Related papers: Predicting antimicrobial drug consumption using we…
Antibiotics are a vital class of drugs closely associated with the prevention and treatment of bacterial infections. Accurate prediction of molecular antimicrobial activity remains a key challenge in the pursuit of novel antibiotic…
Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and…
Estimating the needs of healthcare products and inventory management are still challenging issues in hospitals nowadays. Centers are supposed to cope with tight budgets and patient satisfaction at the same time. Some issues can be tackled…
Recently, Antimicrobial peptides (AMPs) have been an area of interest in the researches, as the first line of defense against the bacteria. They are raising attention as an efficient way of fighting multidrug resistance. Discovering and…
Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare, rendering modern medicines ineffective. AMR arises from antibiotic production and bacterial evolution, but quantifying its transmission remains difficult. With…
Purpose: Antimicrobial resistance is a major global health concern, affecting hospital admissions and treatment success. This study aims to introduce an experimental setup for monitoring bacterial activity over time using image-based…
Identification of antimicrobial peptides is an important and necessary issue in today's era. Antimicrobial peptides are essential as an alternative to antibiotics for biomedical applications and many other practical applications. These…
Antimicrobial resistance is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated…
Batches of pharmaceutical are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detected when patients or healthcare providers report…
Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease.…
Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only…
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of…
Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference…
The paper utilizes the graph embeddings generated for entities of a large biomedical database to perform link prediction to capture various new relationships among different entities. A novel node similarity measure is proposed that…
Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico…
Antimicrobial resistance (AMR) is a risk for patients and a burden for the healthcare system. However, AMR assays typically take several days. This study develops predictive models for AMR based on easily available clinical and…
Microbes can affect processes from food production to human health. Such microbes are not isolated, but rather interact with each other and establish connections with their living environments. Understanding these interactions is essential…
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes…
Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and…
In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain…