2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

{"title":"EEG dynamic source localization using Marginalized Particle Filtering","authors":"B. Ebinger, N. Bouaynaya, P. Georgieva, L. Mihaylova","doi":"10.1109/BIBM.2015.7359727","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359727","url":null,"abstract":"Localization of the brain neural generators that create Electroencephalographs (EEGs) has been an important problem in clinical, research and technological applications related to the brain. The active regions in the brain are modeled as equivalent current dipoles, and the positions and moments of these dipoles or brain sources are estimated. So far, the brain dipoles are assumed to be fixed or time-invariant. However, recent neurological studies are showing that brain sources are not static but vary (in terms of location and moment) depending on various internal and external stimuli. This paper presents a shift in the current paradigm of brain source localization by considering dynamic sources in the brain. We formulate the brain source estimation problem from EEG measurements as a (nonlinear) state-space model. We use the Particle Filter (PF), essentially a sequential Monte Carlo method, to track the trajectory of the moving dipoles in the brain. We further address the “curse of dimensionality,” issue of the PF by taking advantage of the structure of the EEG state-space model, and marginalizing out the linearly evolving states. A Kalman Filter is used to optimally estimate the linear elements, whereas the PF is used to track only the non-linear components. This technique reduces the dimension of the problem; thus exponentially reducing the computational cost. Our simulation results show that, where the PF fails, the Marginalized PF is able to successfully track two dipoles in the brain with only 500 particles.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130181706","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 statistical model of electrostatic isopotential variation in serine protease binding cavities","authors":"Rachel Y. Okun, B. Chen","doi":"10.1109/BIBM.2015.7359859","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359859","url":null,"abstract":"This paper presents EPAC (Electrostatic isoPotential Analytical Comparative model), the first statistical model for evaluating the geometric similarity of electrostatic fields. Beginning with aligned binding cavities, EPAC measures similarity based on the overlapping volume of isopotentials inside ligand binding cavities. We tested the accuracy of our model on two subfamilies of the serine protease superfamily, demonstrating that EPAC effectively identifies binding sites that prefer differently charged substrates. For example, EPAC identified subtle electrostatic variations in proteins that might be expected to be more similar, such as the difference between typical trypsins and a trypsin with a phosphorylated tyrosine nearby the binding site. These results point to applications in the unsupervised comparison of many binding sites from a purely electrostatic perspective, in the search of subtle electrostatic variations that could influence binding specificity.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129117197","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":"Patient portals: Anytime, anywhere","authors":"Subrata Acharya, Gabriel Susai, Manoj Pillai","doi":"10.1109/BIBM.2015.7359785","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359785","url":null,"abstract":"Patient portal is a secure online application which allows patients to have access to their personal health information via Internet around the clock. Patients can view their recent visit information, medications, labs and exams, doctor appointments and communicate with their healthcare providers securely. There have been numerous efforts to build effective patient portals but have not been able to address the cost, security and compliance issues to the fullest. To this effect, the goal of this research is to create a secure patient portal which provides access to patient data, implement all security measures ideal for a healthcare system and to implement OWASP top 10 security measures and industry standard encryption techniques to secure patient data.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907632","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":"Relation dictionary construction and rule learning for PPI extraction from biomedical literatures","authors":"Xiyue Guo, Tingting He, Jie Yuan","doi":"10.1109/BIBM.2015.7359841","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359841","url":null,"abstract":"Using rules to extract protein-protein interactions (PPI) from biomedical literatures has shown recognized positive effect, but the process of making rules is time-costing and expensive. Relation dictionary-based rule is an effective way to solve the problem, while it also encounters a new problem: how to design an excellent dictionary fast and correctly. This paper proposes a weakly supervised method to construct the PPI relation dictionary, and presents a slot-filling method to learn PPI relation rules automatically according to the position of proteins and relation words. Moreover, this method does not depend on much more manual intervention. We conduct the experiment using 5 types of authoritative biomedical PPI corpus, and the results show that our method can improve the PPI extraction effect obviously.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130200030","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":"Using myoelectric signals to recognize grips and movements of the hand","authors":"G. Shuman, Zoran Duric, Daniel Barbará, Jessica Lin, L. Gerber","doi":"10.1109/BIBM.2015.7359712","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359712","url":null,"abstract":"People want to live independently, but too often disabilities or advanced age robs them of the ability to do the necessary activities of daily living (ADLs). Finding relationships between electromyograms measured in the arm and movements of the hand and wrist needed to perform ADLs can help address performance deficits and be exploited in designing myoelectrical control systems for prosthetics and computer interfaces. This paper reports on several machine learning techniques employed to discover the electromyogram patterns present when using the hand to perform 14 typical fine motor functional activities used to accomplish ADLs. Classification and clustering techniques are employed. Improvements to accuracies are introduced, including the use of exponential smoothing and using a symbolic representation to approximate signal streams. Results show the patterns can be learned to an accuracy of approximately 77% for a 15 class problem and the symbolic representation shows the potential for future improvement in accuracies.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125466019","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":"Next generation sequence assembler mis-assembly of phage genomes with terminal redundancy","authors":"Julia D. Warnke-Sommer, I. Thapa, H. Ali","doi":"10.1109/BIBM.2015.7359836","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359836","url":null,"abstract":"Next generation sequencing (NGS) has become the platform of numerous biomedical applications. The study of viral genomes using NGS technologies has led to the characterization of viral species in numerous environments including the human gut microbiome and plant hosts. Many viral genomes are circular or have terminally redundant ends. Circular or linear viral genomes with indeterminate starting and ending points pose a challenge for NGS assemblers, which may erroneously duplicate sections of these genomes. The length of an assembly, often characterized by the N50 length, is frequently used as an indication of an assembly's completeness and even quality. In this paper, we show that the longest contig produced by various assemblers is not always the best assembly for circular or terminally redundant phage genomes and may represent erroneously repeated genomic regions. Results demonstrate that assembly tools may even produce assembled genomes of different lengths for the same species, depending on content inaccurately repeated, leading to results that might be confusing to or inaccurately used by a researcher. To overcome this problem, we introduce strategies for using coverage depth to identify inaccurately repeated content in circular or terminally redundant phage genomes. We conclude the paper by providing the results of assembling two bacteriophage genomes and a bacteriophage metagenomics dataset, highlighting the impact of using the proposed strategies.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663618","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":"Quantitative clinical guidelines for imaging use in evaluation of pediatric cardiomyopathy","authors":"Yuzhe Liu, Vanathi Gopalakrishnan, Shobhit Madan","doi":"10.1109/BIBM.2015.7359910","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359910","url":null,"abstract":"Pediatric cardiomyopathies are a heterogeneous group of disorders in which early detection and treatment can drastically alter the course of the disease. Cardiac MRIs have become an increasingly popular option in evaluating for cardiomyopathies, though they remain expensive and time-consuming compared to echocardiography. Because guidelines for the use of cardiac MRI are vague, we investigated the quantitative characteristics of cardiac echo that predict subsequent positive cardiac MRI. Measurements were extracted from echo reports, processed, and fed into a Bayesian rule learning system. We discovered that ejection fraction and interventricular septum thickness were particularly important predictors of positive cardiac MRI. These features may help justify obtaining a cardiac MRI when echo results are inconclusive.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121317704","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":"Predicting diverse M-best protein contact maps","authors":"S. Sun, Jianzhu Ma, Sheng Wang, Jinbo Xu","doi":"10.1109/BIBM.2015.7359865","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359865","url":null,"abstract":"Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolutionary coupling (EC) analysis, which predicts contacts by detecting co-evolved residues (or columns) in a multiple sequence alignment (MSA), has made good progress due to better statistical assessment techniques and high-throughput sequencing. Existing EC analysis methods predict only a single contact map for a given protein, which may have low accuracy especially when the protein under prediction does not have a large number of sequence homologs. Analogous to ab initio folding that usually predicts a few possible 3D models for a given protein sequence, this paper presents a novel structure learning method that can predict a set of diverse contact maps for a given protein sequence, in which the best solution usually has much better accuracy than the first one. Our experimental tests show that for many test proteins, the best out of 5 solutions generated by our method has accuracy at least 0.1 better than the first one when the top L/5 or L/10 (L is the sequence length) predicted long-range contacts are evaluated, especially for protein families with a small number of sequence homologs. Our best solutions also have better quality than those generated by the two popular EC methods Evfold and PSICOV.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114277132","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":"UIMA based solution in pharma text","authors":"Aditya Rao, Thomas Joseph, V. Saipradeep, Rajgopal Srinivasan","doi":"10.1109/BIBM.2015.7359958","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359958","url":null,"abstract":"Background: Text-processing of unstructured biomedical text has become crucial to pharma companies, both with regards to legacy as well as topical documentation. The Apache Unstructured Information Management Applications (UIMA) framework addresses general information extraction requirements. We present in this poster two use cases of using UIMA for specific unstructured biomedical information extraction tasks in pharma companies. The first use case requires extraction of values belonging to specific fields from legacy clinical study documents. These fields could be diverse, examples being study duration, study population, study arm, completion date and co-morbidity. The second use case deals with accurate propagation of drug label information to digital channels such as drug-specific websites. Due to the increased importance of such websites and mobile applications, pharma companies are looking at text-processing solutions to keep information in such channels accurate and up-to-date. Implementation: The use cases were implemented using the UIMA framework. The framework comprises of core UIMA modules and custom in-house modules specifically built for each of the use cases. Some of the key custom modules include document clustering, section identification, named entity recognition and relation-identification. For the first use case, a total of 70 fields were extracted from clinical study reports. These included study phase, study type, study duration, study start date and the drug dosage. For the second use case, content extraction was first done on drug-websites, and fields such as target dosage, dosage regimen and study duration were then extracted from the content. The field values were evaluated for accuracy against the label information. Conclusion: Both implementations were successful, with high degree of precision and recall. The second use case has successfully moved from proof-of-concept to pilot phase. While there is a requirement for comprehensive knowledge management solutions dealing with exploration and management of biomedical text within the big data umbrella in pharma, we have seen that there also exist small and specific problems in the within the industry that can benefit from bespoke text-processing solutions built around frameworks such as UIMA.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328711","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":"Structure-based protein folding type classification and folding rate prediction","authors":"Balachandran Manavalan, K. Kuwajima, InSuk Joung, Jooyoung Lee","doi":"10.1109/BIBM.2015.7359953","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359953","url":null,"abstract":"Protein folding rate is one of the important properties of a protein. Protein folding rate prediction is useful for understanding protein folding process and guiding protein design. In this study, we developed a support vector machine (SVM) based method to predict protein folding kinetic types (two-state or non-two-state) and the real-value folding rate using the features calculated from the three-dimensional structure such as contact order, various properties from the non-local contact clusters, secondary structural information and sequence length. We systematically studied the contributions of individual features to folding rate prediction. Based on the highest contributions of individual features, we trained our machine using leave one out cross-validation and tested on a testing dataset. The Pearson correlation coefficient, mean absolute difference and root mean square error between the predicted and experimental folding rates (base-10 logarithmic scale) are 0.814, 0.752 and 0.910 for two-state proteins, and 0.860, 0.687 and 0.876 for non-two-state proteins. Moreover, our method predicts whether a protein of known atomic structure folds according to two-state or non-two-state kinetics and correctly classifies 80% of the folding mechanism on a testing dataset. Finally, we evaluated the performance of our method along with the other eight existing protein folding rate prediction tools on non-overlapping benchmarking dataset. The prediction performance will also be reported and discussed.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122619835","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}