9772572
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
OBJECTIVE
Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying visual field (VF) data that explicitly models these spatial and temporal relationships.


METHODOLOGY
We carry out an analysis of our proposed spatio-temporal Bayesian classifier and compare it to a number of classifiers from the machine learning and statistical communities. These are all tested on two datasets of VF and clinical data. We investigate the receiver operating characteristics curves, the resulting network structures and also make use of existing anatomical knowledge of the eye in order to validate the discovered models.


RESULTS
Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the 'nasal step', an early indicator of the onset of glaucoma.


CONCLUSION
The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data.
36513177
A Bayesian network approach to explaining time series with changing structure
Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.
39184835
Improved Robustness in Time Series Analysis of Gene Expression Data by Polynomial Model Based Clustering
Microarray experiments produce large data sets that often contain noise and considerable missing data. Typical clustering methods such as hierarchical clustering or partitional algorithms can often be adversely affected by such data. This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using these models to cluster the data. Similarity measures for polynomials are given that comply with commonly used standard measures. The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene expression data set. It shows significantly better results as noise and missing data are increased.
40113853
A Framework for Modelling Short, High-Dimensional Multivariate Time Series: Preliminary Results in Virus Gene Expression Data Analysis
Short, high-dimensional Multivariate Time Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would be beneficial. Nowhere is this more true than functional genomics where effective ways of analysing gene expression data are urgently needed. Progress in this area could help obtain a "global" view of biological processes, and ultimately lead to a great improvement in the quality of human life. We present a computational framework for modelling this type of data, and report preliminary experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a threestep modelling strategy: correlation search, variable grouping, and short MTS modelling. Novel research is involved in each step which has been individually tested on different real-world datasets in engineering and medicine. This is the first attempt to integrate all these components into a coherent computational framework, and test the framework on a very challenging application area, which has produced promising results.
16058917
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.
36065326
Learning short multivariate time series models through evolutionary and sparse matrix computation
Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities. One area that has been largely overlooked so far is the particular type of time series where the data set consists of a large number of variables but with a small number of observations. In this paper we describe the development of a novel computational method based on Natural Computation and sparse matrices that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the associated parameters. Extensive results are presented, where the proposed method is compared with both traditional statistical and heuristic search techniques and evaluated on a number of criteria. The results have implications for a wide range of applications involving the learning of short MTS models.
17314700
Propagating uncertainty in microarray data analysis
Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.
8133100
Geometry-assisted statistical modeling for face mosaicing
The modeling of facial appearance has many applications. This paper proposes an approach to generating a statistical face model based on video mosaicing. Unlike traditional video mosaicing, we use the geometry of a face to improve the mosaicing result. Given a face sequence, each frame is unwrapped onto certain portion of the surface of a sphere, as determined by spherical projection and the minimization procedure using the Levenberg-Marquardt algorithm. A statistical model containing a mean image and a number of eigenimages, instead of only one image template, is used to represent the face mosaic. Good experimental results have been observed.
45375926
A Markov chain sequence generator for power macromodeling
In this paper, we present a novel sequence generator based on a Markov chain (MC) model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O(nl+n/sup 2/) time. Derived from a strongly mixing MC, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2000 48-bit vectors are generated in less than 0.05 s, with average deviations of the signal statistics p,d, and s equal to 1.6%, 1.8%, and 2.8%, respectively. Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS'85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p,d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.
460263
Heuristic algorithms for multi-constrained quality of service routing
Multi-constrained quality of service (QoS) routing finds a route in the network that satisfies multiple independent quality of service constraints. This problem is NP-hard and a number of heuristic algorithms have been proposed to solve the problem. This paper studies two heuristics, the limited granularity heuristic and the limited path heuristic, for solving general k-constrained problems. Analytical and simulation studies are conducted to compare the time/space requirements of the heuristics and the effectiveness of the heuristics in finding the paths that satisfy the QoS constraints. We prove analytically that for an N nodes and E edges network with k (a small constant) independent QoS constraints, the limited granularity heuristic must maintain a table of size O(|N|/sup k-1/) in each node to be effective, which results in a time complexity of O(|N|/sup k/|E|). We also prove that the limited path heuristic can achieve very high performance by maintaining O(|N|/sup 2/lg(|N|)) entries in each node, which indicates that the performance of the limited path heuristic is not sensitive to the number of constraints. We conclude that although both the limited granularity heuristic and the limited path heuristic can efficiently solve 2-constrained QoS routing problems, the limited path heuristic is superior to the limited granularity heuristic in solving k-constrained QoS routing problems when k>3. Our simulation study further confirms this conclusion.
10851913
Taming heterogeneity - the Ptolemy approach
Modern embedded computing systems tend to be heterogeneous in the sense of being composed of subsystems with very different characteristics, which communicate and interact in a variety of ways-synchronous or asynchronous, buffered or unbuffered, etc. Obviously, when designing such systems, a modeling language needs to reflect this heterogeneity. Today's modeling environments usually offer a variant of what we call amorphous heterogeneity to address this problem. This paper argues that modeling systems in this manner leads to unexpected and hard-to-analyze interactions between the communication mechanisms and proposes a more structured approach to heterogeneity, called hierarchical heterogeneity, to solve this problem. It proposes a model structure and semantic framework that support this form of heterogeneity, and discusses the issues arising from heterogeneous component interaction and the desire for component reuse. It introduces the notion of domain polymorphism as a way to address these issues.