AIMC Topic: COVID-19

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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics.

Briefings in bioinformatics
Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs pr...

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local infor...

Accurate diagnosis of COVID-19 from lung CT images using transfer learning.

European review for medical and pharmacological sciences
OBJECTIVE: In this study, it is aimed to classify data by feature extraction from tomographic images for the diagnosis of COVID-19 using image processing and transfer learning.

Artificial Intelligence: A Game-Changer for Mental Health Care.

Cyberpsychology, behavior and social networking
Starting from the escalating global burden of mental health disorders, exacerbated by the COVID-19 pandemic, the article examines the potential of artificial intelligence (AI) to revolutionize mental health care. With nearly one in five adults facing...

Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions.

Cell reports. Medicine
This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global problem exacerbated by the COVID-19 pandemic. We provide a broad discussion on current developments...

scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.

GigaScience
BACKGROUND: Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction ne...

Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning.

GigaScience
Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computati...

Does Cohort Selection Affect Machine Learning from Clinical Data?

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study investigates cohort selection and its effects on the quality of machine learning (ML) models trained on clinical data, focusing on measurements taken within the first 48 hours of hospital admission. It discusses the potential repercussions...

Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. To address the complexity and data volume challenges involved in efficient medication information re...