2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)最新文献_第2页

Comparison between Complete and Ward’s Linkage Method in Hierarchical Clustering Analysis on Cancer Omics Dataset 癌症组学数据层次聚类分析中Complete与Ward关联方法的比较

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802487

Chen Xinyi

{"title":"Comparison between Complete and Ward’s Linkage Method in Hierarchical Clustering Analysis on Cancer Omics Dataset","authors":"Chen Xinyi","doi":"10.1109/icbcb55259.2022.9802487","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802487","url":null,"abstract":"Diseases, cancer as a particular example, can arise from a multitude of genetic and epigenetic changes. Studying gene expression profiles from tumor samples from cancer patients can reveal information about novel cancer subtypes. With the development of analytical approach, clustering methods are widely used on biomedical high-dimensional data, such as omics data, to find groups of samples that have similar profiles and identify subtypes of cancer. In our study, we applied hierarchical clustering on high dimensional mRNA-seq data to cluster the subtypes of cancer. Our focus is to compare the performance of different linkage methods—complete method and Ward’s method in hierarchical clustering and investigate the characteristics of datasets with which a more suitable linkage measure should be used. Our result shows that for dispersed dataset (Kurtosis>0.1, CV>5), Ward’s method performs better than complete method. On the other hand, complete method achieves more accurate clustering results than Ward’s method when it is used to analyze relatively more aggregated data (Kurtosis<0.1, CV<5).","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125581776","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

A Wearable Multi-sensor System for Classification of Multiple System Atrophy and Parkinson's Disease 用于多系统萎缩和帕金森病分类的可穿戴多传感器系统

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802460

X. Wu, Bohan Yu, P. Liu, Huaiyu Zhu, Jianing Li, Haotian Wang, W. Luo, Pan Yun

{"title":"A Wearable Multi-sensor System for Classification of Multiple System Atrophy and Parkinson's Disease","authors":"X. Wu, Bohan Yu, P. Liu, Huaiyu Zhu, Jianing Li, Haotian Wang, W. Luo, Pan Yun","doi":"10.1109/icbcb55259.2022.9802460","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802460","url":null,"abstract":"Multiple system atrophy (MSA) is an atypical parkinsonism disorder with faster progression and clinical symptoms similar to Parkinson’s disease (PD). Thus, it is critical to discriminate the diseases as early as possible to provide better therapies for patients and gain the maximum profits. Although some methods, such as positron emission tomography and cerebrospinal fluid, have good performance in clinical practice, those methods are limited since they would increase the body burden and bring extra cost to patients. Recently, significant differences have been proven on spatiotemporal gait features between MSA and PD, however, to the best of our knowledge, there remains no research on making differential diagnosis between MSA and PD by gait analysis. Therefore, in this work, we design a wearable multi-sensor system based on inertial sensors to collect gait information from MSA and PD patients, respectively, and analyze their gait information to make differential diagnosis between the two above diseases with similar symptoms. We evaluated the proposed system on a total 10 MSA and 21 PD patients. As a result, the performance of proposed system reached 89.1% sensitivity, 89.1% specificity and 89.4% accuracy for the classification between MSA and PD.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122800612","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

Early Diagnosis of Parkinson's Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristic 通过分析脑磁共振成像和患者特征来早期诊断帕金森病

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802132

Sabrina Zhu

{"title":"Early Diagnosis of Parkinson's Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristic","authors":"Sabrina Zhu","doi":"10.1109/icbcb55259.2022.9802132","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802132","url":null,"abstract":"Parkinson’s disease (PD) is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging (MRI) scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to use deep learning to diagnose PD severity by combining symptoms data and MRI data, all of which comes from the public Parkinson’s Progression Markers Initiative (PPMI) database, in order to provide specialists and patients with more flexibility. A new hybrid model architecture was implemented to fully utilize both forms of clinical data to evaluate PD severity with high accuracy, and models based on only symptoms and only MRI scans were also developed. The developed model integrates a fully connected deep learning neural network for symptoms data training and a transfer learning-based convolutional neural network for MRI scans training. Instead of performing only binary classification, all models classify patients into five severity categories, with stage zero representing healthy patients and stages four and five representing patients with PD. The symptoms-only, MRI scans-only and hybrid models achieved accuracies of 0.77, 0.68, and 0.94, respectively. The hybrid model also had high precision and recall scores of 0.94 and 0.95. Real clinical cases confirm the hybrid model’s strong performance, where patients were classified incorrectly with both other models but correctly by the hybrid. It is also consistent across the five 0-4 severity stages, so early detection of PD is accurate.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125468104","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: 3

Design and Analysis of MEMS Thermopile Sensor with Multiple Absorption Regions 多吸收区MEMS热电堆传感器的设计与分析

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802489

Dinghu Zha, Chuanwei Qiao, Peiyu Zhang

引用次数: 0

Tutorial on 8 Genotype Files Conversion 8基因型文件转换教程

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802470

Muhammad Muneeb, Samuel F. Feng, Andreas Henschel

引用次数: 1

Will AlphaFold2 Be Helpful in Improving the Accuracy of Single-sequence PPI Site Prediction? AlphaFold2是否有助于提高单序列PPI位点预测的准确性?

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802490

Zhe Liu, Weihao Pan, Xu Zhen, Ji Liang, Wenxiang Cai, Kai Yuan, G. Lin

{"title":"Will AlphaFold2 Be Helpful in Improving the Accuracy of Single-sequence PPI Site Prediction?","authors":"Zhe Liu, Weihao Pan, Xu Zhen, Ji Liang, Wenxiang Cai, Kai Yuan, G. Lin","doi":"10.1109/icbcb55259.2022.9802490","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802490","url":null,"abstract":"AlphaFold2 has achieved relatively high structure prediction accuracy on proteins. However, it is reported that directly feeding coordinates into deep learning models cannot achieve ideal results on downstream tasks. Therefore, how to process the predicted results into an effective form that deep learning networks can understand to improve the performance of downstream tasks is worth exploring. In this study, taking single-sequence PPI site prediction as an example, we verified the effects of three processing strategies of coordinates, namely spatial Altering, SVD20, and the rASA feature calculation. The experiment results showed that spatial filtering and the rASA feature were two effective and suitable ways to encode structural information for deep learning models. Besides, we also performed a case study of a mutated protein. The results proved that spatial filtering might potentially introduce structural changes into HHblits profiles and deep learning networks when protein mutations occur. This work provides new insight into the downstream tasks, such as predicting the binding sites of proteins or predicting the effects of mutations.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":" 36","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120833408","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

The Study of Disease Mechanisms Based on Cascading Failure 基于级联失效的疾病机制研究

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802465

Dandan Zhang, Yanhui Wang

引用次数: 0

Improved Protein Secondary Structure Prediction Using Bidirectional Long Short-Term Memory Neural Network and Bootstrap Aggregating 基于双向长短期记忆神经网络和自举聚合的改进蛋白质二级结构预测

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802482

Wenfei Zeng, Ning-Xin Jia, Junda Hu

{"title":"Improved Protein Secondary Structure Prediction Using Bidirectional Long Short-Term Memory Neural Network and Bootstrap Aggregating","authors":"Wenfei Zeng, Ning-Xin Jia, Junda Hu","doi":"10.1109/icbcb55259.2022.9802482","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802482","url":null,"abstract":"Accurate predicting protein secondary structure information is essential to identify structural classes, folds, and tertiary structures of proteins. In this study, we propose an accurate predictor, BiBagPSS, for predicting protein secondary structure information based on integrating Bidirectional Long Short-Term Memory (BiLSTM) neural network, fully connection (FC) neural network, and the strategy of bootstrap aggregating (Bagging). In BiBagPSS, three different feature views, i.e., position-specific scoring matrix (PSSM), hidden Markov model profile (HMM), and predicted solvent accessibility probability matrix (PSAPM), are first employed to extract different protein-level features. Secondly, the above three features are combined and fed into a stacked neural network composed of the units of BiLSTM and FC. Thirdly, the predicted secondary structure probability matrix (PSSPM) generated by trained model is then added to the input features for re-training the model. In order to fully dig out available information from the training data set, we employ the strategy of bootstrap aggregating to train multiple stacked neural network models. Finally, according to the voting results of the above models, the secondary structure state of each protein residue could be determined. Experimental results show that BiBagPSS achieves Q3 scores of 82.39 and 77.30, Q8 scores of 69.95 and 65.61 on TEST524 and CASP14set data sets, respectively, which are higher than or comparable to most of the state-of-the-art predictors. Detailed data analyses show that the major advantage of BiBagPSS lies in the utilization of the PSSPM that helps extract more discriminative information compared with the previously used machine learning algorithms. Meanwhile, the Bagging strategy improves the ability of BiBagPSS to mine available information.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116507769","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

Predicting Conversion to Mild Cognitive Impairment in Cognitively Normal with Incomplete Multi-modal Neuroimages 预测认知正常且多模态神经图像不完整的患者转化为轻度认知障碍

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802479

Yuqing Sun, Yong Liu, Bing Liu

{"title":"Predicting Conversion to Mild Cognitive Impairment in Cognitively Normal with Incomplete Multi-modal Neuroimages","authors":"Yuqing Sun, Yong Liu, Bing Liu","doi":"10.1109/icbcb55259.2022.9802479","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802479","url":null,"abstract":"Assessing clinical progression from cognitively normal (CN) to mild cognitive impairment (MCI) is crucial for early intervention before the onset of cognitive decline. Multi-modal neuroimaging data has provided supplementary biomarkers for computer-aided prediction of neurodegeneration diseases. However, it is still unknown whether tau uptake in positron emission tomography (PET) provides much power for identifying progressive CN who will convert to MCI, since subjects usually lack tau PET scans. In this study, we proposed a neuroimage synthesis network to impute missing tau PET images based on their corresponding T1-weighted magnetic resonance imaging (MRI) scans. With the real MRI and synthetic PET data after imputation, we applied support vector machine classifiers on regional measurement of anatomical features extracted from pre-defined atlases for prediction. Experimental results on Alzheimer's Disease Neuroimaging Initiative dataset suggest that our neuroimage synthesis network synthesized reasonable neuroimages and complementary information provided by tau PET improved the accuracy of identification.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124908908","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

Identification of Hub Genes and Key Pathways in TNF-α and IFN-γ Induced Cytokine Storms via Bioinformatics 通过生物信息学鉴定TNF-α和IFN-γ诱导的细胞因子风暴的枢纽基因和关键途径

2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2022-05-13 DOI: 10.1109/icbcb55259.2022.9802459

Ryan Christian Mailem, L. Tayo

{"title":"Identification of Hub Genes and Key Pathways in TNF-α and IFN-γ Induced Cytokine Storms via Bioinformatics","authors":"Ryan Christian Mailem, L. Tayo","doi":"10.1109/icbcb55259.2022.9802459","DOIUrl":"https://doi.org/10.1109/icbcb55259.2022.9802459","url":null,"abstract":"Cytokine storms, an overaggressive immune response due to the overexpression of pro-inflammatory cytokines, have been identified to play a significant role in COVID-19 infections. Studies have shown that TNF-α and IFN-γ are integral to the process, however, its genetic mechanisms have yet to be fully elucidated. Herein, the key changes in the gene expression of TNF-α and IFN-γ induced cytokine storms are identified through differential gene analysis on the publicly available GEO GSE160163 dataset. GO and KEGG enrichment were used to annotate identified DEGs, and a PPI network was constructed based on the STRING database. A total of 446 differentially expressed genes were identified. Up-regulated genes and downregulated genes were enriched in viral immune response and infection pathways, and steroid biosynthesis and metabolic pathways, respectively. PPI construction revealed 1,834 interactions between 428 proteins, indicating their biological connectivity. Module analysis identified nine (9) hub genes: STAT1, CXCL10, CD274, CXCL9, IRF1, PSMB9, CD86, STAT3, and CXCR4, involved in viral immune response and three (3) significant modules involved in NOD-like receptor signaling, steroid biosynthesis, and viral infections. These identified DEGs, hub genes, and their respective enriched pathways aid us in understanding the molecular mechanisms of cytokine storms, as well as provide potential gene targets and druggable receptors for the treatment of cytokine storms.","PeriodicalId":429633,"journal":{"name":"2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124057128","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