C. Farmaki, M. Krana, M. Pediaditis, Emmanouil G Spanakis, V. Sakkalis
Brain computer interfaces (BCIs) that are focused on navigation applications have been developed for patients suffering from severe paralysis to offer a means of autonomy. In SSVEP-based BCIs, users focus their gaze on flickering targets, which correspond to specific commands. Besides the accuracy of the target identification, several additional aspects are important for the development of a practical and useful BCI, such as low cost, ease of use and robustness to everyday-life conditions. In a previous paper, we presented an SSVEP-based BCI for remote robotic car navigation offering live camera feedback. In this paper, we further improve our implementation by adding a fourth direction (backwards), while redeveloping the software in a free-license environment (Python) using a smaller and lighter robotic car, easier to maneuver in interior spaces. Additionally, we study the possibility of using a single channel EEG and test the performance of our system in an offline session, as well as in an online realistic navigation in a predefined remote route. A total of 14 participants achieved an average offline accuracy of 81%, an average offline ITR of 117.1 bits/min and an average online completion time ratio (BCI completion time against optimal button completion time) of 2.27. All of the participants managed to finish the route under realistic conditions which indicates that our system has the potential to be integrated in the everyday life of immobilized patients.
{"title":"Single-Channel SSVEP-Based BCI for Robotic Car Navigation in Real World Conditions","authors":"C. Farmaki, M. Krana, M. Pediaditis, Emmanouil G Spanakis, V. Sakkalis","doi":"10.1109/BIBE.2019.00120","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00120","url":null,"abstract":"Brain computer interfaces (BCIs) that are focused on navigation applications have been developed for patients suffering from severe paralysis to offer a means of autonomy. In SSVEP-based BCIs, users focus their gaze on flickering targets, which correspond to specific commands. Besides the accuracy of the target identification, several additional aspects are important for the development of a practical and useful BCI, such as low cost, ease of use and robustness to everyday-life conditions. In a previous paper, we presented an SSVEP-based BCI for remote robotic car navigation offering live camera feedback. In this paper, we further improve our implementation by adding a fourth direction (backwards), while redeveloping the software in a free-license environment (Python) using a smaller and lighter robotic car, easier to maneuver in interior spaces. Additionally, we study the possibility of using a single channel EEG and test the performance of our system in an offline session, as well as in an online realistic navigation in a predefined remote route. A total of 14 participants achieved an average offline accuracy of 81%, an average offline ITR of 117.1 bits/min and an average online completion time ratio (BCI completion time against optimal button completion time) of 2.27. All of the participants managed to finish the route under realistic conditions which indicates that our system has the potential to be integrated in the everyday life of immobilized patients.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"24 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":"123839739","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}
Michalis D. Mantzaris, E. Andreakos, D. Fotiadis, Vassiliki T. Potsika, P. Siogkas, Vassiliki I. Kigka, V. Pezoulas, Ioannis G. Pappas, T. Exarchos, I. Končar, J. Pelisek
The scope of this paper is to present the novel risk stratification framework for carotid artery disease which is under development in the TAXINOMISIS study. The study is implementing a multimodal strategy, integrating big data and advanced modeling approaches, in order to improve the stratification and management of patients with carotid artery disease, who are at risk for manifesting cerebrovascular events such as stroke. Advanced image processing tools for 3D reconstruction of the carotid artery bifurcation together with hybrid computational models of plaque growth, based on fluid dynamics and agent based modeling, are under development. Model predictions on plaque growth, rupture or erosion combined with big data from unique longitudinal cohorts and biobanks, including multi-omics, will be utilized as inputs to machine learning and data mining algorithms in order to develop a new risk stratification platform able to identify patients at high risk for cerebrovascular events, in a precise and personalized manner. Successful completion of the TAXINOMISIS platform will lead to advances beyond the state of the art in risk stratification of carotid artery disease and rationally reduce unnecessary operations, refine medical treatment and open new directions for therapeutic interventions, with high socioeconomic impact.
{"title":"A Multimodal Advanced Approach for the Stratification of Carotid Artery Disease","authors":"Michalis D. Mantzaris, E. Andreakos, D. Fotiadis, Vassiliki T. Potsika, P. Siogkas, Vassiliki I. Kigka, V. Pezoulas, Ioannis G. Pappas, T. Exarchos, I. Končar, J. Pelisek","doi":"10.1109/BIBE.2019.00133","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00133","url":null,"abstract":"The scope of this paper is to present the novel risk stratification framework for carotid artery disease which is under development in the TAXINOMISIS study. The study is implementing a multimodal strategy, integrating big data and advanced modeling approaches, in order to improve the stratification and management of patients with carotid artery disease, who are at risk for manifesting cerebrovascular events such as stroke. Advanced image processing tools for 3D reconstruction of the carotid artery bifurcation together with hybrid computational models of plaque growth, based on fluid dynamics and agent based modeling, are under development. Model predictions on plaque growth, rupture or erosion combined with big data from unique longitudinal cohorts and biobanks, including multi-omics, will be utilized as inputs to machine learning and data mining algorithms in order to develop a new risk stratification platform able to identify patients at high risk for cerebrovascular events, in a precise and personalized manner. Successful completion of the TAXINOMISIS platform will lead to advances beyond the state of the art in risk stratification of carotid artery disease and rationally reduce unnecessary operations, refine medical treatment and open new directions for therapeutic interventions, with high socioeconomic impact.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"56 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":"131440280","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}
High Blood Pressure can lead to various cardiovascular diseases increasing the risk of death. Photoplethysmography (PPG) can be used as a low cost, optical technique to determine the arterial blood pressure continuously and noninvasively. Features of several different categories can be extracted from PPG signals. The prominent ones include width-based features, frequency domain features and features extracted from the second derivative of the signal (accelerated PPG). Existing methods primarily use one category of features or another but do not use features from multiple categories. We propose a method to extract a combination of characteristics from the PPG signal, which spans across the aforementioned categories and use them to train a neural network in order to estimate the Blood pressure values. Furthermore, most existing methods are not evaluated on PPG signals collected in a nonclinical setting using consumer-grade/wearable devices, which leaves their applicability to such settings untested. We evaluate our method using a benchmark dataset (MIMIC II) collected in a clinical setting as well a dataset collected using a consumer-grade device in a nonclinical setting. The results show that our method using 53 features achieves Mean Absolute Errors of 4.8 mmHg & 2.5 mmHg for Systolic Blood Pressure and Diastolic Blood Pressure respectively while reaching grade A for both the estimates under the standard British Hypertension Society for the MIMIC II dataset. The same methodology applied to the second dataset shows good agreement (MAE 4.1, 1.7 mmHg for SBP and DBP respectively) with readings taken using a standard oscillometric device, which suggests the robustness of our approach.
{"title":"A Robust Neural Network-Based Method to Estimate Arterial Blood Pressure Using Photoplethysmography.","authors":"Buddhishan Manamperi, Charith D. Chitraranjan","doi":"10.1109/BIBE.2019.00128","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00128","url":null,"abstract":"High Blood Pressure can lead to various cardiovascular diseases increasing the risk of death. Photoplethysmography (PPG) can be used as a low cost, optical technique to determine the arterial blood pressure continuously and noninvasively. Features of several different categories can be extracted from PPG signals. The prominent ones include width-based features, frequency domain features and features extracted from the second derivative of the signal (accelerated PPG). Existing methods primarily use one category of features or another but do not use features from multiple categories. We propose a method to extract a combination of characteristics from the PPG signal, which spans across the aforementioned categories and use them to train a neural network in order to estimate the Blood pressure values. Furthermore, most existing methods are not evaluated on PPG signals collected in a nonclinical setting using consumer-grade/wearable devices, which leaves their applicability to such settings untested. We evaluate our method using a benchmark dataset (MIMIC II) collected in a clinical setting as well a dataset collected using a consumer-grade device in a nonclinical setting. The results show that our method using 53 features achieves Mean Absolute Errors of 4.8 mmHg & 2.5 mmHg for Systolic Blood Pressure and Diastolic Blood Pressure respectively while reaching grade A for both the estimates under the standard British Hypertension Society for the MIMIC II dataset. The same methodology applied to the second dataset shows good agreement (MAE 4.1, 1.7 mmHg for SBP and DBP respectively) with readings taken using a standard oscillometric device, which suggests the robustness of our approach.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"18 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":"131910918","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}
This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 in-silico type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.
{"title":"Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions","authors":"Maxime De Bois, M. Ammi, M. El-Yacoubi","doi":"10.1109/BIBE.2019.00053","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00053","url":null,"abstract":"This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 in-silico type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.","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":"120959358","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}
A. Pentari, Grigorios Tsagkatakis, K. Marias, Georgios C. Manikis, N. Kartalis, N. Papanikolaou, P. Tsakalides
This paper presents a method for reducing the Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) examination time based on the mathematical framework of sparse representations. The aim is to undersample the b-values used for DW-MRI image acquisition which reflect the strength and timing of the gradients used to generate the DW-MRI images since their number defines the examination time. To test our method we investigate whether the undersampled DW-MRI data preserve the same accuracy in terms of extracted imaging biomarkers. The main procedure is based on the use of the k-Singular Value Decomposition (k-SVD) and the Orthogonal Matching Pursuit (OMP) algorithms, which are appropriate for the sparse representations computation. The presented results confirm the hypothesis of our study as the imaging biomarkers extracted from the sparsely reconstructed data have statistically close values to those extracted from the original data. Moreover, our method achieves a low reconstruction error and an image quality close to the original.
{"title":"Sparse Representations on DW-MRI: A Study on Pancreas","authors":"A. Pentari, Grigorios Tsagkatakis, K. Marias, Georgios C. Manikis, N. Kartalis, N. Papanikolaou, P. Tsakalides","doi":"10.1109/BIBE.2019.00147","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00147","url":null,"abstract":"This paper presents a method for reducing the Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) examination time based on the mathematical framework of sparse representations. The aim is to undersample the b-values used for DW-MRI image acquisition which reflect the strength and timing of the gradients used to generate the DW-MRI images since their number defines the examination time. To test our method we investigate whether the undersampled DW-MRI data preserve the same accuracy in terms of extracted imaging biomarkers. The main procedure is based on the use of the k-Singular Value Decomposition (k-SVD) and the Orthogonal Matching Pursuit (OMP) algorithms, which are appropriate for the sparse representations computation. The presented results confirm the hypothesis of our study as the imaging biomarkers extracted from the sparsely reconstructed data have statistically close values to those extracted from the original data. Moreover, our method achieves a low reconstruction error and an image quality close to the original.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 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":"129503198","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}
G. Karagiannidis, Angeliki Papathanasiou, P. Diamantoulakis, A. Saratzis, N. Saratzis
In this paper, we present a novel low cost and low complexity platform for the provisional diagnosis of vessels abnormalities, such as artery stenosis, aneurysms, etc. The proposed system is based on a low cost lightwave wearable device and advanced machine learning techniques to reduce the computational load and improve the accuracy of diagnosis. Specifically, in this work we focus on the image reconstruction in the case of an artery stenosis due to atheromatic plaque, where we briefly present the method and some preliminary results. The proposed platform can automatically provide a provisional diagnosis which can then be followed-up with further detailed and/or established imaging methods (e.g., Doppler ultrasound, Magnetic Resonance Angiography, etc) and treated promptly in order to minimize their likelihood of progression to higher levels of severity. This method may act as an adjunct to existing established screening programmes (e.g., arterial stenosis and aneurysm screening) or be used for new forms of population screening in the future.
{"title":"A Low Complexity and Cost Method to Diagnose Arterial Stenosis Using Lightwave Wearables","authors":"G. Karagiannidis, Angeliki Papathanasiou, P. Diamantoulakis, A. Saratzis, N. Saratzis","doi":"10.1109/BIBE.2019.00127","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00127","url":null,"abstract":"In this paper, we present a novel low cost and low complexity platform for the provisional diagnosis of vessels abnormalities, such as artery stenosis, aneurysms, etc. The proposed system is based on a low cost lightwave wearable device and advanced machine learning techniques to reduce the computational load and improve the accuracy of diagnosis. Specifically, in this work we focus on the image reconstruction in the case of an artery stenosis due to atheromatic plaque, where we briefly present the method and some preliminary results. The proposed platform can automatically provide a provisional diagnosis which can then be followed-up with further detailed and/or established imaging methods (e.g., Doppler ultrasound, Magnetic Resonance Angiography, etc) and treated promptly in order to minimize their likelihood of progression to higher levels of severity. This method may act as an adjunct to existing established screening programmes (e.g., arterial stenosis and aneurysm screening) or be used for new forms of population screening in the future.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 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":"129913862","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}
High-Frequency Oscillations (HFOs) are current strong candidates to serve as biomarkers for the seizure-onset zone (SOZ) in epilepsy. The emergence of new technology and digital methods benefits epilepsy research with the identification and characterizing the SOZ by using HFOs. Invasive recordings, together with automatic detection methods, are at the forefront of epilepsy research, especially when surgery is inevitable for seizure-freedom. However, recent non-invasive HFOs recordings quickly gathered attention for validation to be implemented in future clinical research and practice. This short review aims to briefly provide the research findings regarding HFOs and their significance as biomarkers of epileptogenicity.
{"title":"High-Frequency Oscillations in Epilepsy: A Short Review","authors":"M. Fenech, S. Seri, M. Klados","doi":"10.1109/BIBE.2019.00164","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00164","url":null,"abstract":"High-Frequency Oscillations (HFOs) are current strong candidates to serve as biomarkers for the seizure-onset zone (SOZ) in epilepsy. The emergence of new technology and digital methods benefits epilepsy research with the identification and characterizing the SOZ by using HFOs. Invasive recordings, together with automatic detection methods, are at the forefront of epilepsy research, especially when surgery is inevitable for seizure-freedom. However, recent non-invasive HFOs recordings quickly gathered attention for validation to be implemented in future clinical research and practice. This short review aims to briefly provide the research findings regarding HFOs and their significance as biomarkers of epileptogenicity.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"72 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":"133229715","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}
K. Politof, M. Antonakakis, A. Wollbrink, M. Zervakis, C. Wolters
The primary somatosensory cortex remains one of the most investigated brain areas. However, there is still an absence of an integrated methodology to describe the early temporal alterations in the primary somatosensory network. Source analysis based on combined Electro-(EEG) and Magneto-(MEG) Encephalography (EMEG) has been recently shown to outperform the one's based on single modality EEG or MEG. The study and potential of combined EMEG form the goal of the current study, which investigates the time-variant connectivity of the primary somatosensory network. A subject-individualized pipeline combines a functional source separation approach with the effective connectivity analysis of different spatiotemporal source patterns using a realistic and skull-conductivity calibrated head model. Three-time windows are chosen for each modality EEG, MEG, and EMEG to highlight the thalamocortical and corticocortical interactions. The results show that EMEG is promising in suppressing a so-called connectivity 'leakage' effect when later components seem to influence earlier components, just due to too similar leadfields. Our current results support the notion that EMEG is superior in suppressing the spurious flows within a network of very rapid alterations.
{"title":"Effective Connectivity in the Primary Somatosensory Network using Combined EEG and MEG","authors":"K. Politof, M. Antonakakis, A. Wollbrink, M. Zervakis, C. Wolters","doi":"10.1109/BIBE.2019.00113","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00113","url":null,"abstract":"The primary somatosensory cortex remains one of the most investigated brain areas. However, there is still an absence of an integrated methodology to describe the early temporal alterations in the primary somatosensory network. Source analysis based on combined Electro-(EEG) and Magneto-(MEG) Encephalography (EMEG) has been recently shown to outperform the one's based on single modality EEG or MEG. The study and potential of combined EMEG form the goal of the current study, which investigates the time-variant connectivity of the primary somatosensory network. A subject-individualized pipeline combines a functional source separation approach with the effective connectivity analysis of different spatiotemporal source patterns using a realistic and skull-conductivity calibrated head model. Three-time windows are chosen for each modality EEG, MEG, and EMEG to highlight the thalamocortical and corticocortical interactions. The results show that EMEG is promising in suppressing a so-called connectivity 'leakage' effect when later components seem to influence earlier components, just due to too similar leadfields. Our current results support the notion that EMEG is superior in suppressing the spurious flows within a network of very rapid alterations.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 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":"131097862","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}
Highly accurate genotyping is essential for genomic projects aimed at understanding the etiology of diseases as well as for routinary screening of patients. For this reason, genotyping software packages are subject to a strict validation process that requires a large amount of sequencing data endowed with accurate genotype information. In-vitro assessment of genotyping is a long, complex and expensive activity that also depends on the specific variation and locus, and thus it cannot really be used for validation of in-silico genotyping algorithms. In this scenario, sequencing simulation has emerged as a practical alternative. Simulators must be able to keep up with the continuous improvement of different sequencing technologies producing datasets as much indistinguishable from real ones as possible. Moreover, they must be able to mimic as many types of genomic variant as possible. In this paper we describe OmniSim a simulator whose ultimate goal is that of being suitable in all the possible applicative scenarios. In order to fulfill this goal, OmniSim uses an abstract model where variations are read from a .vcf file and mapped into edit operations (insertion, deletion, substitution) on the reference genome. Technological parameters (e.g. error distributions, read length and per-base quality) are learned from real data. As a result of the combination of our abstract model and parameter learning module, OmniSim is able to output data in all aspects similar to that produced in a real sequencing experiment. The source code of OmniSim is freely available at the URL: https://gitlab.com/geraci/omnisim
{"title":"Technology and Species Independent Simulation of Sequencing Data and Genomic Variants","authors":"F. Geraci, Riccardo Massidda, N. Pisanti","doi":"10.1109/BIBE.2019.00033","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00033","url":null,"abstract":"Highly accurate genotyping is essential for genomic projects aimed at understanding the etiology of diseases as well as for routinary screening of patients. For this reason, genotyping software packages are subject to a strict validation process that requires a large amount of sequencing data endowed with accurate genotype information. In-vitro assessment of genotyping is a long, complex and expensive activity that also depends on the specific variation and locus, and thus it cannot really be used for validation of in-silico genotyping algorithms. In this scenario, sequencing simulation has emerged as a practical alternative. Simulators must be able to keep up with the continuous improvement of different sequencing technologies producing datasets as much indistinguishable from real ones as possible. Moreover, they must be able to mimic as many types of genomic variant as possible. In this paper we describe OmniSim a simulator whose ultimate goal is that of being suitable in all the possible applicative scenarios. In order to fulfill this goal, OmniSim uses an abstract model where variations are read from a .vcf file and mapped into edit operations (insertion, deletion, substitution) on the reference genome. Technological parameters (e.g. error distributions, read length and per-base quality) are learned from real data. As a result of the combination of our abstract model and parameter learning module, OmniSim is able to output data in all aspects similar to that produced in a real sequencing experiment. The source code of OmniSim is freely available at the URL: https://gitlab.com/geraci/omnisim","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"3 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":"132175085","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}
Biocomputing and molecular biology are areas that change knowledge and skills for acquisition, storing, management, analysis, interpretation and dissemination of biological information. This requires the utilization of high performance computers and innovative software tools for management of the vast information, as well as deployment of innovative algorithmic techniques for analysis, interpretation and prognostication of data in order to get to insight of the design and validation of life-science experiments. Sequence alignment is an important method in DNA and protein analysis. The paper describes the computational challenges in biological sequence processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for biological sequence alignment. An investigation of the efficiency of sequence alignment based on parallel multithreaded program implementation of Needleman-Wunsch algorithm is presented in this paper. Parallel computational model based on Needleman-Wunsch algorithm is designed. The proposed parallel model is verified by multithreaded parallel program implementation utilizing OpenMP. A number of experiments have been carried out for the case of various data sets and a various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability both in respect to the workload and machine size.
{"title":"Multithreaded Parallel Sequence Alignment Based on Needleman-Wunsch Algorithm","authors":"Veska Gancheva, I. Georgiev","doi":"10.1109/BIBE.2019.00037","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00037","url":null,"abstract":"Biocomputing and molecular biology are areas that change knowledge and skills for acquisition, storing, management, analysis, interpretation and dissemination of biological information. This requires the utilization of high performance computers and innovative software tools for management of the vast information, as well as deployment of innovative algorithmic techniques for analysis, interpretation and prognostication of data in order to get to insight of the design and validation of life-science experiments. Sequence alignment is an important method in DNA and protein analysis. The paper describes the computational challenges in biological sequence processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for biological sequence alignment. An investigation of the efficiency of sequence alignment based on parallel multithreaded program implementation of Needleman-Wunsch algorithm is presented in this paper. Parallel computational model based on Needleman-Wunsch algorithm is designed. The proposed parallel model is verified by multithreaded parallel program implementation utilizing OpenMP. A number of experiments have been carried out for the case of various data sets and a various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability both in respect to the workload and machine size.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"23 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":"133454550","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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