2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第8页

Attention and concentration in normal and deaf gamers 正常和聋人游戏玩家的注意力和集中力

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621513

A. Teixeira, A. Tomé, L. Roseiro, A. Gomes

{"title":"Attention and concentration in normal and deaf gamers","authors":"A. Teixeira, A. Tomé, L. Roseiro, A. Gomes","doi":"10.1109/BIBM.2018.8621513","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621513","url":null,"abstract":"In this research the performance of individuals with normal hearing and impaired hearing while playing a computer game was evaluated. The aim was to study and understand if impaired hearing gamers are at disadvantage when playing games without being able to hear the music. Three levels (attention, concentration and blinking) were measured to compare and understand how sound can influence players’ attention and concentration performance. The data was recorded using Mindwave equipment during the game Outlast considering two scenarios: game with sound and game without sound. The results show that hearing impaired individuals have the same standard of attention and level of concentration as individuals with normal hearing when there is sound in the game. In the case of the blinking level, this is quite different between the scenarios and the analyzed groups. For this particular study the results suggest that sound is not an important level in the attention and concentration performance of impaired hearing players. Although much work still needs to be done, there is evidence of a relation between the attention and the concentration levels between normal hearing and impaired hearing individuals in the presence and absence of sound.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"153 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":"124649858","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: 1

A Machine Learning Emotion Detection Platform to Support Affective Well Being 支持情感健康的机器学习情感检测平台

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621562

Michael Healy, R. Donovan, P. Walsh, Huiru Zheng

引用次数: 22

Clustering and Comparison of Hierarchies in the Spatial Organization of Chromatin 染色质空间组织中层次结构的聚类与比较

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621097

O. Pushkareva, A. Kurashenko, Uliana Moskvina, A. Rubinov, M. Gelfand

引用次数: 0

Using deep neural network to recognize mutation entities in biomedical literature 利用深度神经网络识别生物医学文献中的突变实体

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621134

Fan Tong, Zheheng Luo, Dongsheng Zhao

{"title":"Using deep neural network to recognize mutation entities in biomedical literature","authors":"Fan Tong, Zheheng Luo, Dongsheng Zhao","doi":"10.1109/BIBM.2018.8621134","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621134","url":null,"abstract":"Automatic recognizing mutation mentions plays a fundamental and critical role in extracting variant-disease relation from biomedical literature. In this paper, we proposed an advanced model for mutation mentions detection by using deep network in combination with decoding algorithm and regular expression. Inspired by the distributed representation of words and characters, we divide each word by letters of difference case, numbers and special characters into tokens for training a token embedding which can capture some nomenclature features of mutations. To build the network, we implemented Bi-directional LSTM (long short-term memory) layers to learn a general form of mutation mentions while capture long-term context information and fully-connected layers to improve the fitting capability, using concatenation of word vectors training from token embeddings as the input. Viterbi algorithm was used to decode the previous output to access initial labeled sequence. On top of that, regular expression patterns were used to label the mutation mentions, which provided extra information to optimize the initial output. While training and testing on NCBI tmVar mutation corpus, our model achieved F-score of 91.59% which performed better than current reported systems.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"12 5 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":"124774234","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

Random Subspace Projection for Predicting Biogeographical Ancestry 预测生物地理祖先的随机子空间投影

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621222

T. T. Toma, Tayo Olufemi-Ajayi, J. Dawson, D. Adjeroh

引用次数: 2

Analysis of Gene Expression Data of RPL10 Mutant T-Cell Leukemia by SEMsubPA RPL10突变型t细胞白血病基因表达数据的SEMsubPA分析

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621215

D. Pepe, K. D. Keersmaecker

{"title":"Analysis of Gene Expression Data of RPL10 Mutant T-Cell Leukemia by SEMsubPA","authors":"D. Pepe, K. D. Keersmaecker","doi":"10.1109/BIBM.2018.8621215","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621215","url":null,"abstract":"This paper describes the analysis of T-cell acute lymphoblastic leukemia (T-ALL) samples with an R98S missense mutation in ribosomal protein L10 (RPL10) compared to samples affected by T-ALL but without the mutation. The goal was to characterize the effect of RPL10 mutations on mRNA gene expression level. To this end, a novel tool called SEMsubPA was used, which allowed to detect significant KEGG sub-pathways and differentially expressed genes (DEGs) in one step. The tool exploits the potential of multi-group structural equation modeling for the discovery of the significant sub-pathways. Furthermore, it allows to test the significance of the connections between the genes in each significant sub-pathway. The most relevant components of the final biological network were characterized by Gene Ontology enrichment analysis based on Biological Process (BP) and Molecular Functions (MF). The analysis revealed key sub-pathways involved in necroptosis, MAPK signaling pathway and T-cell receptor signaling pathways. In addition, the network and enrichment analyses discovered key cancer genes such as AKT1, RIPK1, RIPK3, MYC and H1F1A as well as important molecular functions such as cellular oxidative stress, protein folding and kinase activity. Finally, the performance of SEMsubPA was compared against 3 other pathway and one sub-pathway analysis method. SEMsubPA was by far the best, detecting 81% of the total number of reference pathways, whereas the maximum performance of the other methods was 5%.","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":"125074463","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

BIBM 2018 Cover Page BIBM 2018封面

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/bibm.2018.8621544

引用次数: 0

TE-nester: a recursive software tool for structure-based discovery of nested transposable elements TE-nester:用于基于结构发现嵌套转置元素的递归软件工具

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621071

M. Lexa, Radovan Lapar, Pavel Jedlička, Ivan Vanat, M. Cervenanský, E. Kejnovský

{"title":"TE-nester: a recursive software tool for structure-based discovery of nested transposable elements","authors":"M. Lexa, Radovan Lapar, Pavel Jedlička, Ivan Vanat, M. Cervenanský, E. Kejnovský","doi":"10.1109/BIBM.2018.8621071","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621071","url":null,"abstract":"Eukaryotic genomes are generally rich in repetitive sequences. LTR retrotransposons are the most abundant class of repetitive sequences in plant genomes. They form segments of genomic sequences that accumulate via individual events and bursts of retrotransposition. A limited number of tools exist that can identify fragments of repetitive sequences that likely originate from a longer, originally unfragmented element, using mostly sequence similarity to guide reconstruction of fragmented sequences. Here, we use a slightly different approach based on structural (as opposed to sequence similarity) detection of unfragmented full-length elements, which are then recursively eliminated from the analyzed sequence to repeatedly uncover unfragmented copies hidden underneath more recent insertions. This approach has the potential to detect relatively old and highly fragmented copies. We created a software tool for this kind of analysis called TE-nester and applied it to a number of assembled plant genomes to discover pairs of nested LTR retrotransposons of various age and fragmentation state. TEnester will allow us to test hypotheses about genome evolution, TE life cycle and insertion history. The software, still under improvement, is available for download from a repository at https://gitlab.fi.muni.cz/lexa/nested.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 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":"129719059","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: 3

An approximation method of extremely low p-values using permutation test 利用置换检验的极低p值近似方法

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621082

Sangseob Leem, T. Park

{"title":"An approximation method of extremely low p-values using permutation test","authors":"Sangseob Leem, T. Park","doi":"10.1109/BIBM.2018.8621082","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621082","url":null,"abstract":"The permutation test is a non-parametric method for assessing statistical significance and this method is widely used in a variety of(many) disciplines including bioinformatics. The permutation test is very useful in situations where a null distribution of test statistics is unknown or hard to determine. In permutation tests, p-values calculated by a proportion of the number of statistical values of randomly shuffled data, where the values are more extreme than, or equal to, statistical values of observed data, among the total number of permutations. In this method, the precision of significance depends on the number of permutations although computation time precludes achieving extremely low p-values.In this paper, we propose a novel strategy for approximating extremely low p-values. If two differently sized data sets show similar patterns, the smaller data set has a higher p-value than the larger one. In other words, dividing data simplifies assessing significances of subsets by a permutation test because of relatively large p-values. P-values of the subsets are then integrated into a final p-value as a meta-analysis. Our proposed method consists of two steps: (1) divide data into subsets and perform permutation tests for the subsets; and (2) integrate p-values by Stouffer’s z-score method. We herein demonstrate and validate our method using simulation studies. Those assessments show that p-values of about 1.0e-20 might (could) be well-estimated by the proposed method in a single day for samples larger than 5,000.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"25 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":"129793437","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: 1

Scrutinizing functional interaction networks from RNA-binding proteins to their targets in cancer 审视从rna结合蛋白到癌症靶标的功能相互作用网络

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621502

Sajal Kumar, Hua Zhong, Ruby Sharma, Yiyi Li, Mingzhou Song

{"title":"Scrutinizing functional interaction networks from RNA-binding proteins to their targets in cancer","authors":"Sajal Kumar, Hua Zhong, Ruby Sharma, Yiyi Li, Mingzhou Song","doi":"10.1109/BIBM.2018.8621502","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621502","url":null,"abstract":"RNA-binding proteins (RBPs) participate in all stages of RNA life cycle from transcription, splicing, to translation. Under the ENCODE project, a large number of RBPs were knocked down in human cancer cell lines, offering an excellent opportunity to infer targets of RBPs. Taking both RBP binding sites and RNA-seq profiles of RBP knockdown samples as input, we present a pipeline to identify causal RBP RNA interactions. The pipeline employs a recent functional chi-square test (FunChisq) that deciphers directional association, and utilizes a novel functional index that measures the effect size of functional dependency. We examined $sim 45$ million RBP RNA pairs in leukemia (K562) and liver cancer (HepG2) cell lines for functional patterns as causal interaction candidates. Here, we report a total of 936,707 RBP RNA pairs in the two cell lines that show statistically significant linear or nonlinear functional patterns. About 31% of these pairs have supportive biological evidence from other sources, suggesting the effectiveness of the pipeline. The interactions constitute RBP specific regulatory networks that may potentially represent core mechanisms in the two cancers. The pipeline is implemented through an R interface with pre-computed results and data libraries for users to query specific networks and visualize RBP RNA interactions. Such networks serve as a useful resource for studying RNA dysregulation in cancer.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"190 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":"128357787","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: 3