Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery DOI Creative Commons
Ahmad Al-Omari,

Yazan H. Akkam,

Ala’a Zyout

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0315477 - e0315477

Published: Dec. 20, 2024

Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that even better eliminating The fundamental transformation in a variety of scientific disciplines, which led the emergence machine learning techniques, has presented significant opportunities for development antimicrobial peptides. Machine and deep used predict peptide efficacy study. main purpose overcome traditional experimental method constraints. Gram-negative bacterium Escherichia coli model organism this investigation assesses 1,360 sequences exhibit anti- E . activity. These peptides’ minimal inhibitory concentrations have been observed be correlated with set 34 physicochemical characteristics. Two distinct methodologies implemented. initial involves utilizing pre-computed attributes as input data machine-learning classification approach. In second method, these features converted into signal images, then transmitted neural network. first methods accuracy 74% 92.9%, respectively. proposed were developed target single microorganism (gram negative ), however, they offered framework could potentially adapted other types antimicrobial, antiviral, anticancer further validation. Furthermore, potential result time cost reductions, well innovative AMP-based treatments. research contributes advancement learning-based AMP drug discovery by generating potent application. implications processing biological computation pharmacology.

Language: Английский

Benchmarking Machine Learning Models for Cell Type Annotation in Single-Cell vs Single-Nucleus RNA-Seq Data DOI Creative Commons
Giovane G. Tortelote

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Abstract Background Machine learning (ML) models can automate cell annotation and reduce human bias. However, it remains unclear which ML model best suits the characteristics of single-cell RNA sequencing data whether a trained be applied to transcriptomes collected from nuclei rather than whole cells. This study evaluates performance eight selected for in (scRNA-seq) vs single-nucleus (snRNA-seq) datasets, focusing on their ability generalize across datasets with varying populations transcriptome isolation techniques. Results In first part, we use two publicly available scRNA-seq Peripheral Blood Mononuclear Cells (PBMC3K PBMC10K) assess each type classification within datasets. XGBoost achieved high accuracy (95.4%-95.8%), precision, F1-scores, outperforming simpler like Logistic Regression Naive Bayes. Ensemble methods Random Forest demonstrated strong precision recall. Elastic Net nearly as good generalizability achieving (94.7%-95.1%). second investigated impact techniques (single-cell vs. RNA-seq) using cardiomyocyte differentiation (GSE129096). Although excelled (accuracy F1-scores > 95%), declined notably data, suggesting inherent transcriptomic differences capacity. Notably, all struggled classifying intermediate-stage cells, highlighting challenges distinguishing transitional populations, such cardiac progenitors that retain stem markers while showing expression differentiated markers. Conclusion classify cells origination both snRNA-seq. tree-based penalized elastic regression superior diverse emphasizing importance selection robust annotation. These findings underscore need tailored computational approaches when working heterogeneous data.

Language: Английский

Citations

0
Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data DOI

Mary John,

Imad Barhumi

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Language: Английский

Citations

0
Clinical study on the application of a high-sensitivity electronic nose on thin-film gas sensor array technology combined with deep learning algorithm for early non-invasive diagnosis of chronic atrophic gastritis DOI
Mengting Zhang,

Long Zhu,

Jiezhou He

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107851 - 107851

Published: March 25, 2025

Language: Английский

Citations

0
TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining DOI
Qing Zhang, Xiaoxiao Wu, Xiang Li

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: unknown, P. 108809 - 108809

Published: April 1, 2025

Language: Английский

Citations

0
Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery DOI Creative Commons
Ahmad Al-Omari,

Yazan H. Akkam,

Ala’a Zyout

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0315477 - e0315477

Published: Dec. 20, 2024

Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that even better eliminating The fundamental transformation in a variety of scientific disciplines, which led the emergence machine learning techniques, has presented significant opportunities for development antimicrobial peptides. Machine and deep used predict peptide efficacy study. main purpose overcome traditional experimental method constraints. Gram-negative bacterium Escherichia coli model organism this investigation assesses 1,360 sequences exhibit anti- E . activity. These peptides’ minimal inhibitory concentrations have been observed be correlated with set 34 physicochemical characteristics. Two distinct methodologies implemented. initial involves utilizing pre-computed attributes as input data machine-learning classification approach. In second method, these features converted into signal images, then transmitted neural network. first methods accuracy 74% 92.9%, respectively. proposed were developed target single microorganism (gram negative ), however, they offered framework could potentially adapted other types antimicrobial, antiviral, anticancer further validation. Furthermore, potential result time cost reductions, well innovative AMP-based treatments. research contributes advancement learning-based AMP drug discovery by generating potent application. implications processing biological computation pharmacology.

Language: Английский

Citations

2