AIMC Topic: Neural Networks, Computer

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Using artificial neural networks to accelerate flowsheet optimization for downstream process development.

Biotechnology and bioengineering
An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches ...

Automatic Classification of Slit-Lamp Photographs by Imaging Illumination.

Cornea
PURPOSE: The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique.

The Detection of Nasopharyngeal Carcinomas Using a Neural Network Based on Nasopharyngoscopic Images.

The Laryngoscope
OBJECTIVE: To construct and validate a deep convolutional neural network (DCNN)-based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images.

Application of message passing neural networks for molecular property prediction.

Current opinion in structural biology
Accurate molecular property prediction, as one of the classical cheminformatics topics, plays a prominent role in the fields of computer-aided drug design. For instance, property prediction models can be used to quickly screen large molecular librari...

Comparison of four machine learning algorithms for a pre-impact fall detection system.

Medical & biological engineering & computing
In recent years, real-time health monitoring using wearable sensors has been an active area of research. This paper presents an efficient and low-cost fall detection system based on a pair of shoes equipped with inertial sensors and plantar pressure ...

Design of Nurr1 Agonists Fragment-Augmented Generative Deep Learning in Low-Data Regime.

Journal of medicinal chemistry
Generative neural networks trained on SMILES can design innovative bioactive molecules . These so-called chemical language models (CLMs) have typically been trained on tens of template molecules for fine-tuning. However, it is challenging to apply CL...

Transfer learning enables predictions in network biology.

Nature
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recen...

Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst Case.

Molecules (Basel, Switzerland)
Ab initio kinetic studies are important to understand and design novel chemical reactions. While the Artificial Force Induced Reaction (AFIR) method provides a convenient and efficient framework for kinetic studies, accurate explorations of reaction ...

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-he...

ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Histopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detectio...