Ioannis Tsamardinos

The max-min hill-climbing Bayesian network structure learning algorithm

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

MOTIVATION Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types. RESULTS Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets. AVAILABILITY The software system GEMS is available for download from http://www.gems-system.org for non-commercial use. CONTACT alexander.statnikov@vanderbilt.edu.

Autominder: an intelligent cognitive orthotic system for people with memory impairment

The world’s population is aging at a phenomenal rate. Certain types of cognitive decline, in particular some forms of memory impairment, occur much more frequently in the elderly. This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual’s daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project).

Time and sample efficient discovery of Markov blankets and direct causal relations

Data Mining with Bayesian Network learning has two important characteristics: under conditions learned edges between variables correspond to casual influences, and second, for every variable T in the network a special subset (Markov Blanket) identifiable by the network is the minimal variable set required to predict T. However, all known algorithms learning a complete BN do not scale up beyond a few hundred variables. On the other hand, all known sound algorithms learning a local region of the network require an exponential number of training instances to the size of the learned region.The contribution of this paper is two-fold. We introduce a novel local algorithm that returns all variables with direct edges to and from a target variable T as well as a local algorithm that returns the Markov Blanket of T. Both algorithms (i) are sound, (ii) can be run efficiently in datasets with thousands of variables, and (iii) significantly outperform in terms of approximating the true neighborhood previous state-of-the-art algorithms using only a fraction of the training size required by the existing methods. A fundamental difference between our approach and existing ones is that the required sample depends on the generating graph connectivity and not the size of the local region; this yields up to exponential savings in sample relative to previously known algorithms. The results presented here are promising not only for discovery of local causal structure, and variable selection for classification, but also for the induction of complete BNs.

A vision and strategy for the virtual physiological human in 2010 and beyond

European funding under framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for nearly 2 years. The VPH network of excellence (NoE) is helping in the development of common standards, open-source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also helping to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by framework 6 strategy for a European physiome (STEP) project in 2006. It is now time to assess the accomplishments of the last 2 years and update the STEP vision for the VPH. We consider the biomedical science, healthcare and information and communications technology challenges facing the project and we propose the VPH Institute as a means of sustaining the vision of VPH beyond the time frame of the NoE.

GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (gene expression model selector) for the automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a comprehensive evaluation of classification algorithms using 11 cancer microarray datasets. In this paper we present a preliminary evaluation of the system with five new datasets. The performance of the models produced automatically by GEMS is comparable or better than the results obtained by human analysts. Additionally, we performed a cross-dataset evaluation of the system. This involved using a dataset to build a diagnostic model and to estimate its future performance, then applying this model and evaluating its performance on a different dataset. We found that models produced by GEMS indeed perform well in independent samples and, furthermore, the cross-validation performance estimates output by the system approximate well the error obtained by the independent validation. GEMS is freely available for download for non-commercial use from http://www.gems-system.org.

Text Categorization Models for High-Quality Article Retrieval in Internal Medicine

OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average recall precision, and a sensitivity/specificity match method. RESULTS In most categories, the data-induced models have better or comparable sensitivity, specificity, and precision than the clinical query filters. The polynomial support vector machine models perform the best among all learning methods in ranking the articles as evaluated by area under the receiver operating curve and 11-point average recall precision. CONCLUSION This research shows that, using machine learning methods, it is possible to automatically build models for retrieving high-quality, content-specific articles using inclusion or citation by the ACP Journal Club as a gold standard in a given time period in internal medicine that perform better than the 1994 PubMed clinical query filters.

Efficient solution techniques for disjunctive temporal reasoning problems

Over the past few years, a new constraint-based formalism for temporal reasoning has been developed to represent and reason about Disjunctive Temporal Problems (DTPs). The class of DTPs is significantly more expressive than other problems previously studied in constraint-based temporal reasoning. In this paper we present a new algorithm for DTP solving, called Epilitis, which integrates strategies for efficient DTP solving from the previous literature, including conflictdirected backjumping, removal of subsumed variables, and semantic branching, and further adds no-good recording as a central technique. We discuss the theoretical and technical issues that arise in successfully integrating this range of strategies with one another and with no-good recording in the context of DTP solving. Using an implementation of Epilitis, we explore the effectiveness of various combinations of strategies for solving DTPs, and based on this analysis we demonstrate that Epilitis can achieve a nearly two order-of-magnitude speed-up over the previously published algorithms on benchmark problems in the DTP literature.

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning

Temporal constraints pose a challenge for conditional planning, because it is necessary for a conditional planner to determine whether a candidate plan will satisfy the specified temporal constraints. This can be difficult, because temporal assignments that satisfy the constraints associated with one conditional branch may fail to satisfy the constraints along a different branch. In this paper we address this challenge by developing the Conditional Temporal Problem (CTP) formalism, an extension of standard temporal constraint-satisfaction processing models used in non-conditional temporal planning. Specifically, we augment temporal CSP frameworks by (1) adding observation nodes, and (2) attaching labels to all nodes to indicate the situation(s) in which each will be executed. Our extended framework allows for the construction of conditional plans that are guaranteed to satisfy complex temporal constraints. Importantly, this can be achieved even while allowing for decisions about the precise timing of actions to be postponed until execution time, thereby adding flexibility and making it possible to dynamically adapt the plan in response to the observations made during execution. We also show that, even for plans without explicit quantitative temporal constraints, our approach fixes a problem in the earlier approaches to conditional planning, which resulted in their being incomplete.

A vision and strategy for the virtual physiological human: 2012 update.

European funding under Framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for 5 years. The VPH Network of Excellence (NoE) has been set up to help develop common standards, open source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also working to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by the FP6 STEP project in 2006. In 2010, we wrote an assessment of the accomplishments of the first two years of the VPH in which we considered the biomedical science, healthcare and information and communications technology challenges facing the project (Hunter et al. 2010 Phil. Trans. R. Soc. A 368, 2595–2614 (doi:10.1098/rsta.2010.0048)). We proposed that a not-for-profit professional umbrella organization, the VPH Institute, should be established as a means of sustaining the VPH vision beyond the time-frame of the NoE. Here, we update and extend this assessment and in particular address the following issues raised in response to Hunter et al.: (i) a vision for the VPH updated in the light of progress made so far, (ii) biomedical science and healthcare challenges that the VPH initiative can address while also providing innovation opportunities for the European industry, and (iii) external changes needed in regulatory policy and business models to realize the full potential that the VPH has to offer to industry, clinics and society generally.