2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第6页

RF-Phos: Random forest-based prediction of phosphorylation sites RF-Phos:基于随机森林的磷酸化位点预测

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359670

Ahoi Jones, Hamid D. Ismail, J. H. Kim, R. Newman, B. K.C.Dukka

引用次数: 3

Integrating multiple sources of genomic data by multiplex network reconstruction 多路网络重构整合多源基因组数据

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359926

Shang Gao, W. Zou, Yuanyuan Liu, Xingwang Wang, Y. Zhuang, X. Wei, R. Alhajj

引用次数: 1

SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions SRP:用于预测药物-靶标相互作用的简洁非参数相似性排序模型

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359921

Jianyu Shi, S. Yiu

{"title":"SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions","authors":"Jianyu Shi, S. Yiu","doi":"10.1109/BIBM.2015.7359921","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359921","url":null,"abstract":"The identification of drug-target interactions in web lab is costly and time-consuming. Computational approaches become important to help identifying potential candidates for laboratory experiments. However, they usually involve solving optimization problems or assuming statistical distribution based on prior knowledge, and may require estimating tunable parameters. This paper is motivated by the concepts behind “follow-on” drugs. They are the drugs developed by drug companies to substitute the pioneering drug which was firstly discovered and patented for a specific target and determined a new therapeutic class. There are three observations from “follow-on” drugs. The first observation has been used by many existing methods: drugs interacting with a common target usually have higher similar scores (e.g. the similarity score in terms of chemical structure). The second one is that a drug candidate for a specific target gains more attention if it is more similar to those drugs interacting with the target than other known drugs, even though the similarity score is low. Lastly, people intuitively tend to design a “follow-on” drug for the targets already having more drugs because of less cost and less risk. In our approach, the above observations are translated into more evidences for predicted drug-target interaction. Designing an interaction tendency index to characterize these observations, we propose the similarity-rank-based predictor (SRP). Unlike other models, SRP is a non-parametric model and requires neither solving an optimization problem nor prior statistical knowledge. Based on real benchmark datasets, we show that our model is able to achieve higher accuracy than the two most recent models and our approach is able to cope with two real predicting scenario of missing interactions.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"17 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":"121137291","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: 14

Hybrid multi-threaded simulation of agent-based pandemic modeling using multiple GPUs 使用多个gpu的基于代理的流行病建模的混合多线程模拟

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359894

Barzan Shekh, E. Doncker, D. Prieto

{"title":"Hybrid multi-threaded simulation of agent-based pandemic modeling using multiple GPUs","authors":"Barzan Shekh, E. Doncker, D. Prieto","doi":"10.1109/BIBM.2015.7359894","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359894","url":null,"abstract":"Epidemiology computation models are crucial for the assessment and control of public health crises. Agent-based simulations of pandemic influenza forecast the infectious disease spreading in order to help public health policy makers during emergencies. In such emergencies, decisions are required for public health preparedness in cycles of less than a day, and the agent-based model should be adaptable and tractable for quick and simple calibration with low computational overhead. GPU accelerated computing involves the use of graphics processing units (GPUs) in combination with the CPU to perform heterogeneous computing by offloading compute-intensive portions of the program to the GPU while the remaining program runs on the CPU. In this paper, we demonstrate the utilization of the hardware environment and software tools and discuss strategies for porting agent-based simulations to multiple GPUs. We further compare the performance of simulations using two or four GPUs with the sequential execution on the CPU, in terms of time and speedup. The multi-GPU implementations exhibit great performance and support populations with up to 100 million individuals.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 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":"116857049","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

Identifying inorganic material affinity classes for peptide sequences based on context learning 基于上下文学习的肽序列无机物亲和类识别

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359742

Guangxu Xun, Xiaoyi Li, Marc R. Knecht, P. Prasad, M. Swihart, T. Walsh, A. Zhang

{"title":"Identifying inorganic material affinity classes for peptide sequences based on context learning","authors":"Guangxu Xun, Xiaoyi Li, Marc R. Knecht, P. Prasad, M. Swihart, T. Walsh, A. Zhang","doi":"10.1109/BIBM.2015.7359742","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359742","url":null,"abstract":"There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"51 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":"115101456","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: 4

Deep neural network based protein-protein interaction extraction from biomedical literature 基于深度神经网络的生物医学文献蛋白质相互作用提取

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359845

Zhehuan Zhao, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang, Song Gao

引用次数: 2

Competitive pathway analysis using Structural Equation Models (CPA-SEM) for gene expression data 利用结构方程模型(CPA-SEM)对基因表达数据进行竞争通路分析

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359875

Sungkyoung Choi, Sungyoung Lee, Iksoo Huh, Heungsun Hwang, T. Park

{"title":"Competitive pathway analysis using Structural Equation Models (CPA-SEM) for gene expression data","authors":"Sungkyoung Choi, Sungyoung Lee, Iksoo Huh, Heungsun Hwang, T. Park","doi":"10.1109/BIBM.2015.7359875","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359875","url":null,"abstract":"There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.","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":"121215422","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

A domain ontology for the Non-Coding RNA field 非编码RNA领域的领域本体

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359755

Jingshan Huang, K. Eilbeck, J. Blake, D. Dou, D. Natale, A. Ruttenberg, Barry Smith, Michael T. Zimmermann, Guoqian Jiang, Yu Lin, Bin Wu, Y. He, Shaojie Zhang, Xiaowei Wang, He Zhang, Zixing Liu, M. Tan

引用次数: 2

A seeding-searching-ensemble method for gland segmentation and detection 一种用于腺体分割和检测的种子搜索集成方法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359707

Yizhe Zhang, L. Yang, J. MacKenzie, R. Ramachandran, D. Chen

引用次数: 0

Parallel group ICA for multimodal biomedical data analyses 用于多模态生物医学数据分析的平行组ICA

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359832

Jingyu Liu, Jiayu Chen, V. Calhoun

引用次数: 0