Shaikh Farhad Hossain, Ming Huang, N. Ono, S. Kanaya, M. Altaf-Ul-Amin
A biomarker (short for biological marker) is a medical sign of a disease or condition which indicates a normal or abnormal state of a body. The biomarker is a key factor in the analysis of diseases and also for analyzing inter disease relations. In the previous study, we designed and developed a human biomarker (metabolites and proteins) database and the database is currently available online. This work was supported by the Ministry of Education, Japan and NAIST Big Data Project. We have used our previously developed database and collected 486 human biomarkers and their respective diseases. We determined the similarity among NCBI disease classes based on associated biomarker fingerprints. For this purpose, we collected biomarker PubChem IDs and using them downloaded the SDF files in a batch, then with those molecular description files determined their atom pair fingerprints using ChemmineR package. We constructed a network of biomarkers based on Tanimoto similarity between their fingerprints and applied DPclusO algorithm to find clusters consisting of biomarkers with similar chemical structures. We also conducted hierarchical clustering of the biomarkers. We categorized all the diseases in our data into 18 NCBI disease classes. Combining all information, we finally determined inter disease relations based on structural similarity between biomarkers.
{"title":"Inter Disease Relations Based on Human Biomarkers by Network Analysis","authors":"Shaikh Farhad Hossain, Ming Huang, N. Ono, S. Kanaya, M. Altaf-Ul-Amin","doi":"10.1109/BIBE.2019.00027","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00027","url":null,"abstract":"A biomarker (short for biological marker) is a medical sign of a disease or condition which indicates a normal or abnormal state of a body. The biomarker is a key factor in the analysis of diseases and also for analyzing inter disease relations. In the previous study, we designed and developed a human biomarker (metabolites and proteins) database and the database is currently available online. This work was supported by the Ministry of Education, Japan and NAIST Big Data Project. We have used our previously developed database and collected 486 human biomarkers and their respective diseases. We determined the similarity among NCBI disease classes based on associated biomarker fingerprints. For this purpose, we collected biomarker PubChem IDs and using them downloaded the SDF files in a batch, then with those molecular description files determined their atom pair fingerprints using ChemmineR package. We constructed a network of biomarkers based on Tanimoto similarity between their fingerprints and applied DPclusO algorithm to find clusters consisting of biomarkers with similar chemical structures. We also conducted hierarchical clustering of the biomarkers. We categorized all the diseases in our data into 18 NCBI disease classes. Combining all information, we finally determined inter disease relations based on structural similarity between biomarkers.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121131131","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}
Thodoris Koutsandreas, Ajdini Bajram, C. Mastrokalou, E. Pilalis, A. Chatziioannou, Ilias Maglogiannis
The revolution of single-cell technologies established a novel framework to investigate gene expression profiles in the level of individual cells. Scientists are able to investigate the biological variability of the same tissue, producing isolated transcriptomic data for each single cell. As a result, each transcriptomic experiment could extract a unique expression profile for each cell, posing new challenges in the translation analysis of all these profiles. Pathway analysis tools need to be adapted, not only to analyze simultaneously numerous gene expression profiles, but also to compare them, detecting functional differences and commonalities among the cells of the same issue, separating them to functional subclusters. In this study, we used the output of a single-cell experiment in the hematopoietic system, in order to determine a novel framework for the functional comparison of single cells, based on their pathway analysis with Gene Ontology annotation. Thousands of expression profiles of single cells, congregated in 15 different hematopoietic classes, were translated into networks of significant biological mechanisms, through the use of BioInfoMiner platform. We propose a novel framework to exploit these results and construct appropriate feature spaces of functional omponents, with a view to perform supervised learning to different hematopoietic cell types and separate their respective single cells, according to their functional profile. The constructed classification model performed interestingly high precision and sensitivity scores for some cell types, while the overall performance needs to be improved with further conceptual and technical refinements.
{"title":"Combining Pathway Analysis and Supervised Machine Learning for the Functional Classification of Single-Cell Transcriptomic Data","authors":"Thodoris Koutsandreas, Ajdini Bajram, C. Mastrokalou, E. Pilalis, A. Chatziioannou, Ilias Maglogiannis","doi":"10.1109/BIBE.2019.00160","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00160","url":null,"abstract":"The revolution of single-cell technologies established a novel framework to investigate gene expression profiles in the level of individual cells. Scientists are able to investigate the biological variability of the same tissue, producing isolated transcriptomic data for each single cell. As a result, each transcriptomic experiment could extract a unique expression profile for each cell, posing new challenges in the translation analysis of all these profiles. Pathway analysis tools need to be adapted, not only to analyze simultaneously numerous gene expression profiles, but also to compare them, detecting functional differences and commonalities among the cells of the same issue, separating them to functional subclusters. In this study, we used the output of a single-cell experiment in the hematopoietic system, in order to determine a novel framework for the functional comparison of single cells, based on their pathway analysis with Gene Ontology annotation. Thousands of expression profiles of single cells, congregated in 15 different hematopoietic classes, were translated into networks of significant biological mechanisms, through the use of BioInfoMiner platform. We propose a novel framework to exploit these results and construct appropriate feature spaces of functional omponents, with a view to perform supervised learning to different hematopoietic cell types and separate their respective single cells, according to their functional profile. The constructed classification model performed interestingly high precision and sensitivity scores for some cell types, while the overall performance needs to be improved with further conceptual and technical refinements.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127203386","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}
The therapeutic effects of acupoint-treatment for some diseases have been confirmed in many scientific experiments. Compared to the use of drugs, acupoint-based treatment can effectively alleviate some diseases without the side effect. Therefore, understanding the relationship between acupoints and diseases is important. In our work, we compile a database about diseases and their corresponding acupoints from a large number of books and research papers. We analyze the disease-acupoint correlation using these data and visualize their connections in an interactive way.
{"title":"Visualizing the Associations between Acupoints Based on Diseases They Treat","authors":"Kun-Chan Lan, Jun-Xiang Zhang, Ying-Hsiu Lin","doi":"10.1109/BIBE.2019.00174","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00174","url":null,"abstract":"The therapeutic effects of acupoint-treatment for some diseases have been confirmed in many scientific experiments. Compared to the use of drugs, acupoint-based treatment can effectively alleviate some diseases without the side effect. Therefore, understanding the relationship between acupoints and diseases is important. In our work, we compile a database about diseases and their corresponding acupoints from a large number of books and research papers. We analyze the disease-acupoint correlation using these data and visualize their connections in an interactive way.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126879741","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}
M. Astrinaki, A. Kanterakis, H. Latsoudis, G. Potamias, D. Kafetzopoulos
Over the last 10 years, Next-Generation Sequencing (NGS) has become a powerful tool in clinical genetics and precision medicine. Techniques like Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and Target Sequencing are commonly used for the elucidation of common and rare variants in mendelian and complex diseases. One of the most vital parts of NGS pipelines is the prioritization of annotated variants according to their clinical significance. During this process, a clinical geneticist is presented with annotation information from external databases for each of the thousands of potential variants. The vast amounts of data and the vague nature of existing guidelines for variant reporting, like ACMG (American College of Medical Genetics) can make this procedure very cumbersome and time consuming. Here we present the main computational challenges and existing solutions for this task. We also present Zazz, an online environment for variant annotation, query and exploration. Zazz can efficiently support the submission of complex and dynamically generated queries to hundreds of millions of variants each having hundreds of annotation fields. Zazz also leverages the capabilities of modern browsers to dynamically filter, explore and visualize multidimensional data.
{"title":"Zazz: Variant Annotation and Exploration of Next Generation Sequencing Variants","authors":"M. Astrinaki, A. Kanterakis, H. Latsoudis, G. Potamias, D. Kafetzopoulos","doi":"10.1109/BIBE.2019.00159","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00159","url":null,"abstract":"Over the last 10 years, Next-Generation Sequencing (NGS) has become a powerful tool in clinical genetics and precision medicine. Techniques like Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and Target Sequencing are commonly used for the elucidation of common and rare variants in mendelian and complex diseases. One of the most vital parts of NGS pipelines is the prioritization of annotated variants according to their clinical significance. During this process, a clinical geneticist is presented with annotation information from external databases for each of the thousands of potential variants. The vast amounts of data and the vague nature of existing guidelines for variant reporting, like ACMG (American College of Medical Genetics) can make this procedure very cumbersome and time consuming. Here we present the main computational challenges and existing solutions for this task. We also present Zazz, an online environment for variant annotation, query and exploration. Zazz can efficiently support the submission of complex and dynamically generated queries to hundreds of millions of variants each having hundreds of annotation fields. Zazz also leverages the capabilities of modern browsers to dynamically filter, explore and visualize multidimensional data.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126114005","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}
In this paper, we present a new pain detection approach that analyzes the electroencephalography (EEG) signals using a quadratic time-frequency distribution (QTFD), namely the Wigner-Ville distribution (WVD). The use of the WVD enables to construct a time-frequency representation (TFR) of the EEG signals that characterizes the time-varying spectral components of the EEG signals. To reduce the dimensionality of the constructed WVD-based TFR of the EEG signals, we have extracted 12 time-frequency features that quantify the energy distribution of the EEG signals in the constructed WVD-based TFR. The extracted time-frequency features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To assess the performance of our proposed pain detection approach, we have recorded the EEG signals for 24 participants under tonic cold pain stimulus. The experimental results show that our proposed approach achieved an average classification accuracy of 83.4% in distinguishing between the no-pain and pain classes.
{"title":"A Time-Frequency Distribution Based Approach for Detecting Tonic Cold Pain using EEG Signals","authors":"R. Alazrai, Saifaldeen Al-Rawi, M. Daoud","doi":"10.1109/BIBE.2019.00112","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00112","url":null,"abstract":"In this paper, we present a new pain detection approach that analyzes the electroencephalography (EEG) signals using a quadratic time-frequency distribution (QTFD), namely the Wigner-Ville distribution (WVD). The use of the WVD enables to construct a time-frequency representation (TFR) of the EEG signals that characterizes the time-varying spectral components of the EEG signals. To reduce the dimensionality of the constructed WVD-based TFR of the EEG signals, we have extracted 12 time-frequency features that quantify the energy distribution of the EEG signals in the constructed WVD-based TFR. The extracted time-frequency features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To assess the performance of our proposed pain detection approach, we have recorded the EEG signals for 24 participants under tonic cold pain stimulus. The experimental results show that our proposed approach achieved an average classification accuracy of 83.4% in distinguishing between the no-pain and pain classes.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905069","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}
P. Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou
Detection of cell nuclei in microscopy images is a challenging research topic due to limitations in acquired image quality as well as due to the diversity of nuclear morphology. This has been a topic of enduring interest with promising success shown by deep learning methods. Recently, attention gating methods have been proposed and employed successfully in a diverse array of pattern recognition tasks. In this work, we introduce a novel attention module and integrate it with feature pyramid networks and the state-of-the-art Mask R-CNN network. We show with numerical experiments that the proposed model outperforms the state-of-the-art baseline.
{"title":"Nuclei Detection Using Residual Attention Feature Pyramid Networks","authors":"P. Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou","doi":"10.1109/BIBE.2019.00028","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00028","url":null,"abstract":"Detection of cell nuclei in microscopy images is a challenging research topic due to limitations in acquired image quality as well as due to the diversity of nuclear morphology. This has been a topic of enduring interest with promising success shown by deep learning methods. Recently, attention gating methods have been proposed and employed successfully in a diverse array of pattern recognition tasks. In this work, we introduce a novel attention module and integrate it with feature pyramid networks and the state-of-the-art Mask R-CNN network. We show with numerical experiments that the proposed model outperforms the state-of-the-art baseline.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019235","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}
Electrical bioimpedance is a promising in vivo tissue characterization method. To develop optimized electronic instrumentation, knowledge of the electrical characteristics of the bioimpedance sensor and the targeted tissue are essential. This paper presents novel results from the characterization of a tetrapolar bioimpedance sensor for intestinal intraluminal mucosal ischemia assessment fabricated using flexible printed circuit (FPC) technology. The electrode impedance is measured individually and in pairs in saline solutions and equivalent circuits are proposed. The sensor is subsequently assessed in tetrapolar impedance measurements in saline solutions to extract experimentally the geometrical cell constant of the device. Finally, in vitro tetrapolar measurements from porcine intraluminal intestinal tissue are presented. The electrode impedance was found to be 145 ± 42 kΩ, while the tissue between 1.77 and 2.06 kΩ at 20 Hz. This work allows the design of next generation optimized CMOS instrumentation for implantable bioimpedance measurements for the particular application and sensor.
{"title":"Characterization and Modeling of a Flexible Tetrapolar Bioimpedance Sensor and Measurements of Intestinal Tissues","authors":"P. Kassanos, F. Seichepine, Guang-Zhong Yang","doi":"10.1109/BIBE.2019.00129","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00129","url":null,"abstract":"Electrical bioimpedance is a promising in vivo tissue characterization method. To develop optimized electronic instrumentation, knowledge of the electrical characteristics of the bioimpedance sensor and the targeted tissue are essential. This paper presents novel results from the characterization of a tetrapolar bioimpedance sensor for intestinal intraluminal mucosal ischemia assessment fabricated using flexible printed circuit (FPC) technology. The electrode impedance is measured individually and in pairs in saline solutions and equivalent circuits are proposed. The sensor is subsequently assessed in tetrapolar impedance measurements in saline solutions to extract experimentally the geometrical cell constant of the device. Finally, in vitro tetrapolar measurements from porcine intraluminal intestinal tissue are presented. The electrode impedance was found to be 145 ± 42 kΩ, while the tissue between 1.77 and 2.06 kΩ at 20 Hz. This work allows the design of next generation optimized CMOS instrumentation for implantable bioimpedance measurements for the particular application and sensor.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134084982","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}
H. Tibble, A. Chan, E. Mitchell, R. Horne, M. Mizani, A. Sheikh, A. Tsanas
Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).
{"title":"Heterogeneity in Asthma Medication Adherence Measurement","authors":"H. Tibble, A. Chan, E. Mitchell, R. Horne, M. Mizani, A. Sheikh, A. Tsanas","doi":"10.1109/BIBE.2019.00168","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00168","url":null,"abstract":"Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"211 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134094164","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}
M. Zanti, M. Loizidou, M. Zachariou, K. Michailidou, K. Kyriacou, A. Hadjisavvas, G. Spyrou
The evolution of Next Generation Sequencing (NGS) technologies represents a significant advancement in the field of molecular genetics and has set the ground, for the discovery of novel variants which cannot be easily classified as deleterious or neutral. In-vitro and in-vivo characterization of these variants of uncertain clinical significance (VUS) should be followed; however, it is often not feasible to carry out the experimental interpretation for every single VUS. In silico tools have been crucial for the prediction of the impact of VUS on protein structure, stability and function. Our aim was to combine computational approaches to investigate the impact of VUS identified in a cohort of Cypriot Triple-Negative Breast Cancer (TNBC) patients by NGS. Using a combination of structural, functional and network-based bioinformatics approaches for the classification of a nonsense PRF1 mutation in association with BC susceptibility, we propose a possible triggered interaction of the mutant PRF1 protein with the CDKN2A protein, a product of a BC susceptibility gene. Additionally, our results support that the increased probability of interaction of the mutant counterpart of perforin with its top 10 predicted interactors, could play an important role in the obstruction of cellular processes related to carcinogenesis such as cell death, necrosis, DNA damage, immortality, UV stress, DNA repair and cell cycle control. We conclude that probably the nonsense PRF1 mutation could be associated with BC predisposition. However, although in silico tools provide an important tool for the interpretation of VUS, functional studies, co-segregation analyses and/or case-control association studies are needed to draw conclusions on variant classification.
{"title":"In Silico Assessment of the Structural, Functional and Stability Impact of a Nonsense PRF1 Mutation with Uncertain Clinical Significance; Identified in 2 Unrelated Cypriot Triple-Negative Breast Cancer Patients.","authors":"M. Zanti, M. Loizidou, M. Zachariou, K. Michailidou, K. Kyriacou, A. Hadjisavvas, G. Spyrou","doi":"10.1109/BIBE.2019.00040","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00040","url":null,"abstract":"The evolution of Next Generation Sequencing (NGS) technologies represents a significant advancement in the field of molecular genetics and has set the ground, for the discovery of novel variants which cannot be easily classified as deleterious or neutral. In-vitro and in-vivo characterization of these variants of uncertain clinical significance (VUS) should be followed; however, it is often not feasible to carry out the experimental interpretation for every single VUS. In silico tools have been crucial for the prediction of the impact of VUS on protein structure, stability and function. Our aim was to combine computational approaches to investigate the impact of VUS identified in a cohort of Cypriot Triple-Negative Breast Cancer (TNBC) patients by NGS. Using a combination of structural, functional and network-based bioinformatics approaches for the classification of a nonsense PRF1 mutation in association with BC susceptibility, we propose a possible triggered interaction of the mutant PRF1 protein with the CDKN2A protein, a product of a BC susceptibility gene. Additionally, our results support that the increased probability of interaction of the mutant counterpart of perforin with its top 10 predicted interactors, could play an important role in the obstruction of cellular processes related to carcinogenesis such as cell death, necrosis, DNA damage, immortality, UV stress, DNA repair and cell cycle control. We conclude that probably the nonsense PRF1 mutation could be associated with BC predisposition. However, although in silico tools provide an important tool for the interpretation of VUS, functional studies, co-segregation analyses and/or case-control association studies are needed to draw conclusions on variant classification.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133191108","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}
Usage of different bio materials for dental implants have come a long way since its introduction. Progressive researches made over past few decades evolved new bio materials enabling optimal utilization of implants by exploiting its material characteristics to its fullest. The aim of identifying new bio materials is to obviate the chances of biological rejection and enhance its utility. This article is aimed to present a consolidated review on various dental bio materials explored since 2011 till date.
{"title":"Evolution of BioMaterials for Dental Implants and Futuristic Developments","authors":"T. Sengupta, P. Muthu","doi":"10.1109/BIBE.2019.00118","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00118","url":null,"abstract":"Usage of different bio materials for dental implants have come a long way since its introduction. Progressive researches made over past few decades evolved new bio materials enabling optimal utilization of implants by exploiting its material characteristics to its fullest. The aim of identifying new bio materials is to obviate the chances of biological rejection and enhance its utility. This article is aimed to present a consolidated review on various dental bio materials explored since 2011 till date.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123880373","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}
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