15270290
Modeling Transcriptional Regulation in Chondrogenesis Using Particle Swarm Optimization
Chondrogenesis is a complex developmental process involving many transcription factors. Using mRNA expression data and regulatory DNA sequences, we formulated a quantitative model to predict a set of transcription-factor binding motifs (TFBMs) as a combinatorial problem. To solve such a problem, an efficient computational algorithm should be employed. In the current study, particle swarm optimization was applied. Swarm intelligence is an artificial intelligence approach that mimics a behavior of swarm-forming agents. Such systems are made up with a population of individuals that interact locally and globally. Here, a group of TFBMs was predicted using 200 artificial bees and the results were compared to biologically known binding motifs.
17725399
Regulating gene expression using optimal control theory
We described development of a novel genome-based model-driven strategy useful for regulating eukaryotic gene expression. In order to extract biologically meaningful information from a large volume of mRNA expression data, we built previously a PROmoter-Based Estimation (PROBE) model. The PROBE model allowed us to establish a quantitative relationship between transcription-factor binding motifs in regulatory DNA sequences and mRNA expression levels. Here, we extended PROBE formulation to derive an optimal control law for gene regulation. The responses to shear stress in human synovial cells were chosen as a model biological system, and the system dynamics was identified from the expression pattern of the genes involved in degradation and maintenance of extracellular matrix. In order to suppress the responses to mechanical stimuli, a Ricatti equation was solved and an admissible control law was derived. The approach presented here can be implemented in any biological process, and it would be useful to develop a transcription-mediated strategy for gene therapies and tissue engineering.
15516376
Supervised rank aggregation
This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by means of unsupervised learning. To further enhance ranking accuracies, we propose employing supervised learning to perform the task, using labeled data. We refer to the approach as Supervised Rank Aggregation. We set up a general framework for conducting Supervised Rank Aggregation, in which learning is formalized an optimization which minimizes disagreements between ranking results and the labeled data. As case study, we focus on Markov Chain based rank aggregation in this paper. The optimization for Markov Chain based methods is not a convex optimization problem, however, and thus is hard to solve. We prove that we can transform the optimization problem into that of Semidefinite Programming and solve it efficiently. Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods.
18179447
Towards Designing Neural Network Ensembles by Evolution
This paper proposes a co-evolutionary learning system, i.e., CELS, to design neural network (NN) ensembles. CELS addresses the issue of automatic determination of the number of individual NNs in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of CELS is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. Experiments on two real-world problems demonstrate that CELS can produce NN ensembles with good generalisation ability.
8291180
Grounding for Model Expansion in k-Guarded Formulas with Inductive Definitions
Mitchell and Ternovska [2005] proposed a constraint programming framework based on classical logic extended with inductive definitions. They formulate a search problem as the problem of model expansion (MX), which is the problem of expanding a given structure with new relations so that it satisfies a given formula. Their long-term goal is to produce practical tools to solve combinatorial search problems, especially those in NP. In this framework, a problem is encoded in a logic, an instance of the problem is represented by a finite structure, and a solver generates solutions to the problem. This approach relies on propositionalisation of high-level specifications, and on the efficiency of modern SAT solvers. Here, we propose an efficient algorithm which combines grounding with partial evaluation. Since the MX framework is based on classical logic, we are able to take advantage of known results for the so-called guarded fragments. In the case of k-guarded formulas with inductive definitions under a natural restriction, the algorithm performs much better than naive grounding by relying on connections between k-guarded formulas and tree decompositions.
20301430
A Three-Step Approach to Detect and Remove Grid Lines in Well-Logging Digitization
5302961
A Group Theoretic Formalization of Surface Contact
The surface contacts between solids are always associated with a set of symmetries of the contacting surfaces. These symmetries form a group known as the symmetry group of the surface. In this article we develop a group theoretic formalization for describing surface contact between solids. In particular we define (1) primitive and compound features of a solid, (2) a topological characterization of these features, and (3) the symmetry groups of primitive and compound features. The symmetry group of a feature is a descriptor of the fea ture that is at once abstract and quantitative. We show how to use group theory concepts to describe the exact relative motion (position) of solids under surface contacts, which can be either rigid or articulated. The central result of this article is to prove the following: 1. When primitive features of a solid are mutually dis tinct, 1-congruent or 2-congruent, the symmetry group of a compound feature can be expressed in terms of the intersection of the symmetry groups of its primitive features. 2. When two solids have surface contact, their rela tive positions can be expressed as a coset of their common symmetry group, which in turn can be ex pressed in terms of the intersections of the symmetry groups of the primitive features involved in this con tact. These results show that using group theory to formalize surface contacts is a general approach for specifying spatial relationships and forms a sound basis for the automation of robotic task planning. One advantage of this formulation is its ability to express continuous motions between two surface- contacting solids in a computational manner and to avoid combinatorics arising from multiple relationships, especially from discrete symmetries in the assembly parts and their fea tures. At the end of this article, a geometric representation for symmetry groups and an efficient group intersection algorithm using characteristic invariants are described.