Advances in Bioinformatics最新文献

{"title":"Protein Analysis: From Sequence to Structure","authors":"Jaykumar Jani, A. Pappachan","doi":"10.1007/978-981-33-6191-1_4","DOIUrl":"https://doi.org/10.1007/978-981-33-6191-1_4","url":null,"abstract":"","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51133877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics in Personalized Medicine","authors":"G. Krishnan, A. Joshi, V. Kaushik","doi":"10.1007/978-981-33-6191-1_15","DOIUrl":"https://doi.org/10.1007/978-981-33-6191-1_15","url":null,"abstract":"","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51132914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soft Computing in Bioinformatics","authors":"V. Srivastava","doi":"10.1007/978-981-33-6191-1_23","DOIUrl":"https://doi.org/10.1007/978-981-33-6191-1_23","url":null,"abstract":"","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51133300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis.","authors":"Ammar I Shihab,&nbsp;Faten A Dawood,&nbsp;Ali H Kashmar","doi":"10.1155/2020/3407907","DOIUrl":"https://doi.org/10.1155/2020/3407907","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.</p>","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2020 ","pages":"3407907"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2020/3407907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37923573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach","authors":"Sabeena Mustafa, H. Balkhy, M. Gabere","doi":"10.1155/2019/6815105","DOIUrl":"https://doi.org/10.1155/2019/6815105","url":null,"abstract":"There is no effective therapeutic or vaccine for Middle East Respiratory Syndrome and this study attempts to find therapy using peptide by establishing a basis for the peptide-protein interactions through in silico docking studies for the spike protein of MERS-CoV. The antimicrobial peptides (AMPs) were retrieved from the antimicrobial peptide database (APD3) and shortlisted based on certain important physicochemical properties. The binding mode of the shortlisted peptides was measured based on the number of clusters which forms in a protein-peptide docking using Piper. As a result, we identified a list of putative AMPs which binds to the spike protein of MERS-CoV, which may be crucial in providing the inhibitory action. It is observed that seven putative peptides have good binding score based on cluster size cutoff of 208. We conclude that seven peptides, namely, AP00225, AP00180, AP00549, AP00744, AP00729, AP00764, and AP00223, could possibly have binding with the active site of the MERS-CoV spike protein. These seven AMPs could serve as a therapeutic option for MERS and enhance its treatment outcome.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/6815105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48606490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Silico Screening of Aptamers Configuration against Hepatitis B Surface Antigen","authors":"Mohamad Zulkeflee Sabri, Azzmer Azzar Abdul Hamid, S. Hitam, Mohd. Zulkhairi Abdul Rahim","doi":"10.1155/2019/6912914","DOIUrl":"https://doi.org/10.1155/2019/6912914","url":null,"abstract":"Aptamer has been long studied as a substitute of antibodies for many purposes. However, due to the exceeded length of the aptamers obtained in vitro, difficulties arise in its manipulation during its molecular conjugation on the matrix surfaces. Current study focuses on computational improvement for aptamers screening of hepatitis B surface antigen (HBsAg) through optimization of the length sequences obtained from SELEX. Three original aptamers with affinity against HBsAg were truncated into five short hairpin structured aptamers and their affinity against HBsAg was thoroughly studied by molecular docking, molecular dynamics (MD) simulation, and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method. The result shows that truncated aptamers binding on HBsAg “a” determinant region are stabilized by the dynamic H-bond formation between the active binding residues and nucleotides. Amino acids residues with the highest hydrogen bonds hydrogen bond interactions with all five aptamers were determined as the active binding residues and further characterized. The computational prediction of complexes binding will include validations through experimental assays in future studies. Current study will improve the current in vitro aptamers by minimizing the aptamer length for its easy manipulation.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2019 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/6912914","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46239624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Deleterious nsSNPs within <i>MEFV</i> Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis.","authors":"Mujahed I Mustafa,&nbsp;Tebyan A Abdelhameed,&nbsp;Fatima A Abdelrhman,&nbsp;Soada A Osman,&nbsp;Mohamed A Hassan","doi":"10.1155/2019/1651587","DOIUrl":"https://doi.org/10.1155/2019/1651587","url":null,"abstract":"<p><strong>Background: </strong>Familial Mediterranean Fever (FMF) is the most common autoinflammatory disease (AID) affecting mainly the ethnic groups originating from Mediterranean basin. We aimed to identify the pathogenic SNPs in MEFV by computational analysis software.</p><p><strong>Methods: </strong>We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function.</p><p><strong>Result: </strong>23 novel mutations out of 857 nsSNPs are found to have deleterious effect on the MEFV structure and function.</p><p><strong>Conclusion: </strong>This is the first in silico analysis of MEFV gene to prioritize SNPs for further genetic mapping studies. After using multiple bioinformatics tools to compare and rely on the results predicted, we found 23 novel mutations that may cause FMF disease and it could be used as diagnostic markers for Mediterranean basin populations.</p>","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2019 ","pages":"1651587"},"PeriodicalIF":0.0,"publicationDate":"2019-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/1651587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37392492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.","authors":"Amina Adadi, Safae Adadi, Mohammed Berrada","doi":"10.1155/2019/1870975","DOIUrl":"10.1155/2019/1870975","url":null,"abstract":"<p><p>Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues.</p>","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2019 ","pages":"1870975"},"PeriodicalIF":0.0,"publicationDate":"2019-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37220365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoinformatics Approach for Multiepitopes Vaccine Prediction against Glycoprotein B of Avian Infectious Laryngotracheitis Virus.","authors":"Sumaia A Ali,&nbsp;Yassir A Almofti,&nbsp;Khoubieb A Abd-Elrahman","doi":"10.1155/2019/1270485","DOIUrl":"https://doi.org/10.1155/2019/1270485","url":null,"abstract":"<p><p>Infectious laryngotracheitis virus (ILTV) is a <i>gallid herpesvirus type 1</i>, a member of the genus <i>Iltovirus</i>. It causes an infection in the upper respiratory tract mainly trachea which results in significant economic losses in the poultry industry worldwide. Vaccination against ILTV produced latent infected carriers' birds, which become a source of virus transmission to nonvaccinated flocks. Thus this study aimed to design safe multiepitopes vaccine against glycoprotein B of ILT virus using immunoinformatic tools. Forty-four sequences of complete envelope glycoprotein B were retrieved from GenBank of National Center for Biotechnology Information (NCBI) and aligned for conservancy by multiple sequence alignment (MSA). Immune Epitope Database (IEDB) analysis resources were used to predict and analyze candidate epitopes that could act as a promising peptide vaccine. For B cell epitopes, thirty-one linear epitopes were predicted using Bepipred. However eight epitopes were found to be on both surface and antigenic epitopes using Emini surface accessibility and antigenicity, respectively. Three epitopes ( _<i>190</i> <i>KKLP</i> _<i>193</i> , _<i>386</i> <i>YSSTHVRS</i> _<i>393</i> , and _<i>317</i> <i>KESV</i> _<i>320</i> ) were proposed as B cell epitopes. For T cells several epitopes were interacted with MHC class I with high affinity and specificity, but the best recognized epitopes were ₁₁₈ <i>YVFNVTLYY</i> _<i>126</i> , ₃₃₅ <i>VSYKNSYHF</i> _<i>343</i> , and _<i>622</i> <i>YLLYEDYTF</i> _<i>630</i> . MHC-II binding epitopes, ₃₀₁ <i>FLTDEQFTI</i> _<i>309</i> , _<i>277</i> <i>FLEIANYQV</i> _<i>285</i> , and _<i>743</i> <i>IASFLSNPF</i> _<i>751</i> , were proposed as promising epitopes due to their high affinity for MHC-II molecules. Moreover the docked ligand epitopes from MHC-1 molecule exhibited high binding affinity with the receptors; BF chicken alleles (BF2 2101 and 0401) expressed by the lower global energy of the molecules. In this study nine epitopes were predicted as promising vaccine candidate against ILTV. <i>In vivo</i> and <i>in vitro</i> studies are required to support the effectiveness of these predicted epitopes as a multipeptide vaccine through clinical trials.</p>","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2019 ","pages":"1270485"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/1270485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37174880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Deleterious nsSNPs within MEFV Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis","authors":"Mujahed I. Mustafa, Tebyan A. Abdelhameed, F. A. Abdelrhman, Soada A. Osman, Mohamed A. Hassan","doi":"10.1101/424796","DOIUrl":"https://doi.org/10.1101/424796","url":null,"abstract":"Background Familial Mediterranean Fever (FMF) is the most common auto inflammatory disease (AID) affecting mainly the ethnic groups originating from Mediterranean basin, we aimed to identify the pathogenic SNPs in MEFV by computational analysis software. Methods We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function. Result 23 novel mutations out of 857 nsSNPs that are found to be deleterious effect on the MEFV structure and function. Conclusion This is the first in silico analysis in MEFV gene to prioritize SNPs for further genetic mapping studies. After using multiple bioinformatics tools to compare and rely on the results predicted, we found 23 novel mutations that may cause FMF disease and it could be used as diagnostic markers for Mediterranean basin populations.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2019 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42775449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}