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Cancer Evolution: Mathematical models and Computational Inference

SYSTEMATIC BIOLOGY 2015年第1期64卷 E1-E25页

作者： Beerenwinkel, Niko Schwarz, Roland F. Gerstung, Moritz Markowetz, Florian Swiss Fed Inst Technol Dept Biosyst Sci & Engn CH-4058 Basel Switzerland SIB CH-4058 Basel Switzerland European Bioinformat Inst European Mol Biol Lab Hinxton CB10 1SA Cambs England Wellcome Trust Sanger Inst Hinxton CB10 1SA Cambs England Univ Cambridge Canc Res UK Cambridge Inst Cambridge CB20RE England

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

关键词： Cancer cancer progression evolution population genetics probabilistic graphical models

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PROOF-READING GUIDANCE IN CELL TRACKING BY SAMPLING FROM TRACKING-BY-ASSIGNMENT models 12

PROOF-READING GUIDANCE IN CELL TRACKING BY SAMPLING FROM TRA...

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IEEE 12th International Symposium on Biomedical Imaging

作者： Schiegg, Martin Heuer, Ben Haubold, Carsten Wolf, Steffen Koethe, Ullrich Hamprecht, Fred A. Heidelberg Univ HCI IWR Speyerer Str 6 D-69115 Heidelberg Germany

ISBN: (纸本)9781479923748

Automated cell tracking methods are still error-prone. On very large data sets, uncertainty measures are thus needed to guide the expert to the most ambiguous events so these can be corrected with minimal effort. We present two easy-to-use methods to sample multiple proposal solutions from a tracking-by-assignment graphical model and experimentally evaluate the benefits of the uncertainty measures derived. Expert time for proof-reading is reduced greatly compared to random selection of predicted events.

关键词： Cell tracking uncertainty machine learning probabilistic graphical models

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Debiasing Crowdsourced Quantitative Characteristics in Local Businesses and Services 14

Debiasing Crowdsourced Quantitative Characteristics in Local...

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14th IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN)

作者： Ouyang, Robin Wentao Kaplan, Lance Martin, Paul Toniolo, Alice Srivastava, Mani Norman, Timothy J. Univ Calif Los Angeles Los Angeles CA 90024 USA Army Res Lab Adelphi MD USA Univ Aberdeen Aberdeen Scotland

ISBN: (纸本)9781450334754

Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.

关键词： Crowdsourcing humans as sensors truth discovery probabilistic graphical models

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Using probabilistic models for Missing Data Prediction in Network Industries Performance Measurement Systems 25

Using Probabilistic Models for Missing Data Prediction in Ne...

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25th DAAAM International Symposium on Intelligent Manufacturing and Automation

作者： Kuhi, Kristjan Kaare, Kati Korbe Koppel, Ott Tallinn Univ Technol Dept Logist & Transport Akad 15A EE-12616 Tallinn Estonia

The vast development of information and communication technologies has created new possibilities to acquire and analyze data to take performance measurement systems to next level. Most commonly performance measurement has been known as a financial management tool. Sophisticated new technologies have made it possible to collect continuous real-time data and enabled to start designing and implementing nonfinancial performance measurement systems. Most network industries are undertakings of dominant position and therefore subjects to strict supervision. For the authorities to fulfill their regulatory functions, precise monitoring and systemized feedback on the performance of network industries is essential. The problem lies in non-complete data in terms of missing, faulty or delayed values which might lead to incorrect management decisions. The objective of this paper is to explore the use of mathematical models for missing data prediction in performance measurement systems. Applying deterministic models hide the uncertainty of the value state therefore with higher likelihood false diagnoses occur. Authors propose probabilistic models because likelihood based methods for missing data calculation are able to take into account different parameters and time aspect in a single model to convey more trustworthy estimates in performance measurement systems than traditional methods. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： industrial engineering performance measurement data collection probabilistic graphical models

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Finding Interesting Outliers - A Belief Network based Approach

Finding Interesting Outliers - A Belief Network based Approa...

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IEEE Southeast Conference (IEEE SoutheastCon)

作者： Masood, Adnan Li, Wei Nova SE Univ Grad Sch Comp & Informat Sci Ft Lauderdale FL 33314 USA

ISBN: (纸本)9781467373005

Outliers are deviations from the usual trends of data;to discover interestingness among outliers i. e. finding anomalies which are of real-interest for subject matter experts is an active area of research in data mining and machine learning community. Due to its subjective nature, the definition of what amounts to 'interesting' varies between domains and subject matter experts. This paper provides an overview of the current state of quantification for measures of interestingness, using Bayesian Belief Networks as background knowledge. Building up on this foundation, we also provide a process flow for ranking outliers based on subject matter expert's apriori interestingness. Mining outliers may help discover potential anomalies and fraudulent activities. Meaningful outliers can be retrieved and analyzed by using domain knowledge. Domain knowledge (or background knowledge) is represented using probabilistic graphical models such as Bayesian belief networks. Bayesian networks are graph-based representation used to model and encode mutual relationships between entities. Due to their probabilistic graphical nature, Belief Networks are an ideal way to capture the sensitivity, causal inference, uncertainty and background knowledge in real world data sets. Bayesian Networks effectively present the causal relationships between different entities (nodes) using conditional probability. This probabilistic relationship shows the degree of belief between entities. A quantitative measure which computes changes in this degree of belief acts as a sensitivity measure. In this research paper we provide an overview of interestingness measures, their use to measure sensitivity in belief networks and review the earlier work on so-called Interestingness Filtering Engine. Building upon these foundation, we propose an iterative model to use multiple interestingness measures resulting in better performance and improved sensitivity analysis. The approach quantitatively validates probabilistic

关键词： Outlier Analysis Interestingness Measures Bayesian Belief Network probabilistic graphical models

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Discovering the Temporal Interactions between Clinical Observations 3

Discovering the Temporal Interactions between Clinical Obser...

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International Conference on Healthcare Informatics (ICHI)

作者： Goodwin, Travis Univ Texas Dallas Human Language Technol Res Inst Dallas TX 75080 USA

ISBN: (纸本)9781467395489

The explosion of clinical information provided by the advent of electronic health records (EHRs) offers an exciting opportunity to substantially improve the quality of health care. Updated throughout each patient's health care, EHRs document a wide variety of clinical observations, such as the patient's diagnoses, risk factors, medications, and test results at various points in the patient's history. This allows for the secondary use of EHRs to provide substantial information about the way that patients' clinical pictures and therapies have evolved over time. In this abstract, we outline our ongoing work towards harnessing the chronological, natural language text in EHRs in order to model how individual patients' clinical pictures and therapies have evolved. Moreover, we show our experiments on improving predictive accuracy by discovering latent groups of "similar" patients.

关键词： graphical models History Informatics Medical treatment Predictive models probabilistic logic disease progression probabilistic graphical models temporal prediction probabilistic logic graphical models Informatics Prediction models MEDICAL TREATMENT Disease Progression Exploration Medicine medical history Patient history Quality of Health Care

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Labeling abnormalities in video based complex Human-Object Interactions by robust affordance modelling

Labeling abnormalities in video based complex Human-Object I...

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International Conference on Computer Vision and Image Analysis Applications (ICCVIA)

作者： Hassan, Mahmudul Dharmaratne, Anuja Monash Univ Sch Informat Technol Subang Jaya Malaysia

ISBN: (纸本)9781479971862

Identifying abnormalities in complex Human Object Interaction (HOI) based videos and labeling their possible categories is a novel and ambitious research problem, which requires an optimal blend of the state of the art computer vision and machine learning algorithms. For classifying a HOI event normal or abnormal and subsequently classifying the potential abnormal categories requires the knowledge of the mutual relations between the Human, object and the ambient environment. Researchers have been using various contexts like spatial, temporal, sequential etc. to classify the abnormal actions. In this paper, we have introduced a novel context of object's affordance (which is a semantic map of the human, object and the ambient environment) to identify abnormalities in Human Object Interactions. Furthermore, the sub-classification of the abnormalities is also realized. In order to achieve our goal, we have introduced a set of novel attributes associated with the Human and the Objects and mapped them in a Bayesian network framework. The inference capabilities of the system depict the successful identification of abnormal events. We have also initiated a novel dataset of abnormal Human-Object Interactions in domestic settings. This research work also made a valiant effort to capitalize the abundant statistical data sources currently available, related to the domestic accidents and use them to nourish a practical classifier.

关键词： Human Object Interaction Object's affordance Dynamic time Warping (DTW) probabilistic graphical models Bayesian Network

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Spectral Probablistic Modeling and Applications to Natural Language Processing

Spectral Probablistic Modeling and Applications to Natural L...

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作者： Ankur Parikh Carnegie Mellon University

学位级别：博士

probabilistic modeling with latent variables is a powerful paradigm that has led to key advances in many applications such natural language processing, text mining, and computational biology. Unfortunately, while introducing latent variables substantially increases representation power, learning and modeling can become considerably more complicated. Most existing solutions largely ignore non-identifiability issues in modeling and formulate learning as a nonconvex optimization problem, where convergence to the optimal solution is not guaranteed due to local minima. In this thesis, we propose to tackle these problems through the lens of linear/multi-linear algebra. Viewing la- tent variable models from this perspective allows us to approach key problems such as structure learning and parameter learning using tools such as matrix/tensor decompositions, inversion, and additive metrics. These new tools enable us to develop novel solutions to learning in latent variable models with theoretical and prac- tical advantages. For example, our spectral parameter learning methods for latent trees and junction trees are provably consistent, local-optima-free, and 1-2 orders of magnitude faster than EM for large sample sizes. In addition, we focus on applications in Natural Language Processing, using our insights to not only devise new algorithms, but also to propose new models. Our method for unsupervised parsing is the first algorithm that has both theoretical guarantees and is also practical, performing favorably to the CCM method of Klein and Manning. We also developed power low rank ensembles, a framework for language modeling that generalizes existing n-gram techniques to non-integer n. It consistently outperforms state-of-the-art Kneser Ney baselines and can train on billion-word datasets in a few hours.

关键词： probabilistic graphical models spectral methods kernels unsupervised parsing language modeling

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New Algorithms for Predicting Conformational Polymorphismand Inferring Direct Couplings for Side Chains of Proteins

New Algorithms for Predicting Conformational Polymorphismand...

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作者： Soltan Ghoraie, Laleh University of Waterloo

学位级别：doctor

Protein crystals populate diverse conformationalensembles. Despite much evidence that there is widespreadconformational polymorphism in protein side chains, most of thexray crystallography data are modelled by single conformations inthe Protein Data Bank. The ability to extract or to predict theseconformational polymorphisms is of crucial importance, as itfacilitates deeper understanding of protein dynamics andfunctionality. This dissertation describes a computationalstrategy capable of predicting side-chain polymorphisms. Theapplied approach extends a particular class of algorithms forside-chain prediction by modelling the side-chain dihedral anglesmore appropriately as continuous rather than discrete *** a new inferential technique known as particle beliefpropagation (PBP), we predict residue-speci c distributions thatencode information about side-chain polymorphisms. The predictedpolymorphisms are in relatively close agreement with results froma state-of-the-art approach based on x-ray crystallography *** approach characterizes the conformational polymorphisms ofside chains using electron density information, and hassuccessfully discovered previously unmodelled ***, it is known that coupled uctuations and concertedmotions of residues can reveal pathways of communication used forinformation propagation in a molecule and hence, can help inunderstanding the \allostery" phenomenon in proteins. In order tocharacterize the coupled motions, most existing methods inferstructural dependencies among a protein''s residues. However,recent studies have highlighted the role of coupled side-chain uctuations alone in the allosteric behaviour of proteins, incontrast to a common belief that the backbone motions play themain role in allostery. These studies and the aforementionedrecent discoveries about prevalent alternate side-chainconformations (conformational polymorphism) accentuate the need todevise new computational approaches that

关键词： structural bioinformatics protein structure protein function conformational polymorphism side chains allostery long-range information propagatation GLASSO probabilistic graphical models von-Mises distribution canonical correlation analysis kernel function

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Scalability of Network-Failure Resilience: Analysis Using Multi-Layer probabilistic graphical models

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IEEE-ACM TRANSACTIONS ON NETWORKING 2009年第1期17卷 319-331页

作者： Liu, Guanglei Ji, Chuanyi Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

In this work, we quantify scalability of network resilience upon failures. We characterize resilience as the percentage of lost traffic upon failures and define scalability as the growth rate of the percentage of lost traffic with respect to network size, link failure probability, and network traffic for given failure protection schemes. We apply probabilistic graphical models to characterize statistical dependence between physical-layer failures and the network-layer traffic, and analyze the scalability for large networks of different topologies. We first focus on the scalability of resilience for regular topologies under uniform deterministic traffic with independent and dependent link failures, with and without protection. For large networks with small probabilities of failures and without protection, we show that the scalability of network resilience grows linearly with the average route length and with the "effective" link failure probability. For large networks with 1 + 1 protection, we obtain lower and upper bound of the percentage of lost traffic. We derive approximations of the scalability for arbitrary topologies, and attain close-form analytical results for ring, star, and mesh-torus topologies. We then study network resilience under random traffic with Poisson arrivals. We find that when the network is under light load, the network resilience is reduced to that under uniform deterministic traffic. When the network load is under heavy load, the percentage of lost traffic approaches the marginal probability of link failure. Our scalability analysis shows explicitly how network resilience varies with different factors and provides insights for resilient network design.

关键词： Dependent failures Erlang fixed point approximation network resilience probabilistic graphical models scalability

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