AIMC Topic: Neural Networks, Computer

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A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

Current medical imaging
BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at...

Convolutional Neural Network-based Virtual Screening.

Current medicinal chemistry
Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different sco...

Detecting breast cancer using artificial intelligence: Convolutional neural network.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist's efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have ...

Adversarial deconfounding autoencoder for learning robust gene expression embeddings.

Bioinformatics (Oxford, England)
MOTIVATION: Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in larg...

Supervised learning on phylogenetically distributed data.

Bioinformatics (Oxford, England)
MOTIVATION: The ability to develop robust machine-learning (ML) models is considered imperative to the adoption of ML techniques in biology and medicine fields. This challenge is particularly acute when data available for training is not independent ...

A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.

Bioinformatics (Oxford, England)
MOTIVATION: Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Compu...

Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data.

Bioinformatics (Oxford, England)
MOTIVATION: Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial struc...

Batch equalization with a generative adversarial network.

Bioinformatics (Oxford, England)
MOTIVATION: Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-batch variati...