AIMC Topic: Neural Networks, Computer

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Machine learning in anesthesiology: Detecting adverse events in clinical practice.

Health informatics journal
The credibility of threshold-based alarms in anesthesia monitors is low and most of the warnings they produce are not informative. This study aims to show that Machine Learning techniques have a potential to generate meaningful alarms during general ...

Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation.

Methods in molecular biology (Clifton, N.J.)
Protein posttranslational modifications (PTMs) are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof, the number of functionals PTMs in the eukaryotic proteome is currently underes...

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Methods in molecular biology (Clifton, N.J.)
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluor...

An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

Journal of X-ray science and technology
BACKGROUND: With the emergence of continuously mutating variants of coronavirus, it is urgent to develop a deep learning model for automatic COVID-19 diagnosis at early stages from chest X-ray images. Since laboratory testing is time-consuming and re...

A CNN-LASSO ensemble classification model for incomplete antibody reactants screening in coombs test.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Precise classification of incomplete antibody reactants (IAR) in the Coombs test is the primary means to prevent incompatible blood transfusions. Currently, an automatic and contactless method is required for accurate IAR classification t...

Deep Mining from Omics Data.

Methods in molecular biology (Clifton, N.J.)
Since the advent of high-throughput omics technologies, various molecular data such as genes, transcripts, proteins, and metabolites have been made widely available to researchers. This has afforded clinicians, bioinformaticians, statisticians, and d...

Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction.

Methods in molecular biology (Clifton, N.J.)
We introduce the CPFNN (Correlation Pre-Filtering Neural Network) for biological age prediction based on blood DNA methylation data. The model is built on 20,000 top correlated DNA methylation features and trained by 1810 healthy samples from GEO dat...

Differentiable Visual Computing: Challenges and Opportunities.

IEEE computer graphics and applications
Classical algorithms typically contain domain-specific insights. This makes them often more robust, interpretable, and efficient. On the other hand, deep-learning models must learn domain-specific insight from scratch from a large amount of data usin...

A method for machine learning generation of realistic synthetic datasets for validating healthcare applications.

Health informatics journal
Digital health applications can improve quality and effectiveness of healthcare, by offering a number of new tools to users, which are often considered a medical device. Assuring their safe operation requires, amongst others, clinical validation, nee...