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The emergence of drug resistance in Type 1 (T1) breast cancer poses a critical challenge to effective treatment and patient outcomes. Our study introduces an innovative framework that integrates Bayesian statistical methods with machine learning (ML) to advance predictive modeling of drug resistance mechanisms in T1 breast cancer. By uniting the strengths of dynamic Bayesian networks (DBNs) and ML, this approach enables the analysis of complex, multi-dimensional clinical data, including genomic, proteomic, and treatment response datasets.

DBNs are employed to model the temporal evolution of resistance mechanisms, capturing time-dependent biological changes. ML algorithms complement this by uncovering intricate patterns and forecasting resistance trajectories under various therapeutic regimens. This synergistic combination not only identifies key biomarkers and resistance pathways but also addresses uncertainty and variability in patient responses, providing a robust predictive tool.

The resulting model offers actionable insights to clinicians, aiding in the optimization of treatment strategies and improving patient outcomes. This work highlights the transformative potential of integrating Bayesian and ML methodologies to unravel complex biological phenomena, paving the way for advancements in precision oncology and personalized medicine.

This paper further explores the uses of Bayesian Networks for detecting buildings from digital orthophotos. This work differs from current research in building detection in so far as it utilizes the ability of Bayesian Networks to provide probabilistic methods for evidence combination and, via training, to determine how such evidence should be weighted to maximize classification. In this vein, then, we have also utilized expert performance to not only configure the network values but also to adapt the feature extraction pre-processing units to fit human behavior as closely as possible. Results from digital orthophotos of the Washington DC area prove that such an approach is feasible, robust and worth further analysis.

The search for the optimal ordering of a set of variables in order to solve a computational problem is a difficulty that can appear in several circumstances. One of these situations is the automatic learning of a network structure, for example, a Bayesian Network structure (BN) starting from a dataset. Searching in the space of structures is often unmanageable, especially if the number of variables is high. Popular heuristic approaches, like Cooper and Herskovits's K2 algorithm, depend on a given ordering of variables. Estimation of Distribution Algorithms (EDAs) are a new paradigm for Evolutionary Computation that have been used as a search engine in the BN structure learning problem. In this paper, we will use two different EDAs to obtain not the best structure, but the optimal ordering of variables for the K2 algorithm: UMDA and MIMIC, both of them in discrete and continuous domains. We will also check whether the individual representation and its relation to the corresponding ordering play important roles, and whether MIMIC outperforms the results of UMDA.

Personalized recommendation needs powerful Web Intelligence (WI) technologies to manage, analyze and employ various business data on the Web for e-business intelligence. This paper presents a novel recommendation framework on the Web, which is based on a multilevel customer model comprising three submodels, namely, the customer shopping model (CSM), the customer preference model (CPM), and the customer consumption model (CCM). These models capture a customer's information from different aspects. After preprocessing of raw data, we first build the CSM based on Bayesian networks by mining from customer shopping transactions, and then find the CPM by analyzing customer shopping history. Furthermore, the customer purchasing power can be formalized as a linear CCM. By combining the CSM with the present customer shopping action, a recommendation algorithm based on Bayesian probability inference is used to generate an individual recommendation set of commodities. A personalized filter including customization of the CPM and orientation of the CCM is also used to realize a more personalized recommendation. Experimental evaluation on real world data shows that the proposed approach can achieve personalized commodities recommendation efficiently and effectively.

In this paper, we propose a new method, named Greedy Equivalence Search-Expectation Maximization (GES-EM), for learning Bayesian networks from incomplete data. Our method extends the recently proposed Greedy Equivalence Search (GES) algorithm¹⁰ to deal with incomplete data. For the quality evaluation of learned networks, we make use of the expected Bayesian Information Criterion (BIC) scoring function. In addition, we propose a new structural evaluation criterion. This so-called SEC criterion is more suitable than existing structural evaluation criteria, since it is based on the comparison of learned networks to the generating ones through Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show that GES-EM algorithm yields more accurate structures than the standard Alternating Model Selection-Expectation Maximization (AMS-EM) algorithm.¹⁵.

The high-level recognition of human activity requires a priori hierarchical domain knowledge as well as a means of reasoning based on that knowledge. Based on insights from perceptual psychology, the problem of human action recognition is approached on the understanding that activities are hierarchical, temporally constrained and at times temporally overlapped. A hierarchical Bayesian network (HBN) based on a stochastic context-free grammar (SCFG) is implemented to address the hierarchical nature of human activity recognition. Then it is shown how the HBN is applied to different substrings in a sequence of primitive action symbols via deleted interpolation (DI) to recognize temporally overlapped activities. Results from the analysis of action sequences based on video surveillance data show the validity of the approach.

In the field of handwriting recognition, classifier combination received much more interest than the study of powerful individual classifiers. This is mainly due to the enormous variability among the patterns to be classified, that typically requires the definition of complex high dimensional feature spaces: as the overall complexity increases, the risk of inconsistency in the decision of the classifier increases as well. In this framework, we propose a new combining method based on the use of a Bayesian Network. In particular, we suggest to reformulate the classifier combination problem as a pattern recognition one in which each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is then used to automatically infer the probability distribution for each class and eventually to perform the final classification. Experiments have been performed by using two different pools of classifiers, namely an ensemble of Learning Vector Quantization neural networks and an ensemble of Back Propagation neural networks, and handwritten specimen from the UCI Machine Learning Repository. The obtained performance has been compared with those exhibited by multi-classifier systems adopting the classifiers, but three of the most effective and widely used combining rules: the Majority Vote, the Weighted Majority Vote and the Borda Count.

Handwritten shorthand systems were devised to enable writers to record information on paper at fast speeds, ideally at the speed of speech. While they have been in existence for many years it is only since the 17th Century that widespread usage appeared. Several shorthand systems flourished in the first half of the 20th century until the introduction and widespread use of electronic recording and dictation machines in the 1970's. Since then, shorthand usage has been in rapid decline, but has not yet become a lost skill. Pitman shorthand has been shown to possess unique advantages as a means of fast text entry which is particularly applicable to hand-held devices in mobile environments. This paper presents progress and critical research issues for a Pitman/Renqun Shorthand Online Recognition System. Recognition and transcription experiments are reported which indicate that a correct recognition and transcription rate of around 90% is currently possible.

Learning ancestor graph is a typical NP-hard problem. We consider the problem to represent a Markov equivalence class of ancestral graphs with a compact representation. Firstly, the minimal essential graph is defined to represent the equivalent class of maximal ancestral graphs with the minimum number of invariant arrowheads. Then, an algorithm is proposed to learn the minimal essential graph of ancestral graphs based on the detection of minimal collider paths. It is the first algorithm to use necessary and sufficient conditions for Markov equivalence as a base to seek essential graphs. Finally, a set of orientation rules is presented to orient edge marks of a minimal essential graph. Theory analysis shows our algorithm is sound, and complete in the sense of recognizing all minimal collider paths in a given ancestral graph. And the experiment results show we can discover all invariant marks by these orientation rules.

We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we can obtain a correspondence between the two networks by deriving a closed-form solution so that the outputs of the two networks are similar in the least square error sense, not only when determining the discriminant function, but for the full range of their outputs. For this purpose we take into consideration the probability density functions of the independent variables of the problem when we compute the least square error approximation. Our methodoloy is demonstrated with the help of some real data concerning the problem of risk of desertification assessment for some burned forests in Attica, Greece where the parameters of the Bayesian network constructed for this task are successfully estimated given a neural network trained with a set of data.

Relevance reasoning in Bayesian networks can be used to improve efficiency of belief updating algorithms by identifying and pruning those parts of a network that are irrelevant for computation. Relevance reasoning is based on the graphical property of d-separation and other simple and efficient techniques, the computational complexity of which is usually negligible when compared to the complexity of belief updating in general.

This paper describes a belief updating technique based on relevance reasoning that is applicable in practical systems in which observations and model revisions are interleaved with belief updating. Our technique invalidates the posterior beliefs of those nodes that depend probabilistically on the new evidence or the revised part of the model and focuses the subsequent belief updating on the invalidated beliefs rather than on all beliefs. Very often observations and model updating invalidate only a small fraction of the beliefs and our scheme can then lead to sub stantial savings in computation. We report results of empirical tests for incremental belief updating when the evidence gathering is interleaved with reasoning. These tests demonstrate the practical significance of our approach.

Cell differentiation is a complex process governed by the timely activation of genes resulting in a specific phenotype or observable physical change. Recent reports have indicated heterogeneity in gene expression even amongst identical colonies (clones). While some genes are always expressed, others are expressed with a finite probability. In this report, a mathematical framework is provided to understand the mechanism of osteoblast (bone forming cell) differentiation. A systematic approach using a combination of entropy, pair-wise dependency and Bayesian approach is used to gain insight into the dependencies and underlying network structure. Pairwise dependencies are estimated using linear correlation and mutual information. An algorithm is proposed to identify statistically significant mutual information estimates. The robustness of the dependencies and the network structure to decreasing number of colonies (colony size) and perturbation is investigated. Perturbation is achieved by generating bootstrap samples. The methods discussed are generic in nature and can be extended to similar experimental paradigms.

The learning of a Bayesian network structure, especially in the case of wide domains, can be a complex, time-consuming and imprecise process. Therefore, the interest of the scientific community in learning Bayesian network structure from data is increasing: many techniques or disciplines such as data mining, text categorization, and ontology building, can take advantage from this process. In the literature, there are many structural learning algorithms but none of them provides good results for each dataset. This paper introduces a method for structural learning of Bayesian networks based on a MultiExpert approach. The proposed method combines five structural learning algorithms according to a majority vote combining rule for maximizing their effectiveness and, more generally, the results obtained by using of a single algorithm. This paper shows an experimental validation of the proposed algorithm on standard datasets.

B-Course is a free web-based online data analysis tool, which allows the users to analyze their data for multivariate probabilistic dependencies. These dependencies are represented as Bayesian network models. In addition to this, B-Course also offers facilities for inferring certain type of causal dependencies from the data. The software uses a novel "tutorial stylerdquo; user-friendly interface which intertwines the steps in the data analysis with support material that gives an informal introduction to the Bayesian approach adopted. Although the analysis methods, modeling assumptions and restrictions are totally transparent to the user, this transparency is not achieved at the expense of analysis power: with the restrictions stated in the support material, B-Course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of Bayesian and causal modeling. B-Course can be used with most web-browsers (even Lynx), and the facilities include features such as automatic missing data handling and discretization, a flexible graphical interface for probabilistic inference on the constructed Bayesian network models (for Java enabled browsers), automatic prettyHyphen;printed layout for the networks, exportation of the models, and analysis of the importance of the derived dependencies. In this paper we discuss both the theoretical design principles underlying the B-Course tool, and the pragmatic methods adopted in the implementation of the software.

We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.

Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of contextual features' values. The main challenge of this paper is the multilevel probabilistic meta-model (Bayesian Metanetwork), which is an extension of traditional BN and modification of recursive multinets. It assumes that interoperability between component networks can be modeled by another BN. Bayesian Metanetwork is a set of BN, which are put on each other in such a way that conditional or unconditional probability distributions associated with nodes of every previous probabilistic network depend on probability distributions associated with nodes of the next network. We assume parameters (probability distributions) of a BN as random variables and allow conditional dependencies between these probabilities. Several cases of two-level Bayesian Metanetworks were presented, which consist on interrelated predictive and contextual BN models.

Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Most research work in learning gene networks either assumes that there is no time delay in gene expression or that there is a constant time delay. This paper shows how Bayesian Networks can be applied to represent multi-time delay relationships as well as directed loops. The intractability of the network learning algorithm is handled by using an improved mutual information criterion. Also, a new structure learning algorithm, "Learning By Modification", is proposed to learn the sparse structure of a gene network. The experimental results on synthetic data and real data show that our method is more accurate in determining the gene structure as compared to the traditional methods. Even transcriptional loops spanning over the whole cell can be detected by our algorithm.

This paper presents and discusses the problem of emotion recognition from speech signals with the utilization of features bearing intonational information. In particular parameters extracted from Fujisaki's model of intonation are presented and evaluated. Machine learning models were build with the utilization of C4.5 decision tree inducer, instance based learner and Bayesian learning. The datasets utilized for the purpose of training machine learning models were extracted from two emotional databases of acted speech. Experimental results showed the effectiveness of Fujisaki's model attributes since they enhanced the recognition process for most of the emotion categories and learning approaches helping to the segregation of emotion categories.

As one of the most successful recommender systems, collaborative filtering (CF) algorithms are required to deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian networks (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works on applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers.^1,2 In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, the extended logistic regression on tree augmented naïve Bayes (TAN-ELR)³ CF model consistently performs better than the traditional Pearson correlation-based CF algorithm for the rating data that have few items or high missing rates. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions, while the robustness of the Pearson correlation-based CF algorithm degrades as the sparseness of the data increases.

Maximum likelihood method for estimating parameters of Bayesian networks (BNs) is efficient and accurate for large samples. However, the method suffers from overfitting when the sample size is small. Bayesian methods, which are effective to avoid overfitting, present difficulties for determining optimal hyperparameters of prior distributions with good balance between theoretical and practical points of view when no prior knowledge is available.

As described in this paper, we propose an alternative estimation method of the parameters on BNs. The method uses a principle, rooted in thermodynamics, of minimizing free energy (MFE). We define internal energies, entropies, and temperature, which constitute free energies. Especially for temperature, we propose a "data temperature" assumption and some explicit models. This approach can treat the maximum likelihood principle and the maximum entropy principle in a unified manner of the MFE principle. For assessments of classification accuracy, our method shows higher accuracy than that obtained using the Bayesian method with normally recommended hyperparameters. Moreover, our method exhibits robustness for the choice of introduced hyperparameters.