2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第9页

Automatic Segmentation of Neonates Thermal Imaging for Evaluation of Trunk Thermal Asymmetry 新生儿热成像自动分割评价躯干热不对称性

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621553

Thyago Maia Tavares de Farias, M. Lima, S. Mattos, Juliana Souza S. de Araujo, Lucia Roberta D. N. Mozer, F. Mourato

{"title":"Automatic Segmentation of Neonates Thermal Imaging for Evaluation of Trunk Thermal Asymmetry","authors":"Thyago Maia Tavares de Farias, M. Lima, S. Mattos, Juliana Souza S. de Araujo, Lucia Roberta D. N. Mozer, F. Mourato","doi":"10.1109/BIBM.2018.8621553","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621553","url":null,"abstract":"This paper proposes a new method for automatic segmentation of neonates’s trunks in thermal images. The method is based on an algorithm that use a threshold, associated with an active contour technique. The hypothesis is that it is possible to correlate a given temperature map, presented in a thermal image, with the persistence of the heart ductus arteriosus of a newborn. From this region of interest (ROI), it is possible to capture quantitative and statistical information on the pathology’s temperature map, in order to use them as input in a machine learning applications to, in addition to aiding early diagnosis, to enable its automation. However, the presence of clothing, bandages, sheets, and other objects during the catches may alter the temperature to be considered in the diagnosis. Thus, in order to guarantee the consistency of the data extracted from such images, the region of interest must be segmented and analyzed separately. The proposed automatic segmentation obtained low error rates, generating outputs very similar to those obtained in manual segmentation.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128386649","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

Single-cell Hi-C demonstrates that TADs are stable units of Drosophila genome folding that persist in individual cells 单细胞Hi-C表明TADs是果蝇基因组折叠的稳定单位，持续存在于单个细胞中

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621563

V. V. Zakharova, Aleksandra Galitsyna, K. Polovnikov, E. Khrameeva, M. Logacheva, E. Mikhaleva, E. Vassetzky, A. Gavrilov, Y. Y. Shevelev, S. Nechaev, S. Ulianov, S. Razin

{"title":"Single-cell Hi-C demonstrates that TADs are stable units of Drosophila genome folding that persist in individual cells","authors":"V. V. Zakharova, Aleksandra Galitsyna, K. Polovnikov, E. Khrameeva, M. Logacheva, E. Mikhaleva, E. Vassetzky, A. Gavrilov, Y. Y. Shevelev, S. Nechaev, S. Ulianov, S. Razin","doi":"10.1109/BIBM.2018.8621563","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621563","url":null,"abstract":"The 3D organization of the genome appears to be functionally relevant as it allows establishing of long-range spatial contacts between promoters and remote regulatory elements. However, most of the observations on the 3D genome organization have been made by conventional methods in cell population where only average characteristics of the so-called typical cell can be identified. On the other hand, FISH-based studies demonstrated that the spatial configuration of the genome varies in individual cells. However, the microscopic approaches do not allow performing a genome-wide analysis that is critical to understand better the regulatory events occurring at the level of 3D genome organization. The high throughput chromosome conformation capture protocol (Hi-C) has been modified recently to allow construction of chromatin contact frequency maps for individual cells. Using this modified protocol we constructed Hi-C maps for 20 drosophila cells (line Dm-BG3c2). In the best cell we have captured ~15% of the theoretically available contacts. This allowed constructing of the spatial contact matrices with 10 Kb resolution. Analysis of these matrices demonstrated that topologically-associating domains (TADs) do not represent a computer-generated population average, but exist in individual cells. Importantly, using a number of statistical approaches we show that the observed profile of contact chromatin domains in individual cells cannot be explained by random fluctuations. Furthermore, we show that in individual cells TADs are organized hierarchically, and that this hierarchy closely matches the hierarchy seen in population maps. Finally, we show that genomic regions that frequently harbor the contact domain borders possess specific epigenetic signatures.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128221760","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

Detection of sleep spindles in NREM 2 sleep stages: Preliminary study & benchmarking of algorithms NREM 2睡眠阶段的睡眠纺锤波检测:算法的初步研究与基准测试

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621305

O. Pallanca, Sammy Khalife, J. Read

引用次数: 0

HMNPPID: A Database of Protein-protein Interactions Associated with Human Malignant Neoplasms HMNPPID:与人类恶性肿瘤相关的蛋白-蛋白相互作用数据库

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621402

Qingqing Li, Zhihao Yang, Zhehuan Zhao, Ling Luo, Zhiheng Li, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang, Yijia Zhang

引用次数: 3

Gene expression based prediction of prognostic outcome in ovarian cancer 基于基因表达的卵巢癌预后预测

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621205

T. Ahn, Nayeon Kang, Yonggab Kim, T. Park

{"title":"Gene expression based prediction of prognostic outcome in ovarian cancer","authors":"T. Ahn, Nayeon Kang, Yonggab Kim, T. Park","doi":"10.1109/BIBM.2018.8621205","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621205","url":null,"abstract":"Gene expression provides rich information. Successful application has made to predict prognosis of several cancers such as breast and colon. However, although ovarian cancer is the fifth leading death cancer to women, precise prediction of survival outcome is not available yet. Thus there is a still urgent need for optimized treatment decision.Recent studies made use of public gene expression data sources to predict the clinical outcome of ovarian cancer. Typically, two steps approach has tried. First step is figuring out significant genes by univariate Cox regression model. Second step is providing a statistic that will combine the effect of selected genes in terms of survival risk. One of drawback of the two steps approach is low reproducibility. Statistics for risk group classification built in the train set often fails to be validated when the statistic is applied to the data set. Applying the scheme to the RNAseq data from The Cancer Genome Atlas(TCGA) has shown that the classification results of the patient’s prognosis was classified higher and lower risk patient of the patient’s prognosis. We applied median standard to the classification of existing scheme and suggested other schemes for the successive work.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091326","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: 4

Biomechanical Mechanism of Lumbar Spine with Chinese Spinal Manipulation: A Finite Element Study 腰椎推拿的生物力学机制:有限元研究

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621346

Q. Tian, Z. Fan, Jiayou Zhao, Rusong Guo, Zhenbao Li, Shan Wu

引用次数: 0

Exploring Deep Learning-based Approaches for Predicting Concept Names in SNOMED CT 探索基于深度学习的SNOMED CT概念名称预测方法

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621076

Fengbo Zheng, Licong Cui

{"title":"Exploring Deep Learning-based Approaches for Predicting Concept Names in SNOMED CT","authors":"Fengbo Zheng, Licong Cui","doi":"10.1109/BIBM.2018.8621076","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621076","url":null,"abstract":"Ontologies or terminologies have been widely used as formal representation of biomedical knowledge. New concepts are constantly added to biomedical ontologies due to the evolving nature of biomedical knowledge. Much progress has been made to identify new concepts in SNOMED CT, the largest clinical healthcare terminology. However, proper naming of new concepts remains challenging and relies on the ontology curators’ manual effort. In this paper, we explore three deep learning-based approaches, given bags of words, to automatically predict concept names that comply with the naming convention of SNOMED CT. These deep learning models are simple neural network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) combined with LSTM. Our experiments showed that LSTM-based approach achieved the best performance: a precision of 65.98%, a recall of 61.04%, and an F1 score of 63.41% for predicting concept names for newly added concepts in the March 2018 Edition of SNOMED CT. It also achieved a precision of 74.58%, a recall of 73.33%, and an F1 score of 73.95% for naming missing concepts identified by our previous work. Further examination of results revealed inconsistencies within SNOMED CT which may be leveraged for quality assurance purpose.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131030171","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: 9

Evolutionary Game Theory Can Explain the Choice Between Apoptotic and Necrotic Pathways in Neutrophils 进化博弈论可以解释中性粒细胞凋亡和坏死途径的选择

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621127

Alva Presbitero, E. Mancini, F. Castiglione, V. Krzhizhanovskaya, Rick Quax

{"title":"Evolutionary Game Theory Can Explain the Choice Between Apoptotic and Necrotic Pathways in Neutrophils","authors":"Alva Presbitero, E. Mancini, F. Castiglione, V. Krzhizhanovskaya, Rick Quax","doi":"10.1109/BIBM.2018.8621127","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621127","url":null,"abstract":"Neutrophils are one of the key players in the human innate immune response. In case of an insult, neutrophils neutralize noxious pathogens via two main mechanisms: degranulation and phagocytosis. In case of a minor infection, after performing their role, neutrophils go into programmed death called apoptosis. However, if the insult is too intense, neutrophils take on a violent death pathway called necrosis, releasing their cytoplasmic content into surrounding tissue, thus aggravating inflammation. This seemingly paradoxical action is thought to fuel the inflammatory process by triggering the recruitment of additional neutrophils to the site of inflammation, possibly contributing to the complete elimination of a pathogen in case of severe infections. This delicate balance between the cost and benefit of the neutrophils’ choice of death pathway has been optimized during the evolution of the innate immune system. The goal of our work is to understand how the tradeoff between the cost and benefit of the different death pathways of neutrophils in response to various levels of insults has been optimized over evolutionary time using concepts of evolutionary game theory. We show that by using evolutionary game theory, we are able to formulate a game that accurately predicts the percentage of necrosis and apoptosis when exposed to various levels of insults. By adopting an evolutionary perspective, we identify the driving mechanism leading to the delicate balance between apoptosis and necrosis in neutrophils cell death in response to different insults.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130643000","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: 5

A Deep Learning Framework for Identifying Essential Proteins Based on Protein-Protein Interaction Network and Gene Expression Data 基于蛋白-蛋白相互作用网络和基因表达数据识别必需蛋白的深度学习框架

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621551

Min Zeng, Min Li, Zhihui Fei, Fang-Xiang Wu, Yaohang Li, Yi Pan

{"title":"A Deep Learning Framework for Identifying Essential Proteins Based on Protein-Protein Interaction Network and Gene Expression Data","authors":"Min Zeng, Min Li, Zhihui Fei, Fang-Xiang Wu, Yaohang Li, Yi Pan","doi":"10.1109/BIBM.2018.8621551","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621551","url":null,"abstract":"Identifying essential proteins is of vital importance for disease study and drug design. A lot of topology-based and machine learning-based methods have been proposed to identify essential proteins. However, traditional topology-based methods only focus on explicitly described characteristics of network topology and are not expressive enough to capture the complexity of connectivity patterns observed in biological networks. In addition, identification of essential proteins is an imbalanced learning problem due to the fact that there are significantly more non-essential proteins than the essential ones. Few machine learning-based methods take the imbalanced nature into consideration. We propose a new deep learning framework, to tackle the above limitations. In our model, we make use of the node2vec technique to learn topological features from protein-protein interaction (PPI) network without manual feature selection. To overcome the problem of the imbalanced nature of dataset, we use a sampling method, which does not bias to the majority and minority classes in a training step and tend to make full use of all samples during the whole training process. To evaluate the performance of our model, we test it on S. cerevisiae dataset. Our results show that it greatly outperforms topology-based methods including DC, BC, CC, EC, NC, LAC, PeC and WDC. It also outperforms machine learning-based methods including support vector machine (SVM), decision tree, random forest and Adaboost.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130910025","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: 8

Analysis of Sex and Recurrence Ratios in Simplex and Multiplex Autism Spectrum Disorder Implicates Sex-Specific Alleles as Inheritance Mechanism 单纯性和多重性自闭症谱系障碍的性别和复发率分析暗示性别特异性等位基因是遗传机制

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621554

B. Chrisman, M. Varma, P. Washington, K. Paskov, N. Stockham, Jae-Yoon Jung, D. Wall

{"title":"Analysis of Sex and Recurrence Ratios in Simplex and Multiplex Autism Spectrum Disorder Implicates Sex-Specific Alleles as Inheritance Mechanism","authors":"B. Chrisman, M. Varma, P. Washington, K. Paskov, N. Stockham, Jae-Yoon Jung, D. Wall","doi":"10.1109/BIBM.2018.8621554","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621554","url":null,"abstract":"Autism spectrum disorder (ASD) has a strong male bias, with four times as many affected males as females. ASD is hypothesized to follow a polygenic disease model. While prior literature has linked several genes with the disorder, the specific genetic causes and inheritance methods underlying the condition are still widely unknown. Here, we investigate two popular theories of polygenic inheritance that could account for the male preponderance of ASD: a multiple-threshold model in which females must have a higher genetic burden in order to be affected, and a sex-specific allele model in which variants in genes and regulatory regions have sex-specific effects. We use phenotypic information from the Simons Simplex Collection of families with simplex ASD and the iHART collection of families with multiplex ASD to compare ratios of affected males and females and sex-specific recurrence rates with predictions from each of the inheritance mechanisms. Our results suggest that a sex-specific allele model can be used to explain the male bias behind ASD inheritance.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131713179","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: 7