SMAP: Similarity-based matrix factorization framework for inferring miRNA-disease association

Knowledge-Based Systems, Год журнала: 2023, Номер 263, С. 110295 - 110295

Опубликована: Янв. 13, 2023

Язык: Английский

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Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

Diagnostics, Год журнала: 2023, Номер 13(6), С. 1026 - 1026

Опубликована: Март 8, 2023

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms due to bone marrow producing many white blood cells (WBC). ALL most common types cancer in children and adults. Doctors determine treatment according its stages spread body. rely on analyzing samples under a microscope. Pathologists face challenges, such as similarity between infected normal WBC early stages. Manual diagnosis prone errors, differences opinion, lack experienced pathologists compared number patients. Thus, computer-assisted systems play an essential role assisting detection ALL. In this study, with high efficiency accuracy were developed analyze images C-NMC 2019 ALL-IDB2 datasets. all proposed systems, micrographs improved then fed active contour method extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, MobileNet). The first strategy two datasets hybrid technique CNN-RF CNN-XGBoost. DenseNet121, MobileNet deep feature maps. produce features redundant non-significant features. So, maps Principal Component Analysis (PCA) select highly representative sent RF XGBoost classifiers classification using serially fused models. DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, DenseNet121-ResNet50-MobileNet merged classified XGBoost. classifier reached AUC 99.1%, 98.8%, sensitivity 98.45%, precision 98.7%, specificity 98.85% dataset. With dataset, achieved 100% results AUC, accuracy, sensitivity, precision, specificity.

Язык: Английский

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Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification method

Heliyon, Год журнала: 2024, Номер 10(9), С. e30413 - e30413

Опубликована: Апрель 26, 2024

To comprehend the genuine reading habits and preferences of diverse user cohorts furnish tailored recommendations, this study introduces an English text recommendation model designed specifically for long-tail users. This integrates collaborative filtering algorithms with FastText classification method. Initially, integrated algorithm is explicated, followed by calculation user's interest distribution across various types texts, achieved through enhanced Ebbinghaus forgetting curve analysis behaviors. Subsequently, intelligent generated amalgamating association rule-based algorithms. Through optimization generation process, model's accuracy enhanced, thereby augmenting performance satisfaction system. Finally, a comparative conducted respect to Top-N model, matrix factorization-based illustrating superior F-Measure value proposed model. The findings indicate that when list contains 10, 30, 50, 70 0.75, 0.79, 0.8, 0.74, respectively, outperforming other Furthermore, as number texts increases, all four models gradually improves, final reaching 0.81. Notably, in significantly surpasses three methods. Demonstrating commendable recall rate, root mean square error, normalized cumulative gain, precision, accuracy, adeptly reflects interests, enhancing recommendations overall system performance. offer crucial insights guidance efficacy systems.

Язык: Английский

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LncRNA Expression Profile-Based Matrix Factorization for Predicting lncRNA- Disease Association

IEEE Access, Год журнала: 2024, Номер 12, С. 70297 - 70304

Опубликована: Янв. 1, 2024

Long non-coding RNAs (lncRNAs) play significant roles in multiple biological processes and contribute to the progression development of various human diseases. Therefore, it is necessary decipher novel lncRNA-disease associations from perspective biomarker detection. Numerous computational models have been designed identify using machine learning. However, many these fail effectively incorporate heterogeneous datasets, which can lead reduced model accuracy performance. In this study, we propose a lncRNA expression profile-based matrix factorization method that applies profiles (EMFLDA). Matrix learning exhibits excellent performance not only recommender systems, but also scientific areas. We applied as weights for proposed model, allowed integration information thereby improved As result, EMFLDA outperformed four previous terms AUC scores, achieving scores 0.9042 0.8841 based on leave-one-out cross-validation five-fold cross-validation, respectively. Thus, EMFLDA, serves an effective tool identifying disease-related lncRNAs, plays pivotal role extracting disease biomarkers.

Язык: Английский

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Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association

Biomedicines, Год журнала: 2025, Номер 13(1), С. 136 - 136

Опубликована: Янв. 8, 2025

Background: Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has devoted discovering pivotal of miRNAs incidence understand underlying pathogenesis human diseases. However, identifying miRNA-disease associations using experiments is inefficient terms cost and time. Methods: Here, we discuss a novel machine-learning model that effectively predicts disease-related graph convolutional neural network with collaborative filtering (GCNCF). By applying network, could capture important feature vectors present while preserving structure. exploiting filtering, were learned through matrix factorization deep learning, identified. Results: Extensive experimental results based on area under curve (AUC) scores (0.9216 0.9018) demonstrated superiority our over previous models. Conclusions: We anticipate not only serve as an effective tool for predicting but be employed universal computational framework inferring relationships across entities.

Язык: Английский

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Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion

Computers in Biology and Medicine, Год журнала: 2023, Номер 169, С. 107904 - 107904

Опубликована: Дек. 28, 2023

Язык: Английский

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Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations

BMC Genomic Data, Год журнала: 2025, Номер 26(1)

Опубликована: Янв. 14, 2025

miRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they often time-consuming complicated analyze by current bioinformatics tools. In recent years, machine learning (ML) deep (DL) techniques powerful tools large-scale biological data. Hence, developing a model predict, identify, rank connections diseases can significantly enhance the precision efficiency investigating relationships between diseases. this study, we utilized association data obtained biotechnological develop DL associations. To improve accuracy prediction model, introduced two labeling strategies, weight-based majority-based definitions, classify After preprocessing, was trained novel combining gated recurrent units (GRU) graph convolutional network (GCN) predict level The datasets were from HMDD (the Human miRNA Disease Database) categorized distinct approaches, definitions definitions. We classified associations into three groups, "upregulated", "downregulated" "nonspecific", regression analysis multiclass classification. This GRU-GCN coordinated achieved robust area under curve (AUC) score 0.8 all datasets, demonstrating efficacy predicting potential relationships. By introducing innovative label-preprocessing methods, study addressed diseases, improved ambiguity results different experiments. Based on these refined label developed DL-based refine offers valuable tool complementing experimental methods enhancing our understanding miRNA-related mechanisms.

Язык: Английский

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MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction

BMC Bioinformatics, Год журнала: 2025, Номер 26(1)

Опубликована: Янв. 17, 2025

Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional methods relying on demographic genetic data often fall short accuracy robustness. Recent graph-based models, while promising, frequently neglect role of atomic interactions fail integrate drug fingerprints with SMILES comprehensive molecular graph construction. We introduce multimodal multi-channel attention network adaptive fusion (MGATAF), a framework designed enhance predictions by capturing both local global among nodes. MGATAF improves representation integrating fingerprints, resulting more precise effects. The methodology involves constructing graphs, employing networks capture diverse interactions, using these at multiple abstraction levels. Empirical results demonstrate MGATAF's superior performance compared traditional other techniques. For example, GDSC dataset, achieved 5.12% improvement Pearson correlation coefficient (PCC), reaching 0.9312 an RMSE 0.0225. Similarly, new cell-line tests, outperformed baselines PCC 0.8536 0.0321 0.7364 0.0531 CCLE dataset. significantly advances effectively types complex interactions. This enhances offers robust tool personalized medicine, potentially leading safer Future research can expand this work exploring additional modalities refining mechanisms.

Язык: Английский

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NADG-GAM: Neighbor aggregation-based neurological disease-gene identification via optimal generative adjacency matrix

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112756 - 112756

Опубликована: Янв. 1, 2025

Язык: Английский

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lncRNA-disease association prediction based on optimizing measures of multi-graph regularized matrix factorization

Computer Methods in Biomechanics & Biomedical Engineering, Год журнала: 2025, Номер unknown, С. 1 - 16

Опубликована: Март 20, 2025

In this paper, we propose a novel lncRNA-disease association prediction algorithm based on optimizing measures of multi-graph regularized matrix factorization (OM-MGRMF). The method first calculates the semantic similarity diseases, functional lncRNAs, and Gaussian both. It then constructs new by using K-nearest-neighbor (KNN) algorithm. Finally, objective function is constructed through utilization ranking regularization constraints. This iteratively optimized an adaptive gradient descent experimental results OM-MGRMF outperform those classical methods in both K-fold cross-validation.

Язык: Английский

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