When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks

We introduce a framework for the modeling of sequential data capturing pathways of varying lengths observed in a network. Such data are important, e.g., when studying click streams in information networks, travel patterns in transportation systems, information cascades in social networks, biological pathways or time-stamped social interactions. While it is common to apply graph analytics and network analysis to such data, recent works have shown that temporal correlations can invalidate the results of such methods. This raises a fundamental question: when is a network abstraction of sequential data justified? Addressing this open question, we propose a framework which combines Markov chains of multiple, higher orders into a multi-layer graphical model that captures temporal correlations in pathways at multiple length scales simultaneously. We develop a model selection technique to infer the optimal number of layers of such a model and show that it outperforms previously used Markov order detection techniques. An application to eight real-world data sets on pathways and temporal networks shows that it allows to infer graphical models which capture both topological and temporal characteristics of such data. Our work highlights fallacies of network abstractions and provides a principled answer to the open question when they are justified. Generalizing network representations to multi-order graphical models, it opens perspectives for new data mining and knowledge discovery algorithms.Comment: 10 pages, 4 figures, 1 table, companion python package pathpy available on gitHu

Predicting polycyclic aromatic hydrocarbon concentrations in soil and water samples

Polycyclic Aromatic Hydrocarbons (PAHs) are compounds found in the environment that can be harmful to humans. They are typically formed due to incomplete combustion and as such remain after burning coal, oil, petrol, diesel, wood, household waste and so forth. Testing laboratories routinely screen soil and water samples taken from potentially contaminated sites for PAHs using Gas Chromatography Mass Spectrometry (GC-MS). A GC-MS device produces a chromatogram which is processed by an analyst to determine the concentrations of PAH compounds of interest. In this paper we investigate the application of data mining techniques to PAH chromatograms in order to provide reliable prediction of compound concentrations. A workflow engine with an easy-to-use graphical user interface is at the heart of processing the data. This engine allows a domain expert to set up workflows that can load the data, preprocess it in parallel in various ways and convert it into data suitable for data mining toolkits. The generated output can then be evaluated using different data mining techniques, to determine the impact of preprocessing steps on the performance of the generated models and for picking the best approach. Encouraging results for predicting PAH compound concentrations, in terms of correlation coefficients and root-mean-squared error are demonstrated

pGQL: A probabilistic graphical query language for gene expression time courses

<p>Abstract</p> <p>Background</p> <p>Timeboxes are graphical user interface widgets that were proposed to specify queries on time course data. As queries can be very easily defined, an exploratory analysis of time course data is greatly facilitated. While timeboxes are effective, they have no provisions for dealing with noisy data or data with fluctuations along the time axis, which is very common in many applications. In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points. From a data mining point of view the robust handling of data through a sound statistical model is of great importance.</p> <p>Results</p> <p>We propose probabilistic timeboxes, which correspond to a specific class of Hidden Markov Models, that constitutes an established method in data mining. Since HMMs are a particular class of probabilistic graphical models we call our method Probabilistic Graphical Query Language. Its implementation was realized in the free software package pGQL. We evaluate its effectiveness in exploratory analysis on a yeast sporulation data set.</p> <p>Conclusions</p> <p>We introduce a new approach to define dynamic, statistical queries on time course data. It supports an interactive exploration of reasonably large amounts of data and enables users without expert knowledge to specify fairly complex statistical models with ease. The expressivity of our approach is by its statistical nature greater and more robust with respect to amplitude and frequency fluctuation than the prior, deterministic timeboxes.</p

Development of Java based graphical user interface for Diagnosis of Hepatitis UsingI Mixture of Expert

Hepatitis is deadly, and the fifth leading cause of death after heart disease, stroke, chest disease and cancer. Worldwide, 1.5 million deaths per year have been estimated. Detection of hepatitis is a big problem for general practitioners. An expert doctor commonly makes decisions by evaluating the current test results of a patient or by comparing the patient with others with the same condition with reference to the previous decisions. Many machine learning and data mining techniques have been designed for the automatic diagnosis of hepatitis. However, no one tool is available to the general population for the diagnosis of Hepatitis. Hence, a graphical user interface-enabled tool needs to be developed, through which medical practitioners can feed patient data easily and find hepatitis diagnoses instantly and accurately. &#xd;&#xa;Methods: In this study a hepatitis dataset was taken from the UCI machine repository database with a total of 20 attributes of two classes, Affected and Not Affected. &#xd;&#xa;Results and Conclusion: The models have been generated with a mixture of experts as a classification method for the diagnosis of hepatitis. Very good accuracy has been observed in the generated models. Finally, the model having the least minimum square error was selected. This model was then linked with GUI for the design of tools for hepatitis prediction