Infectious Disease

[Application of machine learning in clinical predictive models for infectious diseases: a review].

Infectious diseases are one of the major threats to global public health. Inconvenience of diagnosis and treatment frequently causes misdiagnosis, missing diagnosis or overtreatment, resulting in serious clinical outcomes. As an important branch of artificial intelligence, machine learning has been widely used in multiple fields. Predictive models created based on patients' clinical characteristic...

A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery.

Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used ...

Are we ready to adapt artificial intelligence for early sepsis detection?

An automated malaria cells detection from thin blood smear images using deep learning.

Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagn...

Vitamin D: A promising antifungal and antibiofilm agent against species.

BACKGROUND AND PURPOSE: species are opportunistic fungal pathogens that cause mild to life-threatening infections in both immunocompetent and immunoc...

COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants.

Antiviral defenses are one of the significant roles of RNA interference (RNAi) in plants. It has been reported that the host RNAi mechanism machinery ...

An interpretable machine learning model for real-time sepsis prediction based on basic physiological indicators.

OBJECTIVE: In view of the important role of risk prediction models in the clinical diagnosis and treatment of sepsis, and the limitations of existing ...

Personal Data for Public Benefit: The Regulatory Determinants of Social Licence for Technologically Enhanced Antimicrobial Resistance Surveillance.

Technologically enhanced surveillance systems have been proposed for the task of monitoring and responding to antimicrobial resistance (AMR) in both h...

Artificial Intelligence for Early Sepsis Detection: A Word of Caution.

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening-based approaches.

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large ...

Harnessing Deep Learning for Omics in an Era of COVID-19.

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new pro...

OPTIMIZATION OF PRE-HOSPITAL FIRST AID MANAGEMENT STRATEGIES FOR PATIENTS WITH INFECTIOUS DISEASES IN HUIZHOU CITY USING DEEP LEARNING ALGORITHM.

The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is ex...

FACTORS AFFECTING PROGNOSIS AND MORTALITY IN SEVERE COVID-19 PNEUMONIA PATIENTS.

Fatality rate in coronavirus disease 2019 (COVID-19) cases has been reported to be 3.4% worldwide. The aim of this study was to evaluate the factors t...

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network.

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit sim...

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological condit...

Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to tre...

COVID-19 disease identification network based on weakly supervised feature selection.

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the healt...

Mechanism of assembly of type 4 filaments: everything you always wanted to know (but were afraid to ask).

Type 4 filaments (T4F) are a superfamily of filamentous nanomachines - virtually ubiquitous in prokaryotes and functionally versatile - of which type ...

Targeting trypanosomes: how chemogenomics and artificial intelligence can guide drug discovery.

Trypanosomatids are protozoan parasites that cause human and animal neglected diseases. Despite global efforts, effective treatments are still much ne...

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In additio...