Network Modeling Analysis in Health Informatics and Bioinformatics 
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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.Neural network-based strategies for automatically diagnosing of COVID-19 from X-ray images utilizing different feature extraction algorithms
This study aims to introduce neural network-based strategies for classifying and detecting COVID-19 early through image processing utilizing X-ray images. Despite the extensive use of directly fed X-ray images into the classifier, there is a lack of comparative analysis between the feature-based system and the direct imaging system in the classification of COVID-19 to evaluate the efficiency of feature extraction methods. Therefore, the proposed system represents introductory experiments of feature-based system using image Feature Extraction Algorithms [Texture, Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Dependence...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - July 4, 2023 Category: Bioinformatics Source Type: research
Global protein interactome of Deinococcus deserti reveals their strategies for radiation resistance
This study also predicted a few topologically important uncharacterized proteins. Amongst them, Deide_20140 and Deide_19830 are common from both the networks that could potentially be involved in the regulation of radiation stress responsive pathways ofD.deserti, which needs further study. So the radiation survival property ofD. deserti is a multidimensional phenomenon where proposed proteins and their biological processes might give a global picture with higher resolution. The molecular modules identified hare provide new directions for further validation and understanding of the stress resistance phenotype, which have tr...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 26, 2023 Category: Bioinformatics Source Type: research
Galangin for COVID-19 and Mucormycosis co-infection: a potential therapeutic strategy of targeting critical host signal pathways triggered by SARS-CoV-2 and Mucormycosis
AbstractMucormycosis co-infection with COVID-19 patients has a poor prognosis for fatality. However, they do not have any therapeutic choices right now. Galangin is prone to both COVID-19 and Mucormycosis, according to an ongoing study; galangin was investigated as a prospective molecular mechanism against COVID-19 with Mucormycosis co-infection to determine its functional role and underlying mechanisms of action. In SARS-COV-2 and Mucormycosis co-infection, we have conducted a series of computational approaches to identify and describe the beneficial mechanism, and pharmacological targets with therapeutic strategies of ga...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 19, 2023 Category: Bioinformatics Source Type: research
Mobile health applications for self-management in chronic lung disease: a systematic review
AbstractIntegration of mobile health (mHealth) applications (apps) into chronic lung disease management is becoming increasingly popular. MHealth apps may support adoption of self-management behaviors to assist people in symptoms control and quality of life enhancement. However, mHealth apps’ designs, features, and content are inco nsistently reported, making it difficult to determine which were the effective components. Therefore, this review aims to summarize the characteristics and features of published mHealth apps for chronic lung diseases. A structured search strategy across five databases (CINAHL, Medline, Embas...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 6, 2023 Category: Bioinformatics Source Type: research
Mobile heath applications for self-management in chronic lung disease: a systematic review
AbstractIntegration of mobile health (mHealth) applications (apps) into chronic lung disease management is becoming increasingly popular. MHealth apps may support adoption of self-management behaviors to assist people in symptoms control and quality of life enhancement. However, mHealth apps’ designs, features, and content are inco nsistently reported, making it difficult to determine which were the effective components. Therefore, this review aims to summarize the characteristics and features of published mHealth apps for chronic lung diseases. A structured search strategy across five databases (CINAHL, Medline, Embas...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 6, 2023 Category: Bioinformatics Source Type: research
A modified kNN algorithm to detect Parkinson ’s disease
Abstract
Parkinson ’s disorder is the second most common neurodegenerative disease, where the patients experience unintentional agitations and motor skills deterioration over time. Therefore, the proposed work has been developed by improving the conventional kNN to detect Parkinsonian disorder with (i) Gait, (ii) Ha ndwriting, and (iii) Voice parameters. In this approach, the concept of weights and δ-neighbourhood is introduced for predicting the class of unknown test samples by replacing voting and neighbouring points. It designs the δ-neighbourhood region under the aegis ofk-neighbouring points, resulting in envis...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - May 26, 2023 Category: Bioinformatics Source Type: research
Genome annotation and comparative functional analysis of genomic islands in Bordetella pertussis Tohama I, Bordetella parapertussis 12822, and Bordetella bronchiseptica RB50 genomes
In conclusion, proteins encoded by predicted genomic islands might be associated with the development of increased virulence, pathogenicity, and host restriction for humans toB. pertussis in comparison toB. parapertussis andB. bronchiseptica. The novel encoded proteins can be exploited as potential therapeutic drug targets againstBordetella species. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - May 23, 2023 Category: Bioinformatics Source Type: research
A deep learning approach for nucleus segmentation and tumor classification from lung histopathological images
AbstractLung cancer is the leading cause of death worldwide. Early diagnosis is crucial to improve patients ’ chance of survival. Automated detection and analysis of cancer types can significantly improve the diagnosis process. It can aid treatment through follow-up analyses. This paper proposes a deep learning based pipeline for multi-class classification of lung tumor type (Benign (B), ADenoCarcinoma (ADC) and Squamous-Cell Carcinoma (SCC)) from histopathological images. A baseline classification method, the\(P_{dir}\) pipeline, is proposed where Whole Slide Histopathological Image (WSHI) patches are classified using t...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - May 7, 2023 Category: Bioinformatics Source Type: research
A subunit vaccine against pneumonia: targeting Streptococcus pneumoniae and Klebsiella pneumoniae
The objective of this work was to develop an immunogenic multi-epitope subunit vaccine capable of eliciting a robust immune response againstS. pneumoniae andK. pneumoniae. The targeted proteins were the pneumococcal surface proteins (PspA and PspC) and choline-binding protein (CbpA) ofS. pneumoniae and the outer membrane proteins (OmpA and OmpW) ofK. pneumoniae. Different computational approaches and various immune filters were employed for designing a vaccine. The immunogenicity and safety of the vaccine were evaluated by utilizing many physicochemical and antigenic profiles. To improve structural stability, disulfide eng...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 19, 2023 Category: Bioinformatics Source Type: research
Human DNA/RNA motif mining using deep-learning methods: a scoping review
This study concludes that the application of existing deep-learning methods in the field of motif discovery is the faster way to process complex data relevant to genomic sequences. Through the PRISMA-ScR reporting guidelines and literature survey analysis, more than 30 existing deep-learning models are compared, and it is concluded that complex DL models are preferred over simpler DL models in terms of performance and scalability evaluation. Selective selection of a DL model architecture can be made to understand the complex behavior of motifs and their associated regulatory mechanism at the gene level. (Source: Network Mo...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 12, 2023 Category: Bioinformatics Source Type: research
Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection
AbstractSchizophrenia is a psychiatric disorder characterized by symptoms such as disorganized thinking, hallucinations, disintegration of reality perception, and delusions, among others. Resting-state functional magnetic resonance imaging is a promising method for studying changes in functional brain networks in schizophrenic patients. Graph theoretic representations can effectively distinguish between healthy and schizophrenic subjects. The process of grouping users with similar interests in social networks, which can also be used to group diseased subjects, is known as community detection. In this paper, we propose a me...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - April 4, 2023 Category: Bioinformatics Source Type: research
Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage
AbstractImage segmentation being the first step is always crucial for brain aneurysm treatment planning; it is also crucial during the procedure. A robust brain aneurysm segmentation has the potential to prevent the blood leakage, also known as sentinel hemorrhage. Here, we present a method combining a multiresolution and a statistical approach in two dimensional domain to segment cerebral aneurysm in which the Contourlet transform (CT) extracts the image features, while the Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algor...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 16, 2023 Category: Bioinformatics Source Type: research
COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks
In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - March 13, 2023 Category: Bioinformatics Source Type: research
Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2
AbstractResponding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the “COVID Moonshot” project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell m embranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug di...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - February 6, 2023 Category: Bioinformatics Source Type: research
Deciphering the lysine acetylation pattern of leptospiral strains by in silico approach
This study aims to identify protein lysine acetylation patterns among groups of pathogenic and saprophytic species ofLeptospira, by screening the leptospiral proteome using a robust proteomics approach. In this study, a total of 15, 78,796 acetylated proteins with 83, 65,945 acetylation sites were identified among 469 strains of Leptospira to predict the pathogenesis pattern and signature peptide sequence, which was conserved among pathogenic Leptospira species, that can be used as a novel vaccine candidate. A similar pattern of acetylation was observed among the pathogenic and intermediate groups while different in the sa...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - January 22, 2023 Category: Bioinformatics Source Type: research
