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Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

BMC SYSTEMS BIOLOGY 2014年第1期8卷 87页

作者： Tian, Ye Zhang, Bai Hoffman, Eric P. Clarke, Robert Zhang, Zhen Shih, Ie-Ming Xuan, Jianhua Herrington, David M. Wang, Yue Virginia Tech Dept Elect & Comp Engn Arlington VA 22203 USA Johns Hopkins Univ Dept Pathol Baltimore MD 21231 USA Childrens Natl Med Ctr Med Genet Res Ctr Washington DC 20010 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20057 USA Wake Forest Sch Med Sect Cardiovasc Med Dept Internal Med Winston Salem NC 27157 USA

Background: Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size;highly complex networks;imperfect prior knowledge;missing significance assessment;and heuristic structural parameter learning. Results: To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to "random" knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://***/***. Conclusions: Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically signif

关键词： Biological networks probabilistic graphical models Differential dependency network Network rewiring Network analysis Systems biology Knowledge incorporation Convex optimization

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Data, predictions, and decisions in support of people and society 14

Data, predictions, and decisions in support of people and so...

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Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

作者： Eric Horvitz Microsoft Research Redmond WA USA

ISBN: (纸本)9781450329569

Deep societal benefits will spring from advances in data availability and in computational procedures for mining insights and inferences from large data sets. I will describe efforts to harness data for making predictions and guiding decisions, touching on work in transportation, healthcare, online services, and interactive systems. I will start with efforts to learn and field predictive models that forecast flows of traffic in greater city regions. Moving from the ground to the air, I will discuss fusing data from aircraft to make inferences about atmospheric conditions and using these results to enhance air transport. I will then focus on experiences with building and fielding predictive models in clinical medicine. I will show how inferences about outcomes and interventions can provide insights and guide decision making. Moving beyond data captured by hospitals, I will discuss the promise of transforming anonymized behavioral data drawn from web services into large-scale sensor networks for public health, including efforts to identify adverse effects of medications and to understand illness in populations. I will conclude by describing how we can use machine learning to leverage the complementarity of human and machine intellect to solve challenging problems in science and society.

关键词： machine learning probabilistic graphical models decision analysis probabilistic inference transportation healthcare

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A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media

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JOURNAL OF COMPUTATIONAL PHYSICS 2013年 250卷 616-643页

作者： Chen, Peng Zabaras, Nicholas Cornell Univ Sibley Sch Mech & Aerosp Engn Mat Proc Design & Control Lab Ithaca NY 14853 USA Cornell Univ Ctr Appl Math Ithaca NY 14853 USA

A probabilistic graphical model approach to uncertainty quantification for flows in random porous media is introduced. Model reduction techniques are used locally in the graph to represent the random permeability. Then the conditional distribution of the multi-output responses on the low dimensional representation of the permeability field is factorized into a product of local potential functions. An expectation-maximization algorithm is used to learn the nonparametric representation of these potentials using the given input/output data. We develop a nonparametric belief propagation method for uncertainty quantification by employing the loopy belief propagation algorithm. The nonparametric nature of our model is able to capture non-Gaussian features of the response. The proposed framework can be used as a surrogate model to predict the responses for new input realizations as well as our confidence on these predictions. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed framework for solving uncertainty quantification problems in flows through porous media using stationary and non-stationary permeability fields. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Uncertainty quantification Predictive modeling probabilistic graphical models Porous media flow Nonparametric belief propagation Loopy belief propagation

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Recommender system in collaborative learning environment using an influence diagram

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第18期40卷 7193-7202页

作者： Anaya, Antonio R. Luque, Manuel Garcia-Saiz, Tomas UNED Dept Artificial Intelligence Madrid 28040 Spain

Giving useful recommendations to students to improve collaboration in a learning experience requires tracking and analyzing student team interactions, identifying the problems and the target student. Previously, we proposed an approach to track students and assess their collaboration, but it did not perform any decision analysis to choose a recommendation for the student. In this paper, we propose an influence diagram, which includes the observable variables relevant for assessing collaboration, and the variable representing whether the student collaborates or not. We have analyzed the influence diagram with two machine learning techniques: an attribute selector, indicating the most important attributes that the model uses to recommend, and a decision tree algorithm revealing four different scenarios of recommendation. These analyses provide two useful outputs: (a) an automatic recommender, which can warn of problematic circumstances, and (b) a pedagogical support system (decision tree) that provides a visual explanation of the recommendation suggested. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Recommender systems probabilistic graphical models Collaborative learning Data mining Machine learning e-Learning

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Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第3期57卷 185-195页

作者： Hernandez-Leal, Pablo Rios-Flores, Alma Avila-Rios, Santiago Reyes-Teran, Gustavo Gonzalez, Jesus A. Fiedler-Cameras, Lindsey Orihuela-Espina, Felipe Morales, Eduardo F. Sucar, L. Enrique Inst Nacl Astrofis Opt & Electr Puebla Mexico Inst Nacl Enfermedades Respiratorias Ctr Invest Enfermedades Infecciosas Mexico City DF Mexico

Objective: The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy. Methods and material: We apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance. Results: In a first experiment, we compared the TNBN approach with other models such as static Bayesian networks, dynamic Bayesian networks and association rules. TNBN achieved a 64.2% sparser structure over the static network. In a second experiment, the TNBN model was applied to a dataset associating antiretroviral drugs with mutations developed under different antiretroviral regimes. The learned models captured previously described mutational pathways and associations between antiretroviral drugs and drug resistance mutations. Predictive accuracy reached 90.5%. Conclusion: Our results suggest possible applications of TNBN for studying drug-mutation and mutation mutation networks in the context of antiretroviral therapy, with direct impact on the clinical management of patients under antiretroviral therapy. This opens new horizons for predicting HIV mutational pathways in immune selection with relevance for antiretroviral drug development and therapy p

关键词： probabilistic graphical models probabilistic learning Human immunodeficiency virus mutations

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An interactive approach for Bayesian network learning using domain/expert knowledge

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2013年第8期54卷 1168-1181页

作者： Masegosa, Andres R. Moral, Serafin Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Using domain/expert knowledge when learning Bayesian networks from data has been considered a promising idea since the very beginning of the field. However, in most of the previously proposed approaches, human experts do not play an active role in the learning process. Once their knowledge is elicited, they do not participate any more. The interactive approach for integrating domain/expert knowledge we propose in this work aims to be more efficient and effective. In contrast to previous approaches, our method performs an active interaction with the expert in order to guide the search based learning process. This method relies on identifying the edges of the graph structure which are more unreliable considering the information present in the learning data. Another contribution of our approach is the integration of domain/expert knowledge at different stages of the learning process of a Bayesian network: while learning the skeleton and when directing the edges of the directed acyclic graph structure. (C) 2013 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical models Bayesian networks Interactive structure learning Domain expert knowledge Stochastic search

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New skeleton-based approaches for Bayesian structure learning of Bayesian networks

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APPLIED SOFT COMPUTING 2013年第2期13卷 1110-1120页

作者： Masegosa, Andres R. Moral, Serafin Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Automatically learning the graph structure of a single Bayesian network (BN) which accurately represents the underlying multivariate probability distribution of a collection of random variables is a challenging task. But obtaining a Bayesian solution to this problem based on computing the posterior probability of the presence of any edge or any directed path between two variables or any other structural feature is a much more involved problem, since it requires averaging over all the possible graph structures. For the former problem, recent advances have shown that search + score approaches find much more accurate structures if the search is constrained by a previously inferred skeleton (i.e. a relaxed structure with undirected edges which can be inferred using local search based methods). Based on similar ideas, we propose two novel skeleton-based approaches to approximate a Bayesian solution to the BN learning problem: a new stochastic search which tries to find directed acyclic graph (DAG) structures with a non-negligible score;and a new Markov chain Monte Carlo method over the DAG space. These two approaches are based on the same idea. In a first step, both employ a previously given skeleton and build a Bayesian solution constrained by this skeleton. In a second step, using the preliminary solution, they try to obtain a new Bayesian approximation but this time in an unconstrained graph space, which is the final outcome of the methods. As shown in the experimental evaluation, this new approach strongly boosts the performance of these two standard techniques proving that the idea of employing a skeleton to constrain the model space is also a successful strategy for performing Bayesian structure learning of BNs. (C) 2012 Elsevier B.V. All rights reserved.

关键词： probabilistic graphical models Bayesian networks Bayesian structure learning Markov chain Monte Carlo Stochastic search

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A Bayesian network to evaluate underground rails maintenance strategies in an automation context

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2013年第4期227卷 411-424页

作者： Bouillaut, Laurent Francois, Olivier Dubois, Stephane Univ Paris Est Marne La Vallee France RATP Regie Autonome Transports Parisiens Engn Dep Fontenay Sous Bois France

Reliability analysis has become an integral part of system design and operation. This is especially true for systems performing critical tasks, such as mass transportation systems. This explains the numerous advances in the field of reliability modeling. More recently, some studies involving the use of Bayesian networks have been proven relevant to represent complex systems and perform reliability studies. In previous works, a generic methodology was introduced for developing a decision support tool to evaluate complex systems maintenance strategies. This article deals with development of such a decision tool dedicated to the maintenance of Paris metro rails. Indeed, owing to fulfillment of high-performance levels of safety and availability (the latter being especially critical at peak hours), operators need to estimate, hour by hour their ability to prevent or to detect broken rails. To address this problem, a decision support tool was developed, the aim of this article is to evaluate, compare and optimize various operating and maintenance strategies.

关键词： Maintenance railway infrastructure steel wheel metro's automation availability optimization decision support probabilistic graphical models

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Exploiting Sparsity in Hyperspectral Image Classification via graphical models

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第3期10卷 505-509页

作者： Srinivas, Umamahesh Chen, Yi Monga, Vishal Nasrabadi, Nasser M. Tran, Trac D. Penn State Univ Dept Elect Engn University Pk PA 16802 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA USA Res Lab Adelphi MD 20783 USA

A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.

关键词： Classification hyperspectral imagery joint sparsity model probabilistic graphical models sparse representation spatial correlation

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Inference of Natural Selection from Interspersed Genomic Elements Based on Polymorphism and Divergence

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MOLECULAR BIOLOGY AND EVOLUTION 2013年第5期30卷 1159-1171页

作者： Gronau, Ilan Arbiza, Leonardo Mohammed, Jaaved Siepel, Adam Cornell Univ Dept Biol Stat & Computat Biol Ithaca NY 14853 USA Triinst Training Program Computat Biol & Med New York NY USA

Complete genome sequences contain valuable information about natural selection, but this information is difficult to access for short, widely scattered noncoding elements such as transcription factor binding sites or small noncoding RNAs. Here, we introduce a new computational method, called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), for measuring the influence of natural selection on such elements. INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and coalescent times. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. It obtains information about divergence from multiple outgroup genomes using a general statistical phylogenetic approach. The INSIGHT model is efficiently fitted to genome-wide data using an approximate expectation maximization algorithm. Using simulations, we show that the method can accurately estimate the parameters of interest even in complex demographic scenarios, and that it significantly improves on methods based on summary statistics describing polymorphism and divergence. To demonstrate the usefulness of INSIGHT, we apply it to several classes of human noncoding RNAs and to GATA2-binding sites in the human genome.

关键词： molecular evolution population genetics noncoding DNA regulatory sequences probabilistic graphical models

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