Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献

{"title":"Uncertainty Quantified Computational Analysis of the Energetics of Virus Capsid Assembly.","authors":"N Clement, M Rasheed, C Bajaj","doi":"10.1109/BIBM.2016.7822775","DOIUrl":"10.1109/BIBM.2016.7822775","url":null,"abstract":"<p><p>Most of the existing research in assembly pathway prediction/analysis of viral capsids makes the simplifying assumption that the configuration of the intermediate states can be extracted directly from the final configuration of the entire capsid. This assumption does not take into account the conformational changes of the constituent proteins as well as minor changes to the binding interfaces that continue throughout the assembly process until stabilization. This paper presents a statistical-ensemble based approach which samples the configurational space for each monomer with the relative local orientation between monomers, to capture the uncertainties in binding and conformations. Furthermore, instead of using larger capsomers (trimers, pentamers) as building blocks, we allow all possible subassemblies to bind in all possible combinations. We represent the resulting assembly graph in two different ways: First, we use the Wilcoxon signed rank measure to compare the distributions of binding free energy computed on the sampled conformations to predict likely pathways. Second, we represent chemical equilibrium aspects of the transitions as a Bayesian Factor graph where both associations and dissociations are modeled based on concentrations and the binding free energies. We applied these protocols on the feline panleukopenia virus and the <i>Nudaurelia capensis</i> virus. Results from these experiments showed significant departure from those one would obtain if only the static configurations of the proteins were considered. Hence, we establish the importance of an uncertainty-aware protocol for pathway analysis, and provide a statistical framework as an important first step towards assembly pathway prediction with high statistical confidence.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"1706-1713"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604467/pdf/nihms894982.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35431193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptional Responses to Ultraviolet and Ionizing Radiation: An Approach Based on Graph Curvature.","authors":"Yongxin Chen,&nbsp;Jung Hun Oh,&nbsp;Romeil Sandhu,&nbsp;Sangkyu Lee,&nbsp;Joseph O Deasy,&nbsp;Allen Tannenbaum","doi":"10.1109/BIBM.2016.7822706","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822706","url":null,"abstract":"<p><p>More than half of all cancer patients receive radiotherapy in their treatment process. However, our understanding of abnormal transcriptional responses to radiation remains poor. In this study, we employ an extended definition of Ollivier-Ricci curvature based on LI-Wasserstein distance to investigate genes and biological processes associated with ionizing radiation (IR) and ultraviolet radiation (UV) exposure using a microarray dataset. Gene expression levels were modeled on a gene interaction topology downloaded from the Human Protein Reference Database (HPRD). This was performed for IR, UV, and mock datasets, separately. The difference curvature value between IR and mock graphs (also between UV and mock) for each gene was used as a metric to estimate the extent to which the gene responds to radiation. We found that in comparison of the top 200 genes identified from IR and UV graphs, about 20~30% genes were overlapping. Through gene ontology enrichment analysis, we found that the metabolic-related biological process was highly associated with both IR and UV radiation exposure.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"1302-1306"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2016.7822706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34784321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Use Status for Dietary Supplements in Clinical Notes.","authors":"Yadan Fan,&nbsp;Lu He,&nbsp;Rui Zhang","doi":"10.1109/BIBM.2016.7822668","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822668","url":null,"abstract":"<p><p>Clinical notes contain rich information about dietary supplements, which are critical for detecting signals of dietary supplement side effects and interactions between drugs and supplements. One of the important factors of supplement documentation is usage status, such as started and discontinuation. Such information is usually stored in the unstructured clinical notes. We developed a rule-based classifier to identify supplement usage status in clinical notes. The categories referring to the patient's status of supplement use were classified into four classes: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). Clinical notes containing 10 of the most commonly consumed supplements (i.e., alfalfa, echinacea, fish oil, garlic, ginger, ginkgo, ginseng, melatonin, St. John's Wort, and Vitamin E) were retrieved from the University of Minnesota Clinical Data Repository. The gold standard was defined by manually annotating 1000 randomly selected sentences or statements mentioning at least one of these 10 supplements. The rules in the classifier was initially developed on two-thirds of the set of 7 supplements (i.e., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E); the performance was evaluated on the remaining one-third of this set. To evaluate the generalizability of rules, we further validated the second testing set on other 3 supplements (i.e., echinacea, fish oil, and melatonin). The performance of the classifier achieved F-measures of 0.95, 0.97, 0.96, and 0.96 for status C, D, S, and U on 7 supplements, respectively. The classifier also showed good generalizability when it was applied to the other 3 supplements with F-measures of 0.96 for C, 0.96 for D, 0.95 for S, and 0.89 for U. This study demonstrated that the classifier can accurately classify supplement usage status, which can be further integrated as a module into the existing natural language processing pipeline for supporting dietary supplement knowledge discovery.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"1054-1061"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2016.7822668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35428398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins.","authors":"Hamid Reza Hassanzadeh, May D Wang","doi":"10.1109/bibm.2016.7822515","DOIUrl":"10.1109/bibm.2016.7822515","url":null,"abstract":"<p><p>Transcription factors (TFs) are macromolecules that bind to cis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several in vivo and in vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"178-183"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302108/pdf/nihms-1595286.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38060153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy.","authors":"Rongjian Li, Dong Si, Tao Zeng, Shuiwang Ji, Jing He","doi":"10.1109/BIBM.2016.7822490","DOIUrl":"10.1109/BIBM.2016.7822490","url":null,"abstract":"<p><p>The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å ). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as <i>α</i>-helix, <i>β</i>-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"41-46"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952046/pdf/nihms874389.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36106213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival.","authors":"Hamid Reza Hassanzadeh,&nbsp;John H Phan,&nbsp;May D Wang","doi":"10.1109/bibm.2016.7822516","DOIUrl":"https://doi.org/10.1109/bibm.2016.7822516","url":null,"abstract":"<p><p>Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients' survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"184-189"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm.2016.7822516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38151657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Temporal Constraints in Qualitative Eligibility Criteria of Cancer Clinical Studies.","authors":"Zhe He,&nbsp;Zhiwei Chen,&nbsp;Jiang Bian","doi":"10.1109/BIBM.2016.7822607","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822607","url":null,"abstract":"<p><p>Clinical studies, especially randomized controlled trials, generate gold-standard medical evidence. However, the lack of population representativeness of clinical studies has hampered their generalizability to the real-world population. Overly restrictive qualitative criteria are often applied to exclude patients. In this work, we develop a lexical-pattern-based tool to structure qualitative eligibility criteria with temporal constraints, with which we analyzed over 10,800 cancer clinical studies. Our results showed that restrictive temporal constraints are often applied on qualitative criteria in cancer studies, limiting the generalizability of their results.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"717-722"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2016.7822607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35676265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CHALLENGES IN MATCHING SECONDARY STRUCTURES IN CRYO-EM: AN EXPLORATION.","authors":"Devin Haslam,&nbsp;Mohammad Zubair,&nbsp;Desh Ranjan,&nbsp;Abhishek Biswas,&nbsp;Jing He","doi":"10.1109/BIBM.2016.7822776","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822776","url":null,"abstract":"<p><p>Cryo-electron microscopy is a fast emerging biophysical technique for structural determination of large protein complexes. While more atomic structures are being determined using this technique, it is still challenging to derive atomic structures from density maps produced at medium resolution when no suitable templates are available. A critical step in structure determination is how a protein chain threads through the 3-dimensional density map. A dynamic programming method was previously developed to generate <i>K</i> best matches of secondary structures between the density map and its protein sequence using shortest paths in a related weighted graph. We discuss challenges associated with the creation of the weighted graph and explore heuristic methods to solve the problem of matching secondary structures.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"1714-1719"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2016.7822776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36106214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sparse Canonical Correlation Analysis via Truncated <i>ℓ</i>₁-norm with Application to Brain Imaging Genetics.","authors":"Lei Du,&nbsp;Tuo Zhang,&nbsp;Kefei Liu,&nbsp;Xiaohui Yao,&nbsp;Jingwen Yan,&nbsp;Shannon L Risacher,&nbsp;Lei Guo,&nbsp;Andrew J Saykin,&nbsp;Li Shen","doi":"10.1109/BIBM.2016.7822605","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822605","url":null,"abstract":"<p><p>Discovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the <i>ℓ</i>₁-norm or its variants. The <i>ℓ</i>₀-norm is more desirable, which however remains unexplored since the <i>ℓ</i>₀-norm minimization is NP-hard. In this paper, we impose the truncated <i>ℓ</i>₁-norm to improve the performance of the <i>ℓ</i>₁-norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"707-711"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2016.7822605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35426546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Absorbable MicroRNA Prediction based on an Ensemble Manifold Ranking Model.","authors":"Jiang Shu,&nbsp;Kevin Chiang,&nbsp;Dongyu Zhao,&nbsp;Juan Cui","doi":"10.1109/BIBM.2015.7359697","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359697","url":null,"abstract":"<p><p>MicroRNAs, a class of short non-coding RNAs, are able to regulate more than half of human genes and affect many fundamental biological processes. It has been long considered synthesized endogenously until very recent discoveries showing that human can absorb exogenous microRNAs from dietary resources. This finding has raised a challenge scientific question: which exogenous microRNAs can be integrated into human circulation and possibly exert functions in human? Here we present a well-designed ensemble manifold ranking model for identifying human absorbable exogenous miRNAs from 14 common dietary species. Specifically, we have analyzed 4,910 dietary microRNAs with 1,120 features derived based on the microRNA sequence and structure. In total, 70 discriminative features were selected to characterize the circulating microRNAs in human and have been used to infer the possibility of a certain exogenous microRNA getting integrated into human circulation. Finally, 461 dietary microRNAs have been identified as transportable exogenous microRNAs. To assess the performance of our ensemble model, we have validated the top predictions through a milk-feeding study. In addition, 26 microRNAs from two virus species were predicted as transportable and have been validated in two external experiments. The results demonstrate the data-driven computational model is highly promising to study transportable microRNAs while bypassing the complex mechanistic details.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2015 ","pages":"295-300"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36655301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}