Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics最新文献_第8页

Diagnosing Schizophrenia: A Deep Learning Approach 诊断精神分裂症:一种深度学习方法

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233658

J. Barry, Srivathsan Srinivasagopalan, Sharma V. Thankachan, V. Gurupur

引用次数: 1

A Distributed Constrained Non-negative Matrix Factorization Algorithm for Time-Series Gene Expression Data 时间序列基因表达数据的分布式约束非负矩阵分解算法

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233579

Matthew S. Dyer, Julian Dymacek

{"title":"A Distributed Constrained Non-negative Matrix Factorization Algorithm for Time-Series Gene Expression Data","authors":"Matthew S. Dyer, Julian Dymacek","doi":"10.1145/3233547.3233579","DOIUrl":"https://doi.org/10.1145/3233547.3233579","url":null,"abstract":"We present a new distributed computing algorithm, Parallel Pattern Discovery (PPD), for constrained Non-negative Matrix Factorization (NMF). Our implementation offers the ability to constrain a specific pattern for optimization of the data while minimizing reconstruction error. Parallel Pattern Discovery operates within a distributed environment using a message passing interface. Distribution of the PPD algorithm provides better scalability and allows operation in single- or multiple-system environments. The algorithm was tested on a set of time-series, dose-dependent mRNA gene expression data. Parallel Pattern Discovery was found to accurately identify patterns within the data and reconstruct the original matrices. Our NMF algorithm found a smaller reconstruction error when compared against standard NMF algorithms. Development focused on running PPD as part of a system which identifies significantly contributing genes. Parallel Pattern Discovery is first run to find patterns from biological data. It is followed by Gene Set Enrichment (GSE) which takes the pattern data and relates it back to genetic pathways.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127013504","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

Feature Selection to Predict Compound's Effect on Aging 特征选择预测化合物对老化的影响

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233597

H. E. Manoochehri, Susmitha Sri Kadiyala, J. Birjandtalab, M. Nourani

引用次数: 7

Faster Computation of Genome Mappability 基因组可绘制性的快速计算

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233645

Sahar Hooshmand, Paniz Abedin, Daniel Gibney, S. Aluru, Sharma V. Thankachan

引用次数: 1

Applying Stochastic Process Model to Imputation of Censored Longitudinal Data 随机过程模型在截尾纵向数据推算中的应用

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233591

I. Zhbannikov, K. Arbeev, A. Yashin

引用次数: 0

Are Profile Hidden Markov Models Identifiable? 轮廓隐马尔可夫模型可识别吗?

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233563

Srilakshmi Pattabiraman, T. Warnow

{"title":"Are Profile Hidden Markov Models Identifiable?","authors":"Srilakshmi Pattabiraman, T. Warnow","doi":"10.1145/3233547.3233563","DOIUrl":"https://doi.org/10.1145/3233547.3233563","url":null,"abstract":"Profile Hidden Markov Models (HMMs) are graphical models that can be used to produce finite length sequences from a distribution. In fact, although they were only introduced for bioinformatics 25 years ago (by Haussler et al., Hawaii International Conference on Systems Science 1993), they are arguably the most commonly used statistical model in bioinformatics, with multiple applications, including protein structure and function prediction, classifications of novel proteins into existing protein families and superfamilies, metagenomics, and multiple sequence alignment. The standard use of profile HMMs in bioinformatics has two steps: first a profile HMM is built for a collection of molecular sequences (which may not be in a multiple sequence alignment), and then the profile HMM is used in some subsequent analysis of new molecular sequences. The construction of the profile thus is itself a statistical estimation problem, since any given set of sequences might potentially fit more than one model well. Hence a basic question about profile HMMs is whether they are em statistically identifiable, which means that no two profile HMMs can produce the same distribution on finite length sequences. Indeed, statistical identifiability is a fundamental aspect of any statistical model, and yet it is not known whether profile HMMs are statistically identifiable. In this paper, we report on preliminary results towards characterizing the statistical identifiability of profile HMMs in one of the standard forms used in bioinformatics.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129722058","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

The PepSeq Pipeline: Software for Antimicrobial Motif Discovery in Randomly-Generated Peptide Libraries PepSeq管道:在随机生成的肽库中发现抗菌基序的软件

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233599

Tanner D. Jensen, Kristi A. Bresciano, Emma Dallon, M. Fujimoto, Cole A. Lyman, Enoch Stewart, Joel S. Griffitts, M. Clement

{"title":"The PepSeq Pipeline: Software for Antimicrobial Motif Discovery in Randomly-Generated Peptide Libraries","authors":"Tanner D. Jensen, Kristi A. Bresciano, Emma Dallon, M. Fujimoto, Cole A. Lyman, Enoch Stewart, Joel S. Griffitts, M. Clement","doi":"10.1145/3233547.3233599","DOIUrl":"https://doi.org/10.1145/3233547.3233599","url":null,"abstract":"Bacteria with resistance genes are becoming ever more common, and new methods of discovering antibiotics are being developed. One of these new methods involves researchers creating random peptides and testing their antimicrobial activity. Developing antibiotics from these peptides requires understanding which sequence motifs will be toxic to bacteria. To determine if the toxic peptides of a randomly-generated peptide library can be uniquely classified based solely on sequence motifs, we created the PepSeq Pipeline: a new software that utilizes a Random Forest algorithm to extract motifs from a peptide library. We found that this pipeline can accurately classify 56% of the toxic peptides in the peptide library using motifs extracted from the model. Testing on simulated data with less noise, we could classify up to 94% of the toxic peptides. The pipeline extracted significant toxic motifs in every library that was tested, but its ability to classify all toxic peptides depended on the number of motifs in the library. Once extracted, these motifs can be used both to understand the biology behind why certain peptides are toxic and to create novel antibiotics. The code and data used in this analysis can be found at https://github.com/tjense25/pep-seq-pipeline.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129862950","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: 2

Integration of Cancer Data through Multiple Mixed Graphical Model 基于多混合图形模型的癌症数据集成

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233557

Christopher Ma, Tina Gui, Xin Dang, Yixin Chen, D. Wilkins

引用次数: 0

Cohesion-driven Online Actor-Critic Reinforcement Learning for mHealth Intervention 内聚驱动的在线行为-批评强化学习用于移动医疗干预

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233553

Feiyun Zhu, Peng Liao, Xinliang Zhu, Jiawen Yao, Junzhou Huang

{"title":"Cohesion-driven Online Actor-Critic Reinforcement Learning for mHealth Intervention","authors":"Feiyun Zhu, Peng Liao, Xinliang Zhu, Jiawen Yao, Junzhou Huang","doi":"10.1145/3233547.3233553","DOIUrl":"https://doi.org/10.1145/3233547.3233553","url":null,"abstract":"In the wake of the vast population of smart device users worldwide, mobile health (mHealth) technologies are hopeful to generate positive and wide influence on people's health. They are able to provide flexible, affordable and portable health guides to devise users. Current online decision-making methods for mHealth assume that the users are completely heterogeneous. They share no information among users and learn a separate policy for each user. However, data for each user is very limited in size to support the separate online learning, leading to unstable policies that contain lots of variances. Besides, we find the truth that a user may be similar with some, but not all, users, and connected users tend to have similar behaviors. In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth. The goal is to explore how to share information among similar users to better convert the limited user information into sharper learned policies. To the best of our knowledge, this is the first online actor-critic RL for mHealth and first network cohesion constrained (actor-critic) RL method in all applications. The network cohesion is important to derive effective policies. We come up with a novel method to learn the network by using the warm start trajectory, which directly reflects the users' property. The optimization of our model is difficult and very different from the general supervised learning due to the indirect observation of values. As a contribution, we propose two algorithms for the proposed online RLs. Apart from mHealth, the proposed methods can be easily applied or adapted to other health-related tasks. Extensive experiment results on the HeartSteps dataset demonstrates that in a variety of parameter settings, the proposed two methods obtain obvious improvements over the state-of-the-art methods.","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116709108","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: 2

Highlights Talks at ACM BCB 2018 ACM bbc2018的演讲亮点

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Pub Date : 2018-08-15 DOI: 10.1145/3233547.3233715

L. Cowen, Xiaoqian Jiang

引用次数: 0