AIMC Topic: Deep Learning

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Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors.

ACS nano
This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity cha...

Single-Molecule Identification and Quantification of Steviol Glycosides with a Deep Learning-Powered Nanopore Sensor.

ACS nano
Steviol glycosides (SGs) are a class of high-potency noncalorie natural sweeteners made up of a common diterpenoid core and varying glycans. Thus, the diversity of glycans in composition, linkage, and isomerism results in the tremendous structural co...

Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning.

EBioMedicine
BACKGROUND: Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years and its poten...

Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis.

British journal of hospital medicine (London, England : 2005)
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using dee...

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

International journal of molecular sciences
Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exp...

Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis.

Scientific reports
Computed tomography (CT) is used as a valuable tool for device selection for interventional therapy in tricuspid regurgitation (TR). We aimed to evaluate predictors of TR reduction using CT and automated deep learning algorithms. Patients with severe...

Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.

BMC medical informatics and decision making
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, whi...

COVID-19 severity detection using chest X-ray segmentation and deep learning.

Scientific reports
COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe i...

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.

Nature communications
The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a "black box" lacking human meaningful explanations for the mo...

Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.

Nature communications
Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trad...