Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of seq...

The midbrain dopamine neurons are hypothesized to provide a physiological correlate of the reward prediction error signal required by current models of reinforcement learning. We examined the activity of single dopamine neurons during a task in which subjects learned by trial and error when to make an eye movement for a juice reward. We found that these neurons encoded the difference between th...

This paper presents a prediction error variance reduction procedure based on fractional digital differentiation with negative order. This reduction is achieved by increasing correlation in the signals. Applications to ECG signals show that savings of more than one bit per residual signal sample can be attained. Keywords—Linear prediction, fractional differentiation, ECG signal.

The paper is devoted to time series prediction using linear, perceptron and Elman neural networks of the proposed pattern structure. Signal wavelet de-noising in the initial stage is discussed as well. The main part of the paper is devoted to the comparison of different models of time series prediction. The proposed algorithm is applied to the real signal representing gas consumption.

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressivemoving average ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the over...

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this pa...

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. ...

Estimating signals from time series is a common task in many domains of science and has been addressed for a long time by specialists. Predicting a signal from recorded time series remains however a very specific task, a great challenge. The wavelet transform provides multi-resolution analysis and allows accurate time-frequency localization of different signal properties. This paper presents a ...

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant “à trous” wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its...

Concept of model based predictive control (MBPC) has been heralded as one of the most significant control developments in recent ten years. Wide range of choice of model structures, prediction horizon, and optimization criteria allows the designer to easily tailor MBPC to its application in industry. The main idea of Model Predictive Control (MPC) is the prediction of the output signal at each ...