Gonzalo A. Ruz

Learning gene regulatory networks using the bees algorithm

Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees algorithm is formulated to learn networks with predefined attractors. The resulting technique is compared with simulated annealing through simulations. The ability of the networks to preserve the attractors when the updating schemes is changed from parallel to sequential is analyzed as well. Results show that Boolean networks are not very robust when the updating scheme is changed. Robust networks were found only for limit cycle length equal to two and specific network topologies. Throughout the simulations, the bees algorithm outperformed simulated annealing, showing the effectiveness of this swarm intelligence technique for this particular application.

Job performance prediction in a call center using a naive Bayes classifier

This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the agent.

Learning Gene Regulatory Networks with Predefined Attractors for Sequential Updating Schemes Using Simulated Annealing

A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks’ robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.

Unsupervised training of Bayesian networks for data clustering

This paper presents a new approach to the unsupervised training of Bayesian network classifiers. Three models have been analysed: the Chow and Liu (CL) multinets; the tree-augmented naive Bayes; and a new model called the simple Bayesian network classifier, which is more robust in its structure learning. To perform the unsupervised training of these models, the classification maximum likelihood criterion is used. The maximization of this criterion is derived for each model under the classification expectation–maximization (EM) algorithm framework. To test the proposed unsupervised training approach, 10 well-known benchmark datasets have been used to measure their clustering performance. Also, for comparison, the results for the k-means and the EM algorithm, as well as those obtained when the three Bayesian network classifiers are trained in a supervised way, are analysed. A real-world image processing application is also presented, dealing with clustering of wood board images described by 165 attributes. Results show that the proposed learning method, in general, outperforms traditional clustering algorithms and, in the wood board image application, the CL multinets obtained a 12 per cent increase, on average, in clustering accuracy when compared with the k-means method and a 7 per cent increase, on average, when compared with the EM algorithm.

Dynamical and Topological Robustness of the Mammalian Cell Cycle Network: A Reverse Engineering Approach

A common gene regulatory network model is the threshold Boolean network, used for example to model the Arabidopsis thaliana floral morphogenesis network or the fission yeast cell cycle network. In this paper, we analyze a logical model of the mammalian cell cycle network and its threshold Boolean network equivalent. Firstly, the robustness of the network was explored with respect to update perturbations, in particular, what happened to the attractors for all the deterministic updating schemes. Results on the number of different limit cycles, limit cycle lengths, basin of attraction size, for all the deterministic updating schemes were obtained through mathematical and computational tools. Secondly, we analyzed the topology robustness of the network, by reconstructing synthetic networks that contained exactly the same attractors as the original model by means of a swarm intelligence approach. Our results indicate that networks may not be very robust given the great variety of limit cycles that a network can obtain depending on the updating scheme. In addition, we identified an omnipresent network with interactions that match with the original model as well as the discovery of new interactions. The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks.

Deconstruction and Dynamical Robustness of Regulatory Networks: Application to the Yeast Cell Cycle Networks

Analyzing all the deterministic dynamics of a Boolean regulatory network is a difficult problem since it grows exponentially with the number of nodes. In this paper, we present mathematical and computational tools for analyzing the complete deterministic dynamics of Boolean regulatory networks. For this, the notion of alliance is introduced, which is a subconfiguration of states that remains fixed regardless of the values of the other nodes. Also, equivalent classes are considered, which are sets of updating schedules which have the same dynamics. Using these techniques, we analyze two yeast cell cycle models. Results show the effectiveness of the proposed tools for analyzing update robustness as well as the discovery of new information related to the attractors of the yeast cell cycle models considering all the possible deterministic dynamics, which previously have only been studied considering the parallel updating scheme.

Reconstruction and update robustness of the mammalian cell cycle network

Given the input-output data of the mammalian cell cycle network under a parallel updating scheme, an attempt to construct a threshold Boolean network with the same dynamics is presented. To accomplish this, mutual information is used to find the network structure, then a swarm intelligence optimization technique called the bees algorithm is used to find the weights and thresholds for the network. It is shown that out of the ten regulatory elements (nodes) of the network, only nine can be modeled as a single threshold function, thus, the resulting network is almost a threshold Boolean network with the exception of the CycA protein which remains with its logical rules instead. The robustness of the network is explored with respect to update perturbations, in particular, what happens to the limit cycle attractors when changing from parallel to a sequential updating scheme. Results shows that the network is not robust since different limit cycles of different lengths appear.

Building Synthetic Networks of the Budding Yeast Cell-Cycle Using Swarm Intelligence

A swarm intelligence technique called the bees algorithm is formulated to build synthetic networks of the budding yeast cell-cycle. The resulting networks contain the original fixed points of the budding yeast cell-cycle network plus additional fixed points to reduce the basin size of the fixed point associated to the G1 phase of the cell-cycle, with the purpose of promoting cell proliferation for biotechnological applications. One thousand synthetic networks were found using the bees algorithm, 84.5% had basins size for the G1 fixed point less or equal to 10, whereas the original model has a basin size for that fixed point of 1764. One of the synthetic networks was analyzed by a biologist concluding that the resulting model was quite consistent from a biological point of view, supporting the proposed method as a tool for biologist to construct synthetic networks with desired characteristics.

Building Bayesian network classifiers through a Bayesian complexity monitoring system

Abstract Nowadays, the need for practical yet efficient machine learning techniques for engineering applications are highly in demand. A new learning method for building Bayesian network classifiers is presented in this article. The proposed method augments the naive Bayesian (NB) classifier by using the Chow and Liu tree construction method, but introducing a Bayesian approach to control the accuracy and complexity of the resulting network, which yields simple structures that are not necessarily a spanning tree. Experiments by using benchmark data sets show that the number of augmenting edges by using the proposed learning method depends on the number of training data used. The classification accuracy was better, or at least equal, to the NB and the tree augmented NB models when tested on 10 benchmark data sets. The evaluation on a real industrial application showed that the simple Bayesian network classifier outperformed the C4.5 and the random forest algorithms and achieved competitive results against C5.0 and a neural network.

Reconstruction of a GRN model of salt stress response in arabidopsis using genetic algorithms

Salinity is one of the main problems in agriculture, negatively influencing the survival, biomass production, and yield of food crops. Exposure to high salinity is connected with ionic stress due to accumulation of sodium ions, osmotic stress, and reactive oxygen species production. To develop crop plants with enhanced tolerance of saline stress, a basic understanding of physiological, biochemical and gene regulatory networks (GRN) is essential. In this paper, an approach to study the saline stress response and tolerance of plants through the GRN involved in this process is proposed. In particular, we reconstruct the GRN of Ara-bidopsis thaliana saline stress response using genetic algorithms and a Boolean network model. The proposed computational intelligence approach was able to successfully infer 1000 threshold Boolean networks that contained the desired Boolean trajectory. The inferred networks were used to build a consensus network, which was useful to identify the regulations or interactions among the genes that were more plausible.