AI Medical Compendium Journal:
mSystems

Showing 1 to 10 of 11 articles

Decoding cancer prognosis with deep learning: the ASD-cancer framework for tumor microenvironment analysis.

mSystems
Deep learning is revolutionizing biomedical research by facilitating the integration of multi-omics data sets while bridging classical bioinformatics with existing knowledge. Building on this powerful potential, Zhang et al. proposed a semi-supervise...

Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.

mSystems
UNLABELLED: In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pat...

deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.

mSystems
Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec) in the active sites, which is encoded by an in-frame UGA codon. Compared to eukaryotes, identificat...

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

mSystems
UNLABELLED: Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS...

Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers.

mSystems
The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiom...

Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.

mSystems
UNLABELLED: The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of micro...

Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.

mSystems
Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited....

Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance.

mSystems
UNLABELLED: The erythromycin resistance RNA methyltransferase () confers cross-resistance to all therapeutically important macrolides, lincosamides, and streptogramins (MLS phenotype). The expression of is often induced by the macrolide-mediated rib...

Antibiotic discovery with artificial intelligence for the treatment of infections.

mSystems
UNLABELLED: Global challenges presented by multidrug-resistant infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in . Here, a library of 11,648 ...

A deep learning method for predicting the minimum inhibitory concentration of antimicrobial peptides against using Multi-Branch-CNN and Attention.

mSystems
Antimicrobial peptides (AMPs) are a promising alternative to antibiotics to combat drug resistance in pathogenic bacteria. However, the development of AMPs with high potency and specificity remains a challenge, and new tools to evaluate antimicrobial...