2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

{"title":"Single-Particle 3D Reconstruction beyond the Nyquist Frequency","authors":"James Z. Chen","doi":"10.1109/BIBM.2018.8621120","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621120","url":null,"abstract":"The Nyquist frequency dictates the highest resolution of information in an image. However, this upper-limit only stands for a single image. In a set of images from the same object but with random inter-frame translation, the ensemble actually contains information beyond the Nyquist frequency. In this work, an algorithm is proposed and validated to retrieve such information in 2D and 3D space. Its application in single-particle electron microscopy can lead to high-throughput data collection and density map reconstruction at higher resolution.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"125 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":"115483404","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}

{"title":"A Pulmonary Vascular Segmentation Algorithm of Chest CT Images Based on Fast Marching Method","authors":"Wenjun Tan, Yao Liu, Jinzhu Yang, Hua Wang, Tongliang Wang, Yanchun Zhang, Dazhe Zhao","doi":"10.1109/BIBM.2018.8621496","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621496","url":null,"abstract":"Pulmonary vascular segmentation plays an important role in lung disease detection. In order to improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular segmentation method based on the fast marching method combined with the gray gradient and threshold is proposed in this paper. Firstly, the lung tissue is extracted from chest CT images by Maximum Between-Class Variance. Then the holes of the extracted region are filled by morphological opening and closing operations. Secondly, the points of the vascular of the middle slice of the CT images are extracted and marked as the original seed points. Finally, the seed points are spread throughout the lung tissue to extract the pulmonary vascular based on the fast marching method with the restricted grayscale value threshold and gradient. The experiments results show that the pulmonary vascular are extracted accurately by this method.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"6 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":"121830395","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}

{"title":"Mmalloc: A Dynamic Memory Management on Many-core Coprocessor for the Acceleration of Storage-intensive Bioinformatics Application","authors":"Zihao Wang, Mingzhe Zhang, Jingrong Zhang, Rui Yan, Xiaohua Wan, Zhiyong Liu, Fa Zhang, Xuefeng Cui","doi":"10.1109/BIBM.2018.8621415","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621415","url":null,"abstract":"In the past decades, many applications in bioinformatics have achieved great success by extracting useful information from huge amounts of data. However, when some storage-intensive applications like BWA-MEM ported to coprocessors to accelerate, they often have memory bottleneck that severely limits program performance and scalability. While dynamic memory allocation is one of the important topics in CPU and GPU, there has been relatively little work on many-core coprocessors. This paper introduces Mmalloc, a fast and highly scalable allocator that accelerates storage-intensive application on many-core coprocessor. Mmalloc is the first allocator to consider the different architecture between MIC and CPU. Mmalloc removes the global heap to reduce the long-distance on-chip coherent and communication. Mmalloc uses a binary sort interval tree to manage the memory. We also separate the header information from the data area using the logical structure to keep the locality of processed data. Our results on BWA-MEM benchmarks demonstrate that Mmalloc has a better speedup and scalability comparing with the state-of-the-art allocator for CPU like Hoard on the many-core coprocessor.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"43 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":"116980273","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}

{"title":"Automatic Relationship Verification in Online Medical Knowledge Base: a Large Scale Study in SemMedDB","authors":"Danchen Zhang, Daqing He, Ning Zou, Xin Zhou, Fen Pei","doi":"10.1109/BIBM.2018.8621316","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621316","url":null,"abstract":"Automatically generated public medical knowledge bases (KBs), such as SemMedDB, are commonly used in various medical informatic tasks because of their comprehensive coverage. However, due to the imperfectness of the automatic algorithms for generating those KBs, they often contain noisy statements about medical concepts and relationships. For example, the extraction precision of SemRep, the tool used for constructing SemMedDB, is reported be 74.5%. Previous work focused on improving the algorithms for more accurate extraction. In this paper, however, we propose a supervised learning method to automatically verify the medical relationships. Through a study conducted on SemMedDB, we develop a method for generating a large set of training data with a relative small human labor annotation cost. We further propose nine features to characterize each medical relationship instance. After testing on several classifiers, our proposed methods can achieve the best F1 score and Accuracy at 80%, which demonstrates the effectiveness of our approach. In summary, our study demonstrates that noisy relationships in large scale medical KBs can be identified and removed without much human involvement.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"47 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":"117152877","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}

{"title":"Analysis of heart rate response to sleep apnea events in patients with Parkinson’s disease","authors":"Shogo Yata, A. Iyama, S. Sakoda, K. Yoshino","doi":"10.1109/BIBM.2018.8621516","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621516","url":null,"abstract":"Degeneration of the autonomic nervous system is observed in the early stage of Parkinson’s Disease (PD). Because of this, early detection of PD may be possible by detecting abnormalities in autonomic nervous system activity. Although 27-60% of patients with PD have sleep apnea syndrome (SAS), sympathetic cardiac overdrive is observed in SAS patients, while degeneration of the sympathetic nervous system is observed in PD. These findings suggest it may be possible to differentiate patients with PD from patients with SAS using their autonomic nervous response. In this study, we analyzed and compared the heart rate response pattern to sleep apnea events in SAS patients to patterns in PD patients with SAS (PD + SAS). Twenty patients with SAS and 15 patients with PD + SAS underwent overnight polysomnography (PSG) at Toneyama National Hospital. Electrocardiography (ECG), SpO2, and airflow data were measured and used for analysis. Time series of participant’s instantaneous heart rate were calculated from ECG signals. The timing of sleep apnea events was calculated from airflow data. In addition, the (1) latency and (2) amplitude of participant’s heart rate response, area of (3) increasing and (4) decreasing heart rate responses, and (5) participant’s heart rate response during the early phase of SpO2 reduction were calculated for each heart rate response to sleep apnea events. Sleep apnea events were divided into two categories based on whether they were the first event in the consecutive series or not. Results found no statistically significant difference in heart rate response indices between patients with PD and PD + SAS for the first apnea event in a consecutive series. On the contrary, the amplitude and area of increasing heart rate responses as well as heart rate response during the early phase of SpO2 reduction were all statistically significantly lower in patients with PD + SAS than in patients with SAS for all the apnea events except the first one in the consecutive series. These results indicate the attenuation of the autonomic nervous system response to sleep apnea events in patients with PD.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"94 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":"127227058","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}

{"title":"Toxicity Prediction Using Pre-trained Autoencoder","authors":"M. Galushka, Fiona Browne, M. Mulvenna, R. Bond, G. Lightbody","doi":"10.1109/BIBM.2018.8621421","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621421","url":null,"abstract":"Toxicology in the 21st Century (Tox21) is a collaborative initiative whose purpose is to investigate and develop efficient testing approaches to predict the impact chemical compounds have on Humans. In this paper we investigate how a pre-trained auto-encoder can be used to build classifiers capable of predicting the toxicity property of chemical compounds. Using a Deep Learning approach, we performed experiments to deter-mine if chemical compound fingerprints can be used to predict active and inactive compounds based on simplified molecular-input line-entry system (SMILES) in twelve selected assays. We conducted these experiments using data from ChEMBL and Tox21 to investigate how the latent layer produced by an auto-encoder can be used to train a classifier. All experimental results are compared against the winning teams of the Tox21 challenge, where positives and limitations of the proposed approaches are discussed.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"157 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":"127366360","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}

{"title":"Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling","authors":"Leila Yousefi, S. Swift, Mahir Arzoky, L. Sacchi, L. Chiovato, A. Tucker","doi":"10.1109/BIBM.2018.8621484","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621484","url":null,"abstract":"Clinicians predict disease and related complications based on prior knowledge and each individual patient's clinical history. The prediction process is complex due to the existence of unmeasured risk factors, the unexpected development of complications and varying responses of patients to disease over time. Exploiting these unmeasured risk factors (hidden variables) can improve the modeling of disease progression and thus enables clinicians to focus on early diagnosis and treatment of unexpected conditions. However, the overuse of hidden variables can lead to complex models that can overfit and are not well understood (being 'black box' in nature). Identifying and understanding groups of patients with similar disease profiles (based on discovered hidden variables) makes it possible to better understand disease progression in different patients while improving prediction. We explore the use of a stepwise method for incrementally identifying hidden variables based on the Induction Causation (IC*) algorithm. We exploit Dynamic Time Warping and hierarchical clustering to cluster patients based upon these hidden variables to uncover their meaning with respect to the complications of Type 2 Diabetes Mellitus patients. Our results reveal that inferring a small number of targeted hidden variables and using them to cluster patients not only leads to an improvement in the prediction accuracy but also assists the explanation of different discovered sub-groups.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"174 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":"126179582","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}

{"title":"OpenHI - An open source framework for annotating histopathological image","authors":"Pargorn Puttapirat, Haichuan Zhang, Yuchen Lian, Chunbao Wang, Xiangrong Zhang, Lixia Yao, Chen Li","doi":"10.1109/BIBM.2018.8621393","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621393","url":null,"abstract":"Histopathological images carry informative cellular visual phenotypes and have been digitalized in huge amount in medical institutes. However, the lack of software for annotating the specialized images has been a hurdle of fully exploiting the images for educating and researching, and enabling intelligent systems for automatic diagnosis or phenotype-genotype association study. This paper proposes an open-source web framework, OpenHI, for the whole-slide image annotation. The proposed framework could be utilized for simultaneous collaborative or crowd-sourcing annotation with standardized semantic enrichment at a pixel-level precision. Meanwhile, our accurate virtual magnification indicator provides annotators a crucial reference for deciding the grading of each region. In testing, the framework can responsively annotate the acquired whole-slide images from TCGA project and provide efficient annotation which is precise and semantically meaningful. OpenHI is an open-source framework thus it can be extended to support the annotation of whole-slide images from different source with different oncological types. It is publicly available at https://gitlab.com/BioAI/OpenHI/. The framework may facilitate the creation of large-scale precisely annotated histopathological image datasets.","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":"123317544","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}

{"title":"Fast and Accurate Genome-Scale Identification of DNA-Binding Sites","authors":"David Martin, Vincent Maillol, Eric Rivals","doi":"10.1109/BIBM.2018.8621093","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621093","url":null,"abstract":"Discovering DNA binding sites in genome sequences is crucial for understanding genomic regulation. Currently available computational tools for finding binding sites with Position Weight Matrices of known motifs are often used in restricted genomic regions because of their long run times. The ever-increasing number of complete genome sequences points to the need for new generations of algorithms capable of processing large amounts of data. Here we present MOTIF, a new algorithm for seeking transcription factor binding sites in whole genome sequences in a few seconds. We propose a web service that enables the users to search for their own matrix or for multiple JASPAR matrices. Beyond its efficacy, the service properly handles undetermined positions within the genome sequence and provides an adequate output listing for each position the matching word and its score. MOTIF is available through a web interface at http://www.atgc-montpellier.fr/motif.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"95 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":"123585884","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}

{"title":"Aligning Multiple PPI Networks with Representation Learning on Networks","authors":"Bo Song, Jianliang Gao, Hongliang Du, Zheng Chen, Xiaohua Hu","doi":"10.1109/BIBM.2018.8621084","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621084","url":null,"abstract":"Protein-protein interaction (PPI) network alignment has been motivating researches for the comprehension of the underlying crucial biological knowledge, such as conserved evolutionary pathways and functionally conserved proteins throughout different species. Existing PPI network alignment methods have tried to improve the coverage ratio by aligning all proteins from different species. However, there is a fundamental biological justification needed to be acknowledged, that not every protein in a species can, nor should, find homologous proteins in other species. In this paper, we propose a novel approach for multiple PPI network alignment that tries to align only those proteins with the most similarities. To provide more comprehensive supports in computing the similarity, we integrate structural features of the networks together with biological characteristics during the alignment. For the structural features, we apply on PPI networks a representation learning method, which creates a low-dimensional vector embedding with the surrounding topologies of each protein in the network. This approach quantifies the structural features, and provides a new way to determine the topological similarity of the networks by transferring which as calculations in vector similarities. We also propose a new metric for the topological evaluation which can better assess the topological quality of the alignment results across different networks. Both biological and topological evaluations demonstrate our approach is promising and preferable against previous multiple alignment methods.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"121 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":"123706100","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}