Proceedings of the 9th International Conference on Bioinformatics Research and Applications最新文献

Unsupervised discovery of Mild Cognitive Impairment subtypes of Alzheimer's disease using consensus clustering and unsupervised learning techniques 使用共识聚类和无监督学习技术无监督地发现阿尔茨海默病的轻度认知障碍亚型

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569201

F. Nezhadmoghadam, Jose Gerardo Tamez-Peña

{"title":"Unsupervised discovery of Mild Cognitive Impairment subtypes of Alzheimer's disease using consensus clustering and unsupervised learning techniques","authors":"F. Nezhadmoghadam, Jose Gerardo Tamez-Peña","doi":"10.1145/3569192.3569201","DOIUrl":"https://doi.org/10.1145/3569192.3569201","url":null,"abstract":"Discovering and characterizing reproducible disease subtypes results is one of the most demanding and fundamental tasks in many fields, such as bioinformatics and health informatics. It could facilitate diagnosis and is a vital step toward more individualized therapy. This paper aims to analyze the ability of unsupervised learning methods to identify a small collection of reliable and stable subtypes of subjects with mild cognitive impairment (MCI) and to discover the primary prodromal Alzheimer's disease (AD) stages in subjects with MCI to AD conversion risk. We present a novel unsupervised learning methodology to identify the notable stable and reproducible subtypes. The proposed method takes advantage of the consensus clustering of unsupervised clustering methods. For this mean, we obtained the data from the Alzheimer's disease Neuroimaging Initiative study. 346 features, including demographic information and MRI-derived features, described 839 subjects with early MCI. We randomly split the data into discovery (70%) and validation (30%) sets. The discovery set was analyzed using five unsupervised clustering methods, and robust consensus clustering was used to determine the most stable and reliable subtypes. The results show that the proposed method identified four different MCI patient subtypes. After discovery, subtypes were predicted in the testing set and associated with MCI conversion. One subtype had a high-risk (OR = 2.99, 95%CI = 1.65 to 5.41) of converting to Alzheimer's disease.","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372808","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}

引用次数: 0

Automatic lung segmentation in CT scans using guided filtering 引导滤波在CT扫描中的自动肺分割

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569209

Gabor Revy, D. Hadhazi, G. Hullám

引用次数: 0

Biosignal Databases for Training of Artificial Intelligent Systems 用于人工智能系统训练的生物信号数据库

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569218

Luís C. N. Barbosa, António H. J. Moreira, Vítor Carvalho, J. Vilaça, P. Morais

{"title":"Biosignal Databases for Training of Artificial Intelligent Systems","authors":"Luís C. N. Barbosa, António H. J. Moreira, Vítor Carvalho, J. Vilaça, P. Morais","doi":"10.1145/3569192.3569218","DOIUrl":"https://doi.org/10.1145/3569192.3569218","url":null,"abstract":"Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will have mild to moderate respiratory diseases, however, the elderly population is the most vulnerable, becoming seriously ill, requiring continuous medical follow-up. In this sense, technologies were developed that allow continuous and individual monitoring of patients, in a home environment, namely through wearable devices, thus avoiding continuous hospitalization. Thus, these devices allow great improvements in data analysis methods since they can continuously acquire the physiological signals of an individual and process them in real-time through artificial intelligence (AI) methods. However, training of AI methods is not straightforward, requiring a large amount of data. In this study, we review the most common biosignal databases available in the literature. A total of thirteen databases were selected. Most of the databases (9 databases) were related to ECG signal, as well as 4 databases containing signals from SPO2, Heart Rate, Blood Pressure, etc. Characteristics were described, namely: the population of the databases, data resolution, sampling rates, sample time, number of signal samples, annotated classes, data acquisition conditions, among other aspects. Overall, this study summarizes and described the public biosignals databases available in the literature, which may be important in the implementation of intelligent classification methods.","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"90 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133292331","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}

引用次数: 1

Profile genetic variability and population diversity of C-C chemokine receptor type 5 (CCR5) gene in HIV infection C-C趋化因子受体5型(CCR5)基因在HIV感染中的遗传变异和群体多样性

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569197

Yu Sun, Yitian Zhou

引用次数: 0

Tool recommendation for workflow composition using frequent patterns 使用频繁模式的工作流组合工具推荐

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569204

Rupika Wijesinghe, Ruwan Weerasinghe

{"title":"Tool recommendation for workflow composition using frequent patterns","authors":"Rupika Wijesinghe, Ruwan Weerasinghe","doi":"10.1145/3569192.3569204","DOIUrl":"https://doi.org/10.1145/3569192.3569204","url":null,"abstract":"Workflows or pipelines provide a means for executing complex data analysis seamlessly. Composing tools into a workflow is essential in bioinformatics experiments. There are scientific workflow systems such as Taverna and Galaxy that facilitate automatic workflow composition. However, designing workflows using workflow systems becomes more complex with the availability of vast numbers of complex, heterogeneous tools. Connecting such heterogeneous tools to a workflow is error-prone and time-consuming. The objective of the study is to develop a suggestive system for interactive workflow composition using frequent patterns in workflows. The approach basically consists of three main phases: pattern mining, component suggestion, and updating the workflow. Frequent patterns of workflows are identified using frequent subgraph mining techniques and N-gram modeling. The suggested components allow reusing best practice workflows while reducing the time required in composing the workflows. Frequent usage patterns identified can also be used in searching similar workflows in workflow repositories. An interactive workflow composition approach is useful for novice as well as experienced scientists in composing workflows with state-of-the-art tools. The approach enhances the reuse of best practice workflows developed by other users. Such systems would succeed more in future with the availability of more and more workflows in the light of open science","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131398794","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}

引用次数: 0

Data and task orchestration defined by spatio-temporal variables for healthcare data science services 由医疗保健数据科学服务的时空变量定义的数据和任务编排

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569208

Jose Carlos Morin Garcia, Juan Armando Barron Lugo, Jose Luis Gonzalez Compean, Ivan Lopez Arevalo, J. Carretero, Martha Cordero Oropeza

{"title":"Data and task orchestration defined by spatio-temporal variables for healthcare data science services","authors":"Jose Carlos Morin Garcia, Juan Armando Barron Lugo, Jose Luis Gonzalez Compean, Ivan Lopez Arevalo, J. Carretero, Martha Cordero Oropeza","doi":"10.1145/3569192.3569208","DOIUrl":"https://doi.org/10.1145/3569192.3569208","url":null,"abstract":"Data science services have become a solution for healthcare organizations to take advantage of the large volumes of data (e.g., data lakes and data warehouses) produced during the interaction of healthcare staff with patients and government agencies. However, the data orchestration for these services is not trivial when dealing with multiple data sources where decision-making processes should combine them to create a single solid information piece (big picture) for making inferences or predictions. In this paper, we present a data and task orchestration method for supporting healthcare data science services. This method considers stages such as data fusion/integration for enabling the crossing of information, computing splits for producing, on-the-fly and on-demand, data subsets by using spatio-temporal variables, converting splited data into information, consolidation of information into segments to create a big picture of data and, in the last stage, makes available data segments for consumption on decision-making processes by using spatio-temporal queries. A case study based on the fusion of healthcare data sources about psychiatric, drug consumption, and macro-economics was conducted by using a prototype of the data orchestration proposed in this paper. The evaluation revealed the flexibility of this data orchestration approach to convert multiple data sources into useful information for healthcare decision-making processes.","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121452587","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}

引用次数: 0

Comparative Evaluation of Oversampling Techniques for Balancing Metabolic Profiles 平衡代谢谱的过采样技术的比较评价

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569200

Mikel Hernandez, Gorka Epelde, R. Gil-Redondo, N. Embade, Ane Alberdi, I. Macía, Ó. Millet

{"title":"Comparative Evaluation of Oversampling Techniques for Balancing Metabolic Profiles","authors":"Mikel Hernandez, Gorka Epelde, R. Gil-Redondo, N. Embade, Ane Alberdi, I. Macía, Ó. Millet","doi":"10.1145/3569192.3569200","DOIUrl":"https://doi.org/10.1145/3569192.3569200","url":null,"abstract":"The problem of imbalanced data is common when applying data analytics paradigms to binary and multiclass data, such as statistical analyses, predictive models, and classification metrics sensitive to imbalanced data, i.e., accuracy. Although there exist some pre-processing, algorithms, and hybrid approaches, none of them has a special focus on balancing metabolic profiles for Metabolic Syndrome analysis. Since the insights and conclusions obtained from data analysis paradigms applied to metabolic data are relevant to the topic, statistical power may be lost due to an imbalance between the Metabolic Syndrome related subclasses. Thus, there is a need to balance metabolic data to improve the insights derived from these types of analyses. In this context, this paper presents a comparative evaluation of six oversampling techniques for balancing metabolic profiles (SMOTE, B-SMOTE, ADASYN, ROS, K-SMOTE, and SVM-SMOTE). An imbalanced dataset with 16 classes from the combinations of 4 binary metabolic conditions is used for this analysis. Additionally, a methodology is defined to objectively evaluate and compare the six oversampling techniques in terms of representativity and variety. The results have shown that ROS and SMOTE have been the best oversampling techniques to balance metabolic data, generating high-quality synthetic profiles that resemble the real ones while balancing all classes equally. This demonstrates that metabolomics studies focused on metabolic syndrome can trust in these oversampling methods to improve their conclusions.","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"493 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127785722","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}

引用次数: 0

Speech Recognition for Functional Decline assessment in older adults 语音识别在老年人功能衰退评估中的应用

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569216

Dona Elisa Bou Zeidan, Abir Noun, Mohamad Nassereddine, Jamal Charara, A. Chkeir

引用次数: 0

Open-Source Application for Mri and Ct Registration Using Homography Transformation 利用单应性变换的Mri和Ct配准的开源应用

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569210

Milica Badža Atanasijević, Tijana Radović, M. Janković, M. Barjaktarović

{"title":"Open-Source Application for Mri and Ct Registration Using Homography Transformation","authors":"Milica Badža Atanasijević, Tijana Radović, M. Janković, M. Barjaktarović","doi":"10.1145/3569192.3569210","DOIUrl":"https://doi.org/10.1145/3569192.3569210","url":null,"abstract":"Medical imaging is substantial in diagnosing and treatment of various types of diseases, however, a single modality may not be sufficient. Therefore, gathering more than one imaging modality to provide additional information is needed. Thus, an open-source application for the registration of the two medical imaging modalities using a homography transformation was implemented. Using homography, the mapping of the points of the magnetic resonance image to the corresponding points of the computer tomography image was performed. The application enables the radiologist to finely adjust the two modalities by moving the homography points and performing isometric transformations on the slices. The usage of the developed open-source application was evaluated by one experienced radiologist, and calculating the mutual information (MI) on the data of the axial plane from three pediatric patients (8.33±4.19 years), collected from the University Children's Hospital, Belgrade, Serbia. The radiologist adjusted the registered images until a satisfactory level of alignment was achieved, and the increase of the initial MI∼0.65 ranging from 0.17 to 0.23 was achieved. The developed software, based on the homography transformations combined with feedback in the form of MI, a simple graphical interface, and an open-source implementation enable its easy use in everyday clinical practice.","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131129474","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}

引用次数: 0

Stenosis Detection in X-ray Coronary Angiography with Deep Neural Networks Leveraged by Attention Mechanisms 基于注意机制的深度神经网络在x线冠状动脉造影狭窄检测中的应用

Proceedings of the 9th International Conference on Bioinformatics Research and Applications Pub Date : 2022-09-18 DOI: 10.1145/3569192.3569212

Pedro Van Stralen, Dinis L. Rodrigues, Arlindo L. Oliveira, M. Menezes, F. Pinto

{"title":"Stenosis Detection in X-ray Coronary Angiography with Deep Neural Networks Leveraged by Attention Mechanisms","authors":"Pedro Van Stralen, Dinis L. Rodrigues, Arlindo L. Oliveira, M. Menezes, F. Pinto","doi":"10.1145/3569192.3569212","DOIUrl":"https://doi.org/10.1145/3569192.3569212","url":null,"abstract":"Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The automatic detection of coronary artery stenosis on X-ray images is important in coronary heart disease diagnosis. Coronary artery disease is caused by atherosclerotic plaques with subsequent stenosis (e.g. narrowing) of the coronary arteries. This makes the heart work harder, risking failure. Automated identification of stenosis may be used for triage or as a second reader in clinical practice, providing a valuable tool for cardiologists. In this paper, we evaluate the detection of stenosis in X-ray coronary angiography images with novel object detection methods based on deep neural networks. We trained and tested three promising object detectors based on different neural network architectures leveraging attention mechanisms (EfficientDet, RetinaNet ResNet-50- FPN, and Faster R-CNN ResNet-101) using clinical angiography data of 438 patients. The metrics obtained on this dataset, have shown an advantage of EfficientDet over alternative approaches, achieving a mean average precision of 0.67 in the task of detecting stenosis in X-Ray angiographies. This result provides evidence that attention mechanisms improve the performance of convolutional neural networks in a medical imaging context.","PeriodicalId":249004,"journal":{"name":"Proceedings of the 9th International Conference on Bioinformatics Research and Applications","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121496174","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}

引用次数: 2