Network Modeling Analysis in Health Informatics and Bioinformatics 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.

Few-shot classification with prototypical neural network for hospital flow recognition under uncertainty
In this study, we propose a Prototypical Neural Network (PNN) tailored for few-shot learning, which effectively learns a representation space from limited labeled data. This enables efficient recognition of distinct characteristics within hospital flow footprints, ensuring examples from the same class are proximate while those from different classes are distant. Additionally, we introduce a synthetic sampling technique (SST) to address uncertainties and variations inherent in hospital personnel flow, thereby enhancing the robustness and performance of our flow recognition system. Through extensive simulation studies, we ev...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 16, 2024 Category: Bioinformatics Source Type: research

TLOD: Innovative ovarian tumor detection for accurate multiclass classification and clinical application
AbstractOvarian tumors pose a major threat to women's health, mostly remaining undetected until they reach advanced stages, resulting in complex treatment and decreased survival rates. Besides, tumor heterogeneity is more responsible for poor treatment response and adverse prognosis. The purpose of this research is to identify ovarian epithelial tumors in premature stage using histopathological images. In this research, we address the need for an improved ovarian tumor detection method through the development of an innovative simple intelligent approach ‘Transfer Learning with ResNet-based Deep Learning for Ovarian Tumor...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 15, 2024 Category: Bioinformatics Source Type: research

Computational screening identifies depsidones as promising Aurora A kinase inhibitors: extra precision docking and molecular dynamics studies
In this study, we explored the potential of depsidone analogues as inhibitors of Aurora A kinase through computational methods. A molecular docking studies of 260 depsidone molecules against Aurora A kinase, were conducted using extra precision docking mode of Glide. Three molecules, parmosidone A, chaetosidone A, and parmosidone E, showed promising docking scores compared to the reference inhibitor. These compounds exhibited strong interactions with the binding site of Aurora A kinase, involving hydrogen bonds and hydrophobic interactions. To further evaluate the stability of these interactions, we performed molecular dyn...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 9, 2024 Category: Bioinformatics Source Type: research

Privacy-preserving predictive modeling for early detection of chronic kidney disease
AbstractWith the phenomenal growth of machine learning, privacy protection has become a major concern in medical science. Sensitive medical information, such as chronic kidney disease (CKD) data, is subject to privacy protections and cannot be shared with others without patients ’confidentiality and data security. This research study aims to design a secure and effective methodology for developing a predictive model for the early detection of chronic kidney disease. In our research study, we addressed two major problems: accurate diagnosis of chronic kidney disease and da ta security. The workflow in this research paper ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 6, 2024 Category: Bioinformatics Source Type: research

Drug repurposing for the treatment of patients infected with SARS-CoV-2
AbstractThis paper presents a systematic analysis of the possibility of repurposing commercial drugs through Molecular Docking and Quantitative Structure and Activity Relationship (QSAR) explorations for the treatment of patients infected with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To do this, we checked the chances of inhibiting the main protease (Mpro) of SARS-CoV-2, the cyclo-oxygenase-2 (COX-2) enzyme, and the spike viral protein by commercial drugs. The molecular structures of the ligands were obtained from the DrugBank database. All ligand molecular structures were previously optimised usin...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 4, 2024 Category: Bioinformatics Source Type: research

Identification of COVID-19 with CT scans using radiomics and DL-based features
AbstractDeep learning plays a crucial role in identifying COVID-19 patients from computed tomography (CT) scans by leveraging its ability to analyze vast amounts of image data and extract patterns indicative of the disease. While deep learning-based models have consistently achieved state-of-the-art performance, the incorporation of relevant handcrafted features alongside deep learning-based features has the potential to enhance overall performance even further. Therefore, this paper proposes a hybrid approach that combines handcrafted and deep learning features from CT scan images for accurate COVID-19 classification. Han...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 29, 2024 Category: Bioinformatics Source Type: research

CT and MRI image fusion via multimodal feature interaction network
AbstractComputed tomography  (CT) and magnetic resonance imaging (MRI) image fusion is a popular technique for integrating information from two different modalities of medical images. This technique can improve image quality and diagnostic efficacy. To effectively extract and balance complementary information in the source i mages, we propose an end-to-end multimodal feature interaction network (MFINet) to fuse CT and MRI images. The MIFNet consists of a shallow feature extractor, a feature interaction (FI), and an image reconstruction. In the FI, we design a deep feature extraction module, which consists of a series o...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 27, 2024 Category: Bioinformatics Source Type: research

Balancing cerebrovascular disease data with integrated ensemble learning and SVM-SMOTE
AbstractThe paper addresses the challenge of imbalanced classification in the context of cerebrovascular diseases, including stroke, transient ischemic attack (TIA), and vascular dementia. The imbalanced nature of cerebrovascular disease datasets poses significant challenges to conventional machine learning algorithms, making precise diagnosis and effective management difficult. The aim of the paper is to propose a novel approach, the INTEL_SS algorithm, which combines ensemble learning techniques with Support Vector Machine-Synthetic Minority Over-sampling Technique (SVM-SMOTE) to effectively handle the imbalanced nature ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 26, 2024 Category: Bioinformatics Source Type: research

$$\mathcal {B}\text {rain}{\mathcal{M}\mathcal{N}}\text {et}$$ : a unified neural network architecture for brain image classification
AbstractIn brain-related diseases, including Brain Tumours and Alzheimer ’s, accurate and timely diagnosis is crucial for effective medical intervention. Current state-of-the-art (SOTA) approaches in medical imaging predominantly focus on diagnosing a single brain disease at a time. However, recent research has uncovered intricate connections between various brain dise ases, realizing that treating one condition may lead to the development of others. Consequently, there is a growing need for accurate diagnostic systems addressing multiple brain-related diseases. Designing separate models for different diseases, however, ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 22, 2024 Category: Bioinformatics Source Type: research

Drug contraindications in comorbid diseases: a protein interactome perspective
AbstractAdverse drug reactions (ADRs) are leading causes of death and drug withdrawals and frequently co-occur with comorbidities. However, systematic studies on the effects of drugs on comorbidities are lacking. Drug interactions with the cellular protein –protein interaction (PPI) network give rise to ADRs. We selected 6 comorbid disease pairs, identified the drugs used in the treatment of the individual diseases ‘A’ and ‘B’– 44 drugs in anxiety and depression, 128 in asthma and hypertension, 48 in chronic obstructive pulmonary disease a nd heart failure, 58 in type 2 diabetes and obesity, 58 in Parkinson’s...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 19, 2024 Category: Bioinformatics Source Type: research

A hybrid modeling approach to simulate complex systems and classify behaviors
AbstractMany important systems, both natural and artificial, are complex in nature, and models and simulations are one of the main instruments to study them. In this paper, we present an approach where a complex social system is represented at a high level of abstraction as a network, thereby addressing several challenges such as quantification, intervention, adaptation and validation. The network represents the factors that influence the mental health and wellbeing in children and young people. In this article, we present an approach that links a system dynamics simulation to simulate the network and ranking algorithms to...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 18, 2024 Category: Bioinformatics Source Type: research

Comparative evaluation of multiomics integration tools for the study of prediabetes: insights into the earliest stages of type 2 diabetes mellitus
AbstractType 2 diabetes mellitus (T2D) remains a critical health concern, particularly in its early disease stages such as prediabetes. Understanding these early stages is paramount for improving patient outcomes. Multiomics data integration tools offer promise in unraveling the underlying mechanisms of T2D. The advent of high-throughput technology and the increasing availability of multiomics data has led to the development of several statistical and network-based integration methods. However, the performance of such methods varies, requiring their output evaluation in an unbiased manner. Here, we conducted a comparative ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 14, 2024 Category: Bioinformatics Source Type: research

MicroRNA-gene regulatory network of TLR signaling in neuroinflammation-induced Parkinson ’s disease: a bioinformatics approach
In this study, we used GO, a bioinformatics tool that uses the representations for genes in an organism; PPI, which shows the physical interac tion between proteins in an organism; and miRNet, a tool to navigate the complex relationships between miRNAs and their targets for deeper biologic understanding. To find out the potential TLR genes and regulatory miRNAs that play a role in neuroinflammation-induced PD. We acquired the gene expressi on profile, GSE26927, from the GEO Omnibus. DAVID bioinformatics and SHINY GO software were employed for GO analysis of DEGs, and the fold enrichment score for each pathway was verified....
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 5, 2024 Category: Bioinformatics Source Type: research

Pan-cancer classification of multi-omics data based on machine learning models
AbstractThe integration of multiple biological layers derived from different omics studies generates a novel concept of pan-cancer molecular classification, suggesting new therapeutic strategies for precision medicine. In this review, we will present a comprehensive portrait of the latest advances for multi-omics combination in oncology considering different cancer types. We will show the different applications of machine learning for characterizing cancer biology and the identification of prognostic and response to therapy prediction opening the scenario to personalized therapy. We grouped the selected articles into six m...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - February 14, 2024 Category: Bioinformatics Source Type: research

The application of exponential random graph models to collaboration networks in biomedical and health sciences: a review
This study took a review approach to collect and analyze ERGM applications in health sciences by following the protocol of a systematic review. We included a total of 30 studies. The bibliometric characteristics revealed significant authors, institutions, countries, funding agencies, and citation impact associated with the publications. In addition, we observed five types of ERGMs for network modeling (standard ERGM and its extensions —Bayesian ERGM, temporal ERGM, separable temporal ERGM, and multilevel ERGM). Most studies (80%) used the standard ERGM, which possesses only endogenous and exogenous variables examining ei...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - January 23, 2024 Category: Bioinformatics Source Type: research