IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences最新文献

{"title":"Efficient computation of the Damerau-Levenshtein distance between biological sequences","authors":"Chunchun Zhao, S. Sahni","doi":"10.1109/ICCABS.2017.8114295","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114295","url":null,"abstract":"We have developed linear space algorithms to compute the Damerau-Levenshtein (DL) distance [1], [2] between two strings and also to find a sequence of edit operations of length equal to the DL distance (optimal trace). Our algorithms require O(s min{m, n} + m + n) space, where s is the size of the alphabet and m and n are, respectively, the lengths of the two strings. Previously known algorithms require O(mn) space. Cache efficient and multi-core linear-space algorithms have also been developed. The cache miss efficiency of the algorithms was analyzed using a simple cache model.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"1 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76060352","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":"Ensemble learning algorithm for drug-target interaction prediction","authors":"Sudipta Pathak, Xingyu Cai","doi":"10.1109/ICCABS.2017.8114292","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114292","url":null,"abstract":"Predicting drug-target interaction through simulation is an immensely important problem. It has a huge impact in drug discovery in pharmaceutical industry. FDA reports that it takes close to five billion dollars to introduce a new drug to the market. A slight improvement in accuracy of prediction in the domain may save millions of dollars in the investment, there by lowering down the cost of production and making drugs more affordable to its consumers. We proposed a new algorithm to combine multiple heterogeneous information for identification of new interactions between the drugs and targets. The algorithm proposed in this paper employs the stacking based approach namely KronRLS-Stacking, to combine models in a linear (or non-linear way), to address the drug-target interaction prediction problem. Our Algorithm is developed on top of RLS and KronRLS algorithms. The novelty of our approach is in combining heterogeneous sources of information using ensemble method called Stacking. Also, our algorithm is embarrassingly parallel and easy to distribute over multiple computing nodes. We compared our results with seventeen other algorithms. Like the other algorithms, we use Area Under Precision Recall (AUPR) curve as a measurement of goodness. We compared our results on Nuclear Receptor(NR), GPCR, Ion Channel(IC) and Enzyme(E) datasets respectively. KronRLS-Stacking obtained highest AUPR in NR, GPCR and IC datasets. In the experiments, we take average over five runs for all the datasets. For each run we performed a 5-fold cross validation. We chose the top 10 best performing kernels on the validation set to generate all results for testing datasets. Even though KronRLS-Stacking offers slightly worse standard deviation, our lowest AUPR score is still better than the best performing algorithms we compared with.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"33 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79175679","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":"Calibration of stochastic biochemical models against behavioral temporal logic specifications","authors":"Sumit Kumar Jha, Arfeen Khalid","doi":"10.1109/ICCABS.2017.8114285","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114285","url":null,"abstract":"Calibrating stochastic biochemical models against experimental insights remains a critical challenge in biological design automation. Stochastic biochemical models incorporate the uncertainty inherent in the system being modeled, thus demanding meticulous calibration techniques. We present an approach for calibrating stochastic biochemical models such that the calibrated model satisfies a given behavioral temporal logic specification with a given probability. Model calibration is defined as an optimization problem that aims to minimize a cost function that computes either a qualitative or a quantitative measure of distance between the parameterized stochastic biochemical model and the expected behavioral specification. To minimize this distance, our approach combines various statistical hypothesis testing methods with automated runtime monitoring of high-level temporal logic specifications against time-series data obtained by simulating stochastic models. We apply sequential probability ratio test (SPRT) and Bayesian statistical model checking (BSMC) when the distance between the model and the behavioral specification is a qualitative value. Alternatively, when the distance is a quantitative value describing how well a specification is satisfied by the model, we use a hypothesis test to sequentially select between two distributions of the distance metric that has the larger mean. Such tests describe the stopping condition to reduce the number of samples required for discovering the correct parameter values. We demonstrate the potential of our approach on two examples using agent-based models implemented in SPARK and rule-based models implemented in BioNetGen modeling languages. The distance between a candidate biochemical model and an expected behavior encoded in temporal logic can be used to drive a local or global search technique during the model calibration process. Our approach follows Simulated Annealing as the global search algorithm that avoids local minima by accepting inferior solutions, at high temperatures, with a very low probability. The problem of stochastic model calibration against behavioral temporal logic specifications has numerous applications in science and engineering and has been widely studied. Our algorithmic approach towards this problem may be an important component of future biological design automation software suite.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"251 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76140262","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":"Semantics-oriented data science and computational life sciences: Innovative application of semantic technologies in microRNA and lncRNA research","authors":"Jingshan Huang, D. Dou, M. Tan, G. Borchert, K. Eilbeck, A. Ruttenberg, Ping Yang","doi":"10.1109/ICCABS.2017.8114284","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114284","url":null,"abstract":"Semantic technologies (based upon domain ontologies) have been widely adopted in various biological, biomedical, and clinical research areas. Consequently, semantics-oriented data science and computational life sciences have increasingly attracted scientists' attention. In our previous efforts, we have developed a semantic data integration and search software tool named OmniSearch [1], which was specifically designed for the microRNA (miRNA or miR) domain. OmniSearch has been successfully applied in numerous biomedical and clinical areas [2-5] such as: chronic obstructive pulmonary disease (COPD) and lung cancer (LC), diabetes and diabetic kidney disease (DKD), osteoarthritis (OA), glioblastoma (GBM), and pediatric acute lymphoblastic leukemia (ALL). We are currently working on OmniSearch Plus, which was extended from OmniSearch and will cover important non-coding RNA (ncRNA) molecules other than miRNAs, including long ncRNAs (lncRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs).","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"17 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73331792","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":"On problem of orienting ordered scaffolds","authors":"S. Aganezov, M. Alekseyev","doi":"10.1109/ICCABS.2017.8114312","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114312","url":null,"abstract":"Introduction: There exists a number of methods that attempt to reconstruct a genome from a set of scaffolds. To do so, they (i) determine the order of scaffolds; and (ii) determine the orientation (i.e., strand of origin) of scaffolds. Some methods attempt to solve these subproblems jointly by using various types of additional data including jumping libraries, long error-prone reads, homology relationships between genomes, etc. Other methods (typically based on wet-lab experiments) can often reliably reconstruct the order of scaffolds, but may fail to impose their orientation. This inspires us to consider the special case of the problem (ii) where the order of scaffolds is given.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"4 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73759555","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":"Classification of HCV infections through sequence image normalization","authors":"S. Basodi, P. Icer, P. Skums, Y. Khudyakov, A. Zelikovsky, Yi Pan","doi":"10.1109/ICCABS.2017.8114313","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114313","url":null,"abstract":"Identification of Hepatitis C virus (HCV) infections is crucial in determining viral outbreaks. HCV has an affinity to lead towards chronic infection with time due to its highly mutable nature. This leads to increase in heterogeneous population of genetically related HCV variants in the affected individuals. To our knowledge, there are no reliable diagnostic assays for distinguishing acute and chronic HCV infections. Providing a robust classification scheme for the staging of viral infection requires identification of prominent features which in this case can be done using domain knowledge. Simple genetic heterogeneity metrics are not sufficient to represent HCV infections accurately as features for the classification algorithms. This is due to complexity of structural development of intra-host populations, which are affected by bouts of selective sweeps and negative selection during chronic infection [1], [2]. Although some machine learning models are known to work well for sequence data for classification problems, their straightforward application to viral genomic data is problematic, since the number of viral sequences and the structures of intra-host viral populations are not consistent across various samples. We propose a novel preprocessing approach to transform irregular viral genomic data into a normalized image data. Such representation allows to apply powerful machine learning algorithms to the problem of classification of recent and chronic HCV infections. Our dataset consists of intra-host HCV populations of a highly heterogeneous genomic region HVR1, collected from 108 recently and 257 chronically infected individuals sampled by next-generation sequencing. We train several classification models using stratified 10-fold cross validation on the transformed image data. SVM classification model achieves the highest accuracy of 98% and also has more than 95% of precision, recall and F1_Score metrics, for both acute and chronically HCV infected individuals.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"85 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90166307","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":"Challenges of using RNA-seq in the clinical setting","authors":"Jaime I. Dávila","doi":"10.1109/ICCABS.2017.8114283","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114283","url":null,"abstract":"RNA-seq is a mature and well-established method for studying the complexity of the transcriptome in the research setting. As this method moves from the research realm to the clinical context, new opportunities for the development of bioinformatics methods arise. During this talk I will present some of the challenges we have found during our work to release a clinical test for tumor samples using RNA-seq. During the first part of the talk I will focus on fusion detection, how it is affected by the degradation of the sample and how to quantify such effect using Fusion Sense [1]. I will also comment on the opportunities and challenges of annotating and predicting the clinical importance of fusions. During the second part of the talk I will comment on variant calling in RNA-seq and how to account for the effects of library preparation by using RVboost [2]. I will then show some preliminary work of leveraging this method in the context of estimating tumor mutational burden in Formalin-Fixed Paraffin-Embedded (FFPE) samples.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"28 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88735608","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":"GRASP2: Fast and memory-efficient gene-centric assembly and homolog search","authors":"Cuncong Zhong, Youngik Yang, Shibu Yooseph","doi":"10.1109/ICCABS.2017.8114296","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114296","url":null,"abstract":"A crucial task for metagenomic analysis is to annotate the function and taxonomy of the sequencing reads generated from a microbiome sample. In general, the reads can either be assembled into contigs and searched against reference databases, or individually searched without assembly. The first approach may suffer due to the fragmentary and incomplete nature of nucleotide sequence assembly, while the second approach is hampered by the reduced functional signal that a short read can contain. To tackle these issues, we previously developed GRASP (Guided Reference-based Assembly of Short Peptides), which accepts a reference protein sequence as input and aims to assemble its homologs from a database containing fragmentary protein sequences. In addition to a gene-centric assembly tool, GRASP also serves as a homolog search tool when using the assembled protein sequences as templates to recruit reads. GrASP has significantly improved sensitivity (60–80% vs. 30–40%) compared to other homolog search tools such as BLAST. However, GRASP is time- and space-consuming compared to these tools, and is not scalable to large datasets. Subsequently, we developed GRASPx which is 30X faster than GRASP. Here, we present a completely redesigned algorithm, GRASP2, for this computational problem. GRASP2 utilizes Burrow-Wheeler Transformation (BWT) to assist with assembly graph generation, and reduces the search space by employing a fast ungapped alignment strategy to reduce unnecessary traversal of non-homologous paths in the assembly graph. GRASP2 is 8-fold faster than GRASPx (and 250-fold faster than GRASP) and uses 8-fold less memory while maintaining the original high sensitivity of GRASP, which makes GRASP2 a useful tool for metagenomics data analysis. GRASP2 is implemented in C++ and is freely available from http://www.sourceforge.net/projects/grasp2.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"41 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88757334","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 whole genome sequence and genome-wide SNPs in highly inbred pigs","authors":"J. Huo, Wenmin Chen, Xiaowei Wu, Kuan Yang, Weirong Pan, Liqing Zhang, Yangzhi Zeng","doi":"10.1109/ICCABS.2017.8114286","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114286","url":null,"abstract":"Not only fulfilling a large portion of the worldwide meat consumption, pigs also serve as a model organism in biomedical studies due to the shared similarity with humans at both physiological and genetic levels. However, as a diploid organism, a normal pig holds two versions of genetic code simultaneously, creating an obstacle for many studies in the related field. For the first time in history, we have been able to successfully inbred pigs for more than three decades. In this paper, we sequenced and analyzed the genome of a highly inbred miniature Chinese pig strain, Banna Mini-pig Inbred Line (BMI). This specific strain of pigs has been inbred for 24 generations, the longest inbreeding history ever published. In contrast to the high level of heterozygosis in Chinese pigs, the BMI pigs show high level of homozygosis and a clear pattern on the distribution of heterozygosis along the genome. Less than 0.5% of all the short variants identified are located in coding regions, suggesting high homozygosis at the transcriptome level. We also conducted genome-wide SNP genotyping in 48 inbred pigs that are in different generations of inbreeding. Results show that overall the homozygosity of the pigs increases with the higher generation of inbreeding and comparison of the SNPs among the inbred lines shows clear trend reflecting the inbreeding history of the pigs. Thus both the whole genome sequence and SNP genotyping demonstrate that the prolonged inbreeding has led to the inbred lines to have nearly identical homologous chromosomes, and thus can provide homozygotic genes and clear genetic background. Our result could reduce the gap of linking phenotypes with genotypes, facilitate the building of pharmaceutical pig models, and shed light on the effect of extensive inbreeding.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"1 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79938340","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":"riboStreamR: A web application for quality control, analysis, and visualization of Ribo-seq data","authors":"Patrick Perkins, S. Heber","doi":"10.1109/ICCABS.2017.8114317","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114317","url":null,"abstract":"Ribo-seq is a popular technique for studying translation and its regulation. Various software tools for data preprocessing, quality assessment, analysis, and visualization of Ribo-seq data have been developed. However, many of them are inaccessible to users without a thorough practical knowledge of software applications, and often multiple different tools have to be used in combination with each other. Here, we present riboStreamR, a comprehensive Ribo-seq quality control (QC) platform in the form of an R Shiny web application. RiboStreamR provides visualization and analysis tools for various Ribo-seq QC metrics, including read length distribution, read periodicity, and translational efficiency. The platform's environment is centered on providing a user-friendly experience, and includes numerous options for graphical customization and report generation. In practice, Ribo-seq data analysis can be sensitive to data quality issues such as read length variation, low read periodicities, and contaminations with ribosomal and transfer RNA. What constitutes ‘high quality’ data is often unclear. Our goal is to develop novel functionality to automatically highlight quality issues and anomalies in the data. This NSF-supported project is performed in collaboration with Jose Alonso, Anna Stepanova, Serina Mazzoni-Putman, and Cranos Williams.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"1 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74892439","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}