Bacterial Infection

Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment of infections caused by various microorganisms, including the gram-negative bacterium Escherichia coli. Traditional culture-based methods for detecting...

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.

Phages, the natural predators of bacteria, were discovered more than 100 years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments are needed to help screen phages quickly for therapeutic use. Traditional computational methods usually ignore the fact that phage-bacteria interacti...

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly id...

Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning.

Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 1...

Peptide Sequencing Via Protein Language Models

We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To da...

Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning

Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, includ...

Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for vari...

M5: A Whole Genome Bacterial Encoder at Single Nucleotide Resolution

A linear attention mechanism is described to extend the context length of an encoder only transformer, called M5 in this report, to a multi-million ...

Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis

We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency depa...

A deep learning method to predict bacterial ADP-ribosyltransferase toxins.

MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, R...

Detection of hospital super bacteria MRSA from the hands of healthcare professionals using machine learning and hyperspectral imaging.

Healthcare-associated infections resulting from cross-contamination, particularly from the hands of multidisciplinary staff, significantly impact pati...

A compact model of Escherichia coli core and biosynthetic metabolism

Metabolic models condense biochemical knowledge about organisms in a structured and standardised way. As large-scale network reconstructions are rea...

PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model

Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public heal...

skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements

Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated ...

Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study.

BACKGROUND: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from viral phary...

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public hea...

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to ...

Coracle-a machine learning framework to identify bacteria associated with continuous variables.

SUMMARY: We present Coracle, an artificial intelligence (AI) framework that can identify associations between bacterial communities and continuous var...

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.

Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly pres...

Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis.

BACKGROUND: The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments.