AIMC Topic: Deep Learning

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Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug desi...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...

A novel fast kilovoltage switching dual-energy CT with deep learning: Accuracy of CT number on virtual monochromatic imaging and iodine quantification.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: A novel fast kilovoltage switching dual-energy CT with deep learning [Deep learning based-spectral CT (DL-Spectral CT)], which generates a complete sinogram for each kilovolt using deep learning views that complement the measured views at ea...

Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, for dr...

Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Recent research in predicting protein secondary structure populations (SSP) based on Nuclear Magnetic Resonance (NMR) chemical shifts has helped quantitatively characterise the structural conformational properties of intrinsically disordered proteins...

Extracting and Learning Fine-Grained Labels from Chest Radiographs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful o...

Patient Cohort Retrieval using Transformer Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a g...

Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recogn...