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.

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

Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead
AbstractThe integration of deep learning (DL) into co-clinical applications has generated substantial interest among researchers aiming to enhance clinical decision support systems for various aspects of disease management, including detection, prediction, diagnosis, treatment, and therapy. However, the inherent opacity of DL methods has raised concerns within the healthcare community, particularly in high-risk or complex medical domains. There exists a significant gap in research and understanding when it comes to elucidating and rendering transparent the inner workings of DL models applied to the analysis of medical imag...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 22, 2023 Category: Bioinformatics Source Type: research

Unraveling the complexity: deep learning for imbalanced retinal lesion detection and multi-disease identification
In conclusion, the feasibility of using a single model was demonstrated, while the techniques employed helped to increase mAP-related metrics. Our research provides novel insights into the use of retinal photographs for the prediction of systemic biomarkers associated with multiple diseases. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 22, 2023 Category: Bioinformatics Source Type: research

A comprehensive survey to study the utilities of image segmentation methods in clinical routine
AbstractThe clinicians usually desire to know the shape of the liver during treatment planning to minimize the damage to the surrounding healthy tissues and hepatic vessels, thus, building the geometric model of the liver becomes paramount. There have been several liver image segmentation methods to build the model over the years. Considering the advantages of conventional image segmentation methods, this paper reviews them that spans over last 2 decades. The review examines about twenty-five automated and eleven semi-automatic approaches that include Probabilistic atlas,K-means, Model and knowledge-based (such as active a...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 15, 2023 Category: Bioinformatics Source Type: research

Collapsed lung disease classification by coupling denoising algorithms and deep learning techniques
AbstractLung collapse is an adverse lung condition that occurs due to an injury, tumor, or cancer in the lung. Atelectasis and pneumothorax are two primary lung disorders that can lead to the collapse of the lungs. In this article, we aimed to identify the cases of atelectasis and pneumothorax from the X-ray images of human lungs. The X-ray images are enhanced with Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Discrete Wavelet Transform (DWT) separately to remove the noises and improve the image quality. The enhanced images are convolved and merged together before passing them through a modified DenseNet201 ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 9, 2023 Category: Bioinformatics Source Type: research

An improved cost-sensitive approach toward the selection of wart treatment methods
AbstractWarts are benign tumors, caused due to the infection of human papillomavirus (HPV). The identification of wart-specific treatment methods is pertaining to major challenges such as class imbalance, prediction accuracy, and biased nature of learning algorithm. In this article, a bagged ensemble of cost-sensitive extra tree classifier (BECSETC) is developed toward the selection of wart-specific treatment methods. BECSETC outperforms the state-of-the-art techniques (SOTA) by a margin of (0 –45, 0\(-\)31.60), (0 –12, 0\(-\)2.6) in terms of sensitivity and specificity which overcome the imbalanced distribution on bot...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 14, 2023 Category: Bioinformatics Source Type: research

Automatic classification of depressive users on Twitter including temporal analysis
AbstractIn recent years, identifying traits of mental illness on social media platforms have caught researchers ’ attention. Unfortunately, different mental illnesses have similar symptoms, which makes detection very challenging. The present study conducts an examination of depressed users on the social media platform Twitter, utilizing the CLPsych 2015 database. In addition to conducting an analysis of sym ptoms and emotions as in current research, we have incorporated temporal analysis. This innovative approach has enabled us to differentiate between two prevalent forms of depression: Major Depressive Disorder and Pers...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 9, 2023 Category: Bioinformatics Source Type: research

A diagnosis model for detection and classification of diabetic retinopathy using deep learning
AbstractDiabetes mellitus (DM) is an immense progressive disease that affects the usage of blood glucose as energy, resulting in surplus glucose in the blood. If prolonged diabetes, it causes damage to both larger and smaller blood vessels, known as macrovascular and microvascular complications, respectively. The main objective of this paper is to develop an automated method for the detection, segmentation, and severity classification of type 2 diabetes mellitus (T2DM) microvascular complication Diabetic Retinopathy (DR) using the EyePACS dataset. An RU-Net (Residual U-Net) is proposed for segmentation, and a CCNN (Concate...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 13, 2023 Category: Bioinformatics Source Type: research

Analysis of cortisol mechanism to predict common genes between PCOS and its co-morbidities
AbstractPolycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder and one of the main causes of PCOS is hormonal imbalance due to lifestyle changes. Estrogen, progesterone, testosterone, cortisol and melatonin are the major hormones that regulate the menstrual cycle and other endocrine disorders in women. The hormone cortisol, in particular, can lead to many comorbid conditions related to PCOS. In this proposed work, PubMed articles were mined using R program to retrieve target genes for PCOS. The NCBI Gene/Genome database was used to download PCOS genes, and genes related to cortisol and major comorbid disea...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 23, 2023 Category: Bioinformatics Source Type: research

IR-CNN: Inception residual network for detecting kidney abnormalities from CT images
In this study, we propose an efficient architecture “IR-CNN” based on the Inception residual network for the detection of three major kidney diseases, tumor, kidney stone and cyst, using CT images. We customized the top layer of InceptionResNetV2 and further added global average pooling (GAP), batch normalization (BN), dropout and dense layers wi th swish activation functions to extract robust features, avoid vanishing gradient problems and achieve better accuracy in detecting kidney disease. The proposed IR-CNN model was trained and tested on a publicly available kidney CT dataset with 4000 images using different opti...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 20, 2023 Category: Bioinformatics Source Type: research

Advancements and emerging trends in brain tumor classification using MRI: a systematic review
AbstractBrain tumor (BT) classification plays a crucial role in the diagnosis and treatment of BTs using Magnetic Resonance Imaging (MRI) scans. This systematic review aims to analyze and summarize the existing literature on BT classification using MRI, focusing on the advancements, techniques, and trends in this field. A comprehensive search strategy was employed to identify relevant articles published between 2020 and 2023. The selected articles were analyzed based on their pre-processing techniques, feature extraction methods, segmentation approaches, and classification algorithms. The review highlights the strengths an...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 9, 2023 Category: Bioinformatics Source Type: research

Using nonlinear analysis and neural network to classify bipolar I disorder electroencephalogram signals from normal electroencephalograms
AbstractBipolar I disorder is a severe neuropsychiatric illness that affects many people around the world. Early diagnosis of adolescents with bipolar I disorder is a very challenging task due to its atypical symptoms. Thirty adolescents, including 15 bipolar I disorder patients and 15 healthy adolescents, participated in the study. These participants were subjected to electroencephalography (EEG), and their EEG signals were recorded through 19 Ag/AgCl electrodes in eyes closed at rest. After preprocessing step to noise reduction and artifacts rejection, three nonlinear features from fractal analysis (Higuchi, Katz, and Pe...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 5, 2023 Category: Bioinformatics Source Type: research

COVID-19 lag time and case fatality rate calculation tool, as well as a tool to identify when policymakers made mistakes
AbstractThere is no tool for calculating a case fatality rate (CFR) and a lag time of COVID-19. This paper proposes a new policymaker tool, covidlag for policymakers to calculate the CFR and the lag time with associated relationship from infection to death. The more the infections, the more the COVID-19 deaths. The less the infections, the less the deaths. We took advantage of this correlation between infection and death. In other words, the number of daily cases and that of daily deaths are used for calculating CFRs and lag times in the US. Scoring policies are based on dividing the number of daily cumulative deaths by th...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 5, 2023 Category: Bioinformatics Source Type: research

An exhaustive review of computational prediction techniques for PPI sites, protein locations, and protein functions
AbstractThe field of proteomics encompasses a comprehensive examination of proteins, encompassing their structural properties, interactions with other biomolecules, subcellular localization, functional roles, interaction sites, regions of disorder, and exploring novel protein designs. Each of these domains interlinks, contributing valuable information to the study of each other part. Extensive research in most of these areas has given rise to many more challenges that require further exploration. This review mainly concentrates on prediction approaches for protein –protein interaction sites, protein subcellular locations...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - August 31, 2023 Category: Bioinformatics Source Type: research