Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics – clustering gene expression data – to the operations research community.     

Multirobot coordination in pick-and-place tasks on a moving conveyor

This paper considers the problem of multirobot coordination in pick-and-place tasks on a conveyor band. The robot team is composed of identical robots with mutually exclusive, but neighboring workspaces. The products are fed in at one end of the band, move through each workspace sequentially until being picked up and are collected at the other end—if not picked up interim. Each robot has the same task—that is picking up and packaging as many products as possible. We propose an approach based on noncooperative game theory where each robot uses local observations of the conveyor band and their neighbors' actions in order to decide on its actions. The developed algorithm has been implemented and tested in a simulated manufacturing environment using Webots. Results obtained from the simulations are analyzed using a variety of statistical performance measures.     

Eigenclassifiers for combining correlated classifiers

In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ula?, M. Semerci, O.T. Y?ld?z, E. Alpayd?n, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298–1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.     

Cost-conscious comparison of supervised learning algorithms over multiple data sets

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi²Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from “best” to “worst” where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi²Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.     

Design of a framework for modeling, integration and simulation of physiological models

Multiscale modeling and integration of physiological models carry challenges due to the complex nature of physiological processes. High coupling within and among scales present a significant challenge in constructing and integrating multiscale physiological models. In order to deal with such challenges in a systematic way, there is a significant need for an information technology framework together with related analytical and computational tools that will facilitate integration of models and simulations of complex biological systems. Physiological Model Simulation, Integration and Modeling Framework (Phy-SIM) is an information technology framework providing the tools to facilitate development, integration and simulation of integrated models of human physiology. Phy-SIM brings software level solutions to the challenges raised by the complex nature of physiological systems. The aim of Phy-SIM, and this paper is to lay some foundation with the new approaches such as information flow and modular representation of the physiological models. The ultimate goal is to enhance the development of both the models and the integration approaches of multiscale physiological processes and thus this paper focuses on the design approaches that would achieve such a goal.     

Implementation of genetic algorithm in an embedded microcontroller-based polarization control system

Implementation of genetic algorithm in a PIC32MX microcontroller-based polarization control system is proposed and demonstrated. The controller measures the signal intensity that is used to estimate the genetic value. This process is controlled by the genetic algorithm rather than referring to the Look-Up-Table as implemented in existing solutions. To reach optimum performance, the code is optimized by using the best genetic parameters so that the fastest execution time can be achieved. An ability of genetic algorithm to work efficiently in polarization control system possesses many advantages including easy code construction, low memory consumption and fast control speed. Genetic algorithm is intelligent enough to be used for endless polarization stabilization and in the worst case, able to stabilize the polarization changes in 300 μs. In the best case the response time can reach 17 μs.     

Iterative performance improvement of fuzzy control systems for three tank systems

This paper suggests the performance improvement of fuzzy control systems (FCSs) for three tank systems using iterative feedback tuning (IFT). The stable design of Takagi–Sugeno–Kang fuzzy controllers is guaranteed by means of a stability theorem based on LaSalle’s global invariant set theorem formulated for a class of multi input-multi output (MIMO) nonlinear processes. An IFT algorithm characterized by setting the step size to guarantee the FCS stability is proposed. The theoretical approaches are applied in a case study that deals with the IFT-based stable design of fuzzy controllers dedicated to the level control of a cylindrical three tank system as a representative MIMO system. A set of experimental results for a laboratory setup illustrates the performance improvement.     

The ratio of crystallinity and thermodynamical interactions of polycaprolactone with some aliphatic esters and aromatic solvents by inverse gas chromatography

Summary The retention diagrams of benzene, toluene, ethyl benzene, chloro benzene, n-propyl benzene, isopropyl benzene, methyl acetate, ethyl acetate, n-propyl acetate, n-butyl acetate and isoamyl acetate on the polycaprolactone were plotted at temperatures between 70 and 140^oC by inverse gas chromatography technique. Percent crystallinity of polycaprolactone were obtained at temperatures below melting point from the retention diagrams of benzene, toluene and ethyl benzene. It was concluded that the data obtained by inverse gas chromatography were comparable those of obtained by differential scanning calorimetry. Specific retention volume, V_g^o, Flory-Huggins polymer-solvent interaction parameters, ₁₂, the weight fraction activity coefficients of the solvents at infinite dilution, ₁, effective exchange energy parameters, X_eff were determined. Later, the partial molar heat of sorption, H_1,sorp and the partial molar heat of mixing, H₁ were obtained from the slope of the logarithm of specific retention volume, Ln V_g^o versus 1/T plot and from the slope of the logarithm of the weight fraction activity coefficients, ₁versus 1/T plot, respectively.