Ikuko Nishikawa

Computational Prediction of O-linked Glycosylation Sites that Preferentially Map on Intrinsically Disordered Regions of Extracellular Proteins

O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability.

Line balancing using a genetic evolution model

Abstract Line balancing that allots operations equally to workstations along a conveyer line in manufacturing factories is important for realizing high-efficiency production. It is a kind of NP-hard problem because there are many combinations of operations. Recently, genetic algorithms have been given attention as a strong tool to solve NP-hard problems like job-shop scheduling problems. In this paper, a method to apply genetic algorithms to line-balancing problems is proposed. Computer simulations verify that the proposed method can obtain quasi-optimum solutions in a few minutes by using a workstation computer for NP-hard line-balancing problems which are hard to solve using ordinary methods.

Detection and recognition of road signs using simple layered neural networks

A road sign detection method is proposed, using 2 simple 3-layered neural networks. Multiple preprocessing steps are taken for masking the irrelevant areas and selecting the candidate areas from an original input color images, and both neural networks are modules for this selection process. One network is for matching a color of an input pixel with a road sign, and the other is for matching a shape of an input object. The final recognition is given by a template matching, and an auxiliary re-detection step is added to improve the efficiency. The experiments using a large number of pictured images under several different conditions show the high detection rates over 95% in most cases, while the computational cost is low owing to the smallness and the simplicity of the neural networks.

Prediction of the O -glycosylation sites in protein by layered neural networks and support vector machines

O-glycosylation is one of the main types of the mammalian protein glycosylation, which is serine or threonine specific, though any consensus sequence is still unknown. In this paper, a layered neural network and a support vector machine are used for the prediction of O-glycosylation sites. Three types of encoding for a protein sequence within a fixed size window are used as the input to the network, that is, a sparse coding which distinguishes all 20 amino acid residues, 5-letter coding and hydropathy coding. In the neural network, one output unit gives the prediction whether a particular site of serine or threonine is glycosylated, while SVM classifies into the 2 classes. The performance is evaluated by the Matthews correlation coefficient. The preliminary results on the neural network show the better performance of the sparse and 5-letter codings compared with the hydropathy coding, while the improvement according to the window size is shown to be limited to a certain extent by SVM.

Improvements of the Traffic Signal Control by Complex-Valued Hopfield Networks

The phase synchronization in the complex-valued Hopfield network has been shown to be effective for a signal control in an area-wide urban traffic flow control. The basic idea of the original method is to attain the global effectiveness as a weighted summation of the local effectiveness. And the complex-valued Hopfield network is designed to converge to such an optimal state through the appropriate interaction between the neurons which model the traffic signals. As the result, the network possesses the energy function which expresses the global effectiveness, whose leading term is given by the summation of the substantial traffic flows under the given offset. Thus, it is a bottom-up approach to optimize the global effectiveness as the total of the local effectiveness. In this paper, two different approaches are introduced, and added to or compared with the above approach. The first approach is the feedback from the real time information of local traffics. The purpose of the feedback is to decrease the differences of the disadvantages among conflicting flows, which are measured by a congestion or the number of waiting vehicles. The addition of the feedback to the original method shows that the local feedback works as a pinpoint control on a local congestion, while keeping the total effectiveness especially in regular traffic patterns. The second is a top-down approach to attain the global optimization by real-coded genetic algorithms. The proposed GA directly searches the effective offset using a traffic simulator to calculate the average traveling time for the evaluation. Therefore, genetic operations are designed for a small size population and a real-code in a torus space. The best offsets obtained by GA reduce the average traveling time by 2%~7% compared with the results obtained by the original approach.

The constraints satisfaction problem approach in the design of an architectural functional layout

A design support system with a new strategy for finding the optimal functional configurations of rooms for architectural layouts is presented. A set of configurations satisfying given constraints is generated and ranked according to multiple objectives. The method can be applied to problems in architectural practice, urban or graphic design—wherever allocation of related geometrical elements of known shape is optimized. Although the methodology is shown using simplified examples—a single story residential building with two apartments each having two rooms—the results resemble realistic functional layouts. One example of a practical size problem of a layout of three apartments with a total of 20 rooms is demonstrated, where the generated solution can be used as a base for a realistic architectural blueprint. The discretization of design space is discussed, followed by application of a backtrack search algorithm used for generating a set of potentially ‘good’ room configurations. Next the solutions are classified by a machine learning method (FFN) as ‘proper’ or ‘improper’ according to the internal communication criteria. Examples of interactive ranking of the ‘proper’ configurations according to multiple criteria and choosing ‘the best’ ones are presented. The proposed framework is general and universal—the criteria, parameters and weights can be individually defined by a user and the search algorithm can be adjusted to a specific problem.

Measurement of binocular stereoacuity for design of head mounted display with wide view

To design a new type of head mounted display (HMD) with a wide view, the depth perception at the periphery of the retina is important. In this paper, we measure the binocular stereoacuity for fovea and periphery of the retina. From the measurement data, the characteristics of binocular steroacuity is investigated. Basing on the results, we propose a method for design of a HMD with a wide view in, order to set proper disparity for fovea, and periphery of retina.

Independent component analysis-based prediction of O-Linked glycosylation sites in protein using multi-layered neural networks

In this paper, we develop a new method for prediction 0-linked glycosylation site and pattern analysis in protein, which combines independent component analysis (ICA) with a multi-layer neural network (NN). ICA is first used to construct main basis (subspace) of the protein sequence for features extraction. The projections of protein sequence on the subspace with low dimension are used as input data instead of the higher-dimensional protein sequences. Neural network is built to predict whether a particular site of serine or threonine is glycosylated. Compared with other subspace method, our proposed new method can improve the prediction accuracy.

Effective Integration of Imitation Learning and Reinforcement Learning by Generating Internal Reward

This paper describes an integrative machine learning architecture of imitation learning and reinforcement learning. The learning architecture aims to help integration of the two learning process by generating internal rewards. After observing superiors, human learners usually start practicing through trial and error. Humans usually learn tasks through both imitation learning and reinforcement learning. Imitation learning and reinforcement learning should be harmonized as an effective and integrative learning system. A simple reinforcement learning requires a huge amount of trials and errors in an agents learning phase. However, imitation learning can reduce the amount of time. Based on this idea, the composition of reinforcement learning and imitation learning is proposed as an integrative machine learning architecture. In this paper, an additional internal reward system, which is generated by the learner agent, is introduced to achieve this goal. The learning architecture is evaluated through an experiment and the effectiveness of the integration is examined.

PHASE DYNAMICS OF COMPLEX-VALUED NEURAL NETWORKS AND ITS APPLICATION TO TRAFFIC SIGNAL CONTROL

Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.