Interdisciplinary Sciences, Computational Life Sciences This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding
AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classi...
Source: Interdisciplinary Sciences, Computational Life Sciences - December 7, 2023 Category: Bioinformatics Source Type: research

DCDA: CircRNA –Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder
This study proposes a deep learning-based circRNA –disease association predictor methodology called DCDA, which uses multiple data sources to create circRNA and disease features and reveal hidden feature codings of a circular RNA–disease pair with a deep autoencoder, then predict the relation score of the pair by a deep neural network. Fivefold cross-validation results on the benchmark dataset showed that our model outperforms state-of-the-art prediction methods in the literature with the AUC score of 0.9794.Graphical abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - November 17, 2023 Category: Bioinformatics Source Type: research

Comprehensive scRNA-seq Model Reveals Artery Endothelial Cell Heterogeneity and Metabolic Preference in Human Vascular Disease
AbstractVascular disease is one of the major causes of death worldwide. Endothelial cells are important components of the vascular structure. A better understanding of the endothelial cell changes in the development of vascular disease may provide new targets for clinical treatment strategies. Single-cell RNA sequencing can serve as a powerful tool to explore transcription patterns, as well as cell type identity. Our current study is based on comprehensive scRNA-seq data of several types of human vascular disease datasets with deep-learning-based algorithm. A gene set scoring system, created based on cell clustering, may h...
Source: Interdisciplinary Sciences, Computational Life Sciences - November 17, 2023 Category: Bioinformatics Source Type: research

Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models
AbstractAs one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning wa...
Source: Interdisciplinary Sciences, Computational Life Sciences - November 14, 2023 Category: Bioinformatics Source Type: research

Computer-Aided Diagnosis of Complications After Liver Transplantation Based on Transfer Learning
AbstractLiver transplantation is one of the most effective treatments for acute liver failure, cirrhosis, and even liver cancer. The prediction of postoperative complications is of great significance for liver transplantation. However, the existing prediction methods based on traditional machine learning are often unavailable or unreliable due to the insufficient amount of real liver transplantation data. Therefore, we propose a new framework to increase the accuracy of computer-aided diagnosis of complications after liver transplantation with transfer learning, which can handle small-scale but high-dimensional data proble...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 25, 2023 Category: Bioinformatics Source Type: research

Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data
In this study, we developed a novel computational method for clustering scRNA-seq data by considering both global and local information, named GCFG. This method characterizes the global properties of data by applying concept factorization, and the regularized Gaussian graphical model is utilized to evaluate the local embedding relationship of data. To learn the cell –cell similarity matrix, we integrated the two components, and an iterative optimization algorithm was developed. The categorization of single cells is obtained by applying Louvain, a modularity-based community discovery algorithm, to the similarity matrix. T...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 10, 2023 Category: Bioinformatics Source Type: research

A Multi-perspective Model for Protein –Ligand-Binding Affinity Prediction
AbstractGathering information from multi-perspective graphs is an essential issue for many applications especially for protein –ligand-binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein–ligand complexes with bette r interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein–ligand-binding affinity problem, taking into account the heterogeneity of proteins an...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 10, 2023 Category: Bioinformatics Source Type: research

Tumour Growth Mechanisms Determine Effectiveness of Adaptive Therapy in Glandular Tumours
AbstractIn cancer treatment, adaptive therapy holds promise for delaying the onset of recurrence through regulating the competition between drug-sensitive and drug-resistant cells. Adaptive therapy has been studied in well-mixed models assuming free mixing of all cells and spatial models considering the interactions of single cells with their immediate adjacent cells. Both models do not reflect the spatial structure in glandular tumours where intra-gland cellular interaction is high, while inter-gland interaction is limited. Here, we use mathematical modelling to study the effects of adaptive therapy on glandular tumours t...
Source: Interdisciplinary Sciences, Computational Life Sciences - September 30, 2023 Category: Bioinformatics Source Type: research

A Novel Deep Learning Model for Medical Image Segmentation with Convolutional Neural Network and Transformer
AbstractAccurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that combine transformer and convolutional neural networks often use skip connections in U-shaped networks, which may limit their ability to capture contextual information in medical images. To address this limitation, we propose a coordinated mobile and residual transformer UNet (MRC-TransUNet) that combines the strengths of transformer and UNet architectures. Our approach uses a lightweight MR-ViT to address the semantic g...
Source: Interdisciplinary Sciences, Computational Life Sciences - September 4, 2023 Category: Bioinformatics Source Type: research

CA-UNet Segmentation Makes a Good Ischemic Stroke Risk Prediction
AbstractStroke is still the World ’s second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction o...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 26, 2023 Category: Bioinformatics Source Type: research

Identification of gene-level methylation for disease prediction
In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Succe...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 21, 2023 Category: Bioinformatics Source Type: research

Classification of Glomerular Pathology Images in Children Using Convolutional Neural Networks with Improved SE-ResNet Module
AbstractClassification of glomerular pathology based on histology sections is the key to diagnose the type and degree of kidney diseases. To address problems in the classification of glomerular lesions in children, a deep learning-based complete glomerular classification framework was designed to detect and classify glomerular pathology. A neural network integrating Resnet and Senet (RS-INet) was proposed and a glomerular classification algorithm implemented to achieve high-precision classification of glomerular pathology. SE-Resnet was applied with improvement by transforming the convolutional layer of the original Resnet...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 31, 2023 Category: Bioinformatics Source Type: research

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation
AbstractBreast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 24, 2023 Category: Bioinformatics Source Type: research

Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning
ConclusionsA deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm ’s ability to classify RV abnormalities by comparing it with human experts.Graphical abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - July 20, 2023 Category: Bioinformatics Source Type: research

StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images
AbstractKidney stone disease is one of the most common and serious health problems in much of the world, leading to many hospitalizations with severe pain. Detecting small stones is difficult and time-consuming, so an early diagnosis of kidney disease is needed to prevent the loss of kidney failure. Recent advances in artificial intelligence (AI) found to be very successful in the diagnosis of various diseases in the biomedical field. However, existing models using deep networks have several problems, such as high computational cost, long training time, and huge parameters. Providing a low-cost solution for diagnosing kidn...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 15, 2023 Category: Bioinformatics Source Type: research