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Computer-aided diagnosis of digestive tract tumor based on deep learning for medical images
AbstractWith the continuous development of society, natural pollution and people's unhealthy habits have led to an increasing number of patients with gastrointestinal cancer. As a malignant tumor, if the digestive tract tumor can be extracted and checked out, it will be very helpful to the patient's treatment. But the detection of gastrointestinal tumors is really not easy, so this article hopes that the method based on deep learning artificial intelligence will help the key technology of computer-aided diagnosis of gastrointestinal tumors in medical images. Through research, it is found that as the learning rate alpha inc...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - January 28, 2022 Category: Bioinformatics Source Type: research

Identification of glycophorin C as a prognostic marker for human breast cancer using bioinformatic analysis
AbstractBreast cancer is an expanding threat that leads to many women's death worldwide. Despite the improvement of the early detection methods and treatment, still, there is a high number of breast cancer mortality. To increase patient survival in breast cancer, identifying novel biomarkers is essential for therapeutics targets. The Glycophorin C  (GYPC) gene is correlated with patient survival, which can be a possible biomarker for early detection in breast cancer progression. However, the expression of GYPC is not clearly defined in breast cancer. Here, we widely analyzed the expression pattern of GYPC in breast cancer...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - January 3, 2022 Category: Bioinformatics Source Type: research

Generating novel molecule for target protein (SARS-CoV-2) using drug �target interaction based on graph neural network
AbstractThe transmittable spread of viral coronavirus (SARS-CoV-2) has resulted in a significant rise in global mortality. Due to lack of effective treatment, our aim is to generate a highly potent active molecule that can bind with the protein structure of SARS-CoV-2. Different machine learning and deep learning approaches have been proposed for molecule generation; however, most of these approaches represent the drug molecule and protein structure in 1D sequence, ignoring the fact that molecules are by nature in 3D structure, and because of this many critical properties are lost. In this work, a framework is proposed tha...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 18, 2021 Category: Bioinformatics Source Type: research

Generating novel molecule for target protein (SARS-CoV-2) using drug –target interaction based on graph neural network
AbstractThe transmittable spread of viral coronavirus (SARS-CoV-2) has resulted in a significant rise in global mortality. Due to lack of effective treatment, our aim is to generate a highly potent active molecule that can bind with the protein structure of SARS-CoV-2. Different machine learning and deep learning approaches have been proposed for molecule generation; however, most of these approaches represent the drug molecule and protein structure in 1D sequence, ignoring the fact that molecules are by nature in 3D structure, and because of this many critical properties are lost. In this work, a framework is proposed tha...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 18, 2021 Category: Bioinformatics Source Type: research

Mathematical modeling of the outbreak of COVID-19
AbstractThe novel coronavirus SARS-Cov-2 is a pandemic condition and poses a massive menace to health. The governments of different countries and their various prohibitory steps to restrict the virus ’s expanse have changed individuals’ communication processes. Due to physical and financial factors, the population’s density is more likely to interact and spread the virus. We establish a mathematical model to present the spread of the COVID-19 in India and worldwide. By the simulation proce ss, we find the infected cases, infected fatality rate, and recovery rate of the COVID-19. We validate the model by the rough set...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 10, 2021 Category: Bioinformatics Source Type: research

T-cell epitope-based vaccine designing against Orthohantavirus: a causative agent of deadly cardio-pulmonary disease
AbstractOrthohantavirus, a zoonotic virus responsible for causing human cardio-pulmonary disease, is proven to be a fatal disease. Due to the paucity of regimens to cure the disease and efficient management to eradicate this deadly virus, there is a constant need to expand in-silico approaches belonging to immunology domain to formulate best feasible peptide-based vaccine against it. In lieu of that, we have predicted and validated an epitope of nine-residue-long sequence “MIGLLSSRI”. The predicted epitope has shown best interactions with HLA alleles of MHC Class II proteins, namely HLA DRB1_0101, DRB1_0401, DRB1_0405,...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 7, 2021 Category: Bioinformatics Source Type: research

The impact of pre-clustering on classification of heterogeneous protein data
AbstractThe aim of this paper is to evaluate improvement in the classification of protein sequence data by introducing clustering as a prepossessing step. Clustering analysis was introduced to discover any possible sub-clusters that might have different patterns within the same protein class. A classification learning algorithm is then applied to each cluster to enhance the classification accuracy. Two standard benchmark datasets: caspase 3 human substrates that include cleaved and non-cleaved peptides, and the membrane proteins inner and\(\alpha\)-helical proteins were used to examine the proposed approach. Different desc...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 7, 2021 Category: Bioinformatics Source Type: research

Factors affecting the difference of protein supplements on physical fitness
This study aims to explore the effect of protein supplementation on athletes ’ physical fitness. This article takes 48 male and female athletes as experimental subjects and randomly divides them into 6 groups. The experiment period was 8 days, and each group was supplemented with protein every day. During the experiment, the athlete collected 10 ml of elbow venous blood for the first time in a quiet fasting state in the laboratory at 8 am. Two hours after the main class (after relaxing), go to the laboratory to draw 10 ml of venous blood from the elbow twice until the 8th day. Then, the chemical immunoassay method is ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - December 7, 2021 Category: Bioinformatics Source Type: research

Prediction of suitable T and B cell epitopes for eliciting immunogenic response against SARS-CoV-2 and its mutant
AbstractSpike glycoprotein of SARS-CoV-2 is mainly responsible for the recognition and membrane fusion within the host and this protein has an ability to mutate. Hence, T cell and B cell epitopes were derived from the spike glycoprotein sequence of wild SARS-CoV-2. The proposed T cell and B cell epitopes were found to be antigenic and conserved in the sequence of SARS-CoV-2 mutant (B.1.1.7). Thus, the proposed epitopes are effective against SARS-CoV-2 and its B.1.1.7 mutant. MHC-I that best interacts with the proposed T cell epitopes were found, using immune epitope database. Molecular docking and molecular dynamic simulat...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 26, 2021 Category: Bioinformatics Source Type: research

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens
AbstractCOVID-19 is a pandemic disease caused by novel corona virus, SARS-CoV-2, initially originated from China. In response to this serious life-threatening disease, designing and developing more accurate and sensitive tests are crucial. The aim of this study is designing a multi-epitope of spike and nucleocapsid antigens of COVID-19 virus by bioinformatics methods. The sequences of nucleotides obtained from the NCBI Nucleotide Database. Transmembrane structures of proteins were predicted by TMHMM Server and the prediction of signal peptide of proteins was performed by Signal P Server. B-cell epitopes ’ prediction was ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 25, 2021 Category: Bioinformatics Source Type: research

Empirical mode decomposition based adaptive noise canceller for improved identification of exons in eukaryotes
AbstractIdentification of exons in eukaryotes using DSP techniques is a challenging task in genomic signal processing owing to the low density of coding regions. Although many DSP techniques have been proposed, still fast and accurate identification of exons is a great challenge. In this paper, an empirical mode decomposition (EMD) based adaptive noise canceller (ANC) along with a zero-phase anti-notch filter is proposed for improved identification of exons. An anti-notch filter extracts the period-3 property present in the exons and generates the feature, whereas the EMD-based ANC can remove the 1/f background noise prese...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 24, 2021 Category: Bioinformatics Source Type: research

In silico chemical profiling and identification of neuromodulators from Curcuma amada targeting acetylcholinesterase
AbstractCurcuma amada is a potent medicinal herb with diverse bioactive molecules, demonstrating anti-inflammatory, antihypercholesterolemic, and antioxidant properties, and used conventionally to treat various neurodegenerative diseases, including Alzheimer ’s disease (AD). The present study characterized the secondary metabolites ofCurcuma amada for their drug-likeness properties, identified potent hits by targeting Acetylcholinesterase (AChE). Here in silico ADMET analysis was performed for chemical profiling, while molecular docking and molecular dynamics (MD) simulations were used for hit selection and binding chara...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 7, 2021 Category: Bioinformatics Source Type: research

An effective feature extraction with deep neural network architecture for protein-secondary-structure prediction
AbstractWith the increased importance of proteins in day-to-day life, it is imperative to know the protein functions. Deciphering protein structure elucidates protein functions. Experimental approaches for protein-structure analysis are expensive and time-consuming, and require high dexterity. Thus, finding a viable computational approach is vital. Due to the high complexity of predicting protein structure (tertiary structure) directly, research in this field aims at the protein-secondary-structure prediction which is directly related to its tertiary structure. This research aims at exploring a plethora of features, namely...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 23, 2021 Category: Bioinformatics Source Type: research

Identification of key genes, pathways, and associated comorbidities in chikungunya infection: insights from system biology analysis
AbstractThe chikungunya (CHIKV) viral infection is a global health burden characterized by the neurologic complications with CHIKV infection. CHIKV has relation with Ebola, Dengu, Semlikhi Forest Virus (SLFV) characterized by inflammations in these viral diseases. The present study aimed to discover molecular signatures for comorbidity of viral infections. So, in our study, we have analyzed transcriptome datasets related to viral diseases namely, CHIKV, Ebola, Dengu, SLFV, and inflammatory disorder “Pain” associated with these viral diseases. We built relationship networks based on the CHIKV virus after identifying sha...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 12, 2021 Category: Bioinformatics Source Type: research

Study the inhibitory effect of some plant origin flavonoids against targetable cancer receptors GRP78 by molecular docking
In this study, the molecular docking approach was utilized to assess the effect of 70 flavonoids on the inhibition of glucose-regulated protein 78  kDa (GRP78). Epigallocatechin gallate (EGCG) was employed for the selection of the studied flavonoids based on estimated binding energy. Four compounds, namely, naringin, poncirin, prunin, and epicatechin gallate showed higher affinities to GRP78. The predicted binding energy for these compounds w as respectively − 11.60, − 10.40, − 10.19 and − 10.04 (kcal/mol) in comparison with the value of − 9.86 (kcal/mol) for EGCG. The hydrogen bond with residues Asp39...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 7, 2021 Category: Bioinformatics Source Type: research