fu...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Human brain mapping
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the d...

Deep supervised learning with mixture of neural networks.

Artificial intelligence in medicine
Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single mo...

Artificial intelligence and the future of psychiatry: Insights from a global physician survey.

Artificial intelligence in medicine
BACKGROUND: Futurists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appe...

Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network.

Oral diseases
OBJECTIVES: The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)-odontogenic keratocysts, dentigerous cysts, and periapical cysts-using dental panoramic radiography and cone beam...