PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 13, 2025

Introduction Pathological myopia (PM) is a serious visual impairment that may lead to irreversible damage or even blindness. Timely diagnosis and effective management of PM are great significance. Given the increasing number cases worldwide, there an urgent need develop automated, accurate, highly interpretable diagnostic technology. Methods We proposed computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used provide intuitive interpretation for model’s decision-making process. Results The experimental results demonstrated achieved excellent performance in automatically detecting PM, accuracies 98.50, 98.25, 97.25% training, validation, test datasets, respectively. can focus specific areas image when making detection decisions. Discussion developed capable reliably providing accurate detection. Grad-CAM also process model. This approach provides healthcare professionals tool AI

Language: Английский

---

Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics

Toxins, Journal Year: 2025, Volume and Issue: 17(2), P. 78 - 78

Published: Feb. 9, 2025

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools promising candidates for therapeutic development. However, traditional conotoxin classification functional characterization remain labor-intensive, necessitating increasing adoption computational approaches. In particular, machine learning (ML) techniques facilitated advancements sequence-based classification, prediction, de novo peptide design. This review explores recent progress applying ML deep (DL) research, comparing key databases, feature extraction techniques, models. Additionally, we discuss future directions, emphasizing integration multimodal data refinement predictive frameworks enhance discovery.

Language: Английский

---

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach

IET Systems Biology, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

Abstract Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy naturally derived peptides in reducing pressure. Hypertension one risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive possessing antihypertensive properties provide considerable potential viable substitutes for conventional pharmaceutical medications. Currently, thorough examination peptide (AHTPs), by using traditional wet‐lab methods highly expensive labours. Therefore, in‐silico approaches especially machine‐learning (ML) algorithms are favourable due saving time cost discovery AHTPs. In this study, novel ML‐based predictor, called StackAHTP was developed predicting accurate AHTPs from sequence only. The proposed method, utilise two types feature descriptors Pseudo‐Amino Acid Composition Dipeptide encode local global hidden information sequences. Furthermore, encoded features serially merged ranked through SHapley Additive explanations (SHAP) algorithm. Then, top fed into three different ensemble classifiers (Bagging, Boosting, Stacking) enhancing prediction performance model. StackAHTPs method achieved superior compare ML (AdaBoost, XGBoost Light Gradient Boosting (LightGBM), Bagging Boosting) on 10‐fold cross validation independent test. experimental outcomes demonstrate that our outperformed existing an accuracy 92.25% F1‐score 89.67% test non‐AHTPs. authors believe research will remarkably contribute large‐scale characterisation accelerate drug process. At https://github.com/ali‐ghulam/StackAHTPs you may find datasets used.

Language: Английский

---

Machine learning-based classification of viral membrane proteins

Current Proteomics, Journal Year: 2025, Volume and Issue: 22(1), P. 100003 - 100003

Published: Feb. 1, 2025

Language: Английский

---

Integrating reduced amino acid with language models for prediction of protein thermostability

Food Bioscience, Journal Year: 2025, Volume and Issue: unknown, P. 106934 - 106934

Published: May 1, 2025

Language: Английский

---

EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species

Briefings in Bioinformatics, Journal Year: 2025, Volume and Issue: 26(3)

Published: May 1, 2025

Abstract With the rapid advancement of proteomics, post-translational modifications, particularly lysine crotonylation (Kcr), have gained significant attention in basic research, drug development, and disease treatment. However, current methods for identifying these modifications are often complex, costly, time-consuming. To address challenges, we proposed EDS-Kcr, a novel bioinformatics tool that integrates state-of-the-art protein language model ESM2 with deep supervision to improve efficiency accuracy Kcr site prediction. EDS-Kcr demonstrated outstanding performance across various species datasets, proving its applicability wide range proteins, including those from humans, plants, animals, microbes. Compared existing prediction models, our excelled multiple key indicators, showcasing superior predictive power robustness. Furthermore, enhanced transparency interpretability through visualization techniques mechanisms. In conclusion, provides an efficient reliable suitable diagnosis development. We also established freely accessible web server at http://eds-kcr.lin-group.cn/.

Language: Английский

---

Predicting cyclins based on key features and machine learning methods

Methods, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

---

Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(24), P. 13674 - 13674

Published: Dec. 21, 2024

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large family with high infectivity and pathogenicity primary pathogen causing global pandemic disease 2019 (COVID-19). Phosphorylation major type protein post-translational modification that plays an essential role in process SARS-CoV-2–host interactions. The precise identification phosphorylation sites host cells infected SARS-CoV-2 will be great importance to investigate potential antiviral responses mechanisms exploit novel targets for therapeutic development. Numerous computational tools have been developed on basis phosphoproteomic data generated by mass spectrometry-based experimental techniques, which can accurately ascertained across whole SARS-CoV-2-infected proteomes. In this work, we comprehensively reviewed several aspects construction strategies availability these predictors, including benchmark dataset preparation, feature extraction refinement methods, machine learning algorithms deep architectures, model evaluation approaches metrics, publicly available web servers packages. We highlighted compared prediction performance each tool independent serine/threonine (S/T) tyrosine (Y) datasets discussed overall limitations current existing predictors. summary, review would provide pertinent insights into exploitation new powerful site tools, facilitate localization more suitable target molecules verification, contribute development therapies.

Language: Английский

---