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Detecting disease-related SNP loci based on GSP
AbstractA large number of studies have shown that susceptibility to some diseases may be related to some SNP (Single Nucleotide Polymorphism) loci. Accurate location of disease-related SNP loci can help people understand the pathogenesis of diseases and prevent them from happening. Based on Graph Signal Processing (GSP) theory, this paper proposes a novel method called GSP to detect disease-related SNP loci. GSP method is divided into five steps. The first step is to establish a graph signal model of all SNP loci. The second step is to extract the high-frequency components of graph signal with a high-pass filter. The third...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - July 1, 2020 Category: Bioinformatics Source Type: research

Molecular docking study of the anticandida activity some schiff bases and their complexes
AbstractThe research study entails the molecular docking study of some recently designed Schiff base complexes and their ligands (Schiff base) on two receptors, a crystal structure ofCandida albicans Mep2 (5AF1) and a thiamin pyrophosphokinase fromCandida albicans (2G9Z). These two receptors were used to investigate the inhibitory potentials of some Schiff bases on candida albicans when coupled with a central metal atom. The docking results for 2G9Z the complex A3 had the best binding affinity of 21.473  kcal/mol, while for 5AF1 the complex A4 best inhibits the crystal structure with a binding affinity of 23.518 kcal/mol...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 29, 2020 Category: Bioinformatics Source Type: research

A principal component regression model for predicting phytochemical binding to the H. pylori CagA protein
In this study, a predictive model for the binding free energy of natural compounds towards the cagA protein is presented. The formulated model which is built on principal component —multiple linear regression demonstrates reliable accuracy (r2test = 0.92, RMSEtest = 0.483), while only requiring five independent variables for the prediction. It was further noted that topological descriptors had the greatest influence on the generated principal components which served as the predictors. The created regression model can help promote and accelerate the disco very of natural compounds as cagA binders for the developme...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 24, 2020 Category: Bioinformatics Source Type: research

Correction to: Process mining project methodology in healthcare: a case study in a tertiary hospital
The acknowledgment section has been published missing some names in the online published article. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 21, 2020 Category: Bioinformatics Source Type: research

A novel approach to identify subtype-specific network biomarkers of breast cancer survivability
ConclusionThis method can be used to identify the survivability of breast cancer patients using gene relationship networks. It has high prediction performance, including specificity and sensitivity for both cohorts of survivals and deceased. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 19, 2020 Category: Bioinformatics Source Type: research

Enhancing convolution-based sentiment extractor via dubbed N-gram embedding-related drug vocabulary
AbstractEveryday patients ’ narratives on social media can reveal crucial public health issues. Mining those online narratives, which remained so far unconsidered, may mirror further hidden patient health status. Deep learning-based sentiment analysis (SA) approaches broadly focus on grammar directions such as semantic dir ection or only center on extract sentiment words. They provide both richer representation capabilities and better performance but do not consider the related medication concepts. As a result, the inaccurate recognition of related drug entities may seriously fail to retrieve the relevant sentiment ex pr...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 16, 2020 Category: Bioinformatics Source Type: research

A regression-based model for predicting the best mode of treatment for Egyptian liver cancer patients
In this study, we analyzed the data of 1427 Egyptian patients with liver cancer who were either treated by one of five different treatment methods or not treated. We proposed and compared between two pipelines, a Single-Model pipeline and a Multi-Model pipeline, for analyzing th e patient’s clinical and genetic data to recommend the best liver cancer treatment and, therefore, potentially improve their prognosis. We studied the performance of six regression methods in predicting the outcome of the treatment modalities for liver cancer patients. The best performing method w as used in building the models in the proposed pi...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 10, 2020 Category: Bioinformatics Source Type: research

Evaluation of the prognostic significance of CDK6 in breast cancer
AbstractCyclin-dependent kinase (CDK) gene family is an important cell cycle progression regulator, which may function as a tumor suppressor. We performed a systematic onco-informatics analysis to investigate the prognostic value ofCDK6 in breast cancer.CDK6 expression was evaluated using the Oncomine and UALCAN database. The relevance betweenCDK6 level and clinical parameters and survival data in breast cancer was analyzed using the bc-GenExMiner, and Kaplan –meier plotter databases. The methylation status and heat map ofCDK6 were analyzed by utilizing the UCSC Xena and UALCAN. It was identified thatCDK6 was under-expre...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 10, 2020 Category: Bioinformatics Source Type: research

In silico vaccine design against Chlamydia trachomatis infection
This study identifies the putative vaccine candidates that are membrane bound with high antigenicity properties; antigenicity induces the immunogenicity which involves identification of T-cell and B-cell epitopes that induce both humoral and cell-mediated immunity. The epitopes ‘LSWEMELAY’, ‘LSNTEGYRY’, ‘TSDLGQMEY’, ‘FIDLLQAIY’ and ‘FSNNFSDIY’ were predicted as core sequences for class I MHC molecules. The identified epitopes showed promising ability to interact with the human leukocyte antigens (HLA). These epitopes showed maximum population coverage w ith epitope conservancy above 80%. Molecular docki...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 9, 2020 Category: Bioinformatics Source Type: research

Identification of Multiple Sclerosis lesion subtypes and their quantitative assessments with EDSS using neuroimaging
This article presents a technique to classify MS lesion subtypes in MR images and then evaluates the correlation between lesion subtypes and Expanded Disability Status Scale (EDSS). The technique used textural features based on the Gray Level Co-occurrence Matrix (GLCM) and histogram information (mean and variance) to describe each lesion and normal tissue of FLAIR Images. Then it discriminated them with Support Vector Machine (SVM). A comprehensive post-processing module improved the quality of segmentation. We found corresponding areas in T2-weighted (T2-w), T1-weighted (T1-w) called black holes, and T1-weighted enhancin...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 7, 2020 Category: Bioinformatics Source Type: research

Machine learning approach for wart treatment selection: prominence on performance assessment
AbstractWarts are benign tumors, caused by human papillomavirus (HPV). The present study mainly emphasis on the selection of suitable methods for the removal of a common and plantar wart. There are numerous wart treatment methods for this disease, among them cryotherapy and immunotherapy are well-known approaches. Identifying the suitable wart treatment method manually is quite challenging. Moreover, existing machine learning (ML) techniques show a poor prediction accuracy towards the selection of wart treatment method, however, the prediction accuracy is not satisfactory and can be further improved. To achieve the same, t...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 2, 2020 Category: Bioinformatics Source Type: research

Using Twitter for diabetes community analysis
AbstractSocial media platforms have become a common venue for sharing experiences and knowledge about health-related topics. This research focuses on examining social media-based communication patterns related to diabetes on the Twitter platform. Specifically, we apply an updated methodology to examine changes in the current use of hash-tags, trending hash-tags, and the frequency of diabetes-related tweets using a previous study as a baseline. Our results show significant growth in the diabetes community on Twitter over time and also evidence that this community is increasing in its capacity to spread awareness regarding d...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - June 1, 2020 Category: Bioinformatics Source Type: research

Impact of deep sequencing on hepatocellular carcinoma utilizing high-throughput technology
In this study, next-generation sequence (NGS) is utilized by applying OncoSNP-SEQ technique to a number of human chromosomes for analyzing hepatic cancer data that identify genome-wide mutations in copy number of the genomic information data. The outcomes referred to a certain number of chromosome aberrations detected with significant genes such as: SHC, TCP1, CCT3,SHC1, EPHA5, UGT2B28, UBE1L2, and also strike (CREB3L4, RAB1, MAGI2) genes which are discovered lately in 2013, tumor suppressorsSHC1 andCKS1B, LRP1B, as well as oncogeneUBE1L2, all of which may play a central role in cancer cell survival during the progress of ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - May 25, 2020 Category: Bioinformatics Source Type: research

Breast cancer classification with reduced feature set using association rules and support vector machine
AbstractIn the last few years, machine learning is one of the driving forces of science and industry, but increasing of data requires paradigm shifts in traditional methods in the application of machine learning techniques on this data especially in healthcare field. Furthermore, with the availability of different clinical technologies, tumor features have been collected for breast cancer classification. Therefore, feature selection and accuracy improvement have become a challenging and time-consuming task. In this paper, the proposed approach has two stages. In the first, Association Rules (AR) are used to eliminate insig...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - May 20, 2020 Category: Bioinformatics Source Type: research

A real-time biosurveillance mechanism for early-stage disease detection from microblogs: a case study of interconnection between emotional and climatic factors related to migraine disease
This study paves the way to discover disease-related features using both emotional and climatic factors. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - May 13, 2020 Category: Bioinformatics Source Type: research