We employ the Rayleigh entropy maximization to develop a novel IDP scheme which requires computing only five features for each residue of a protein sequence, that is, the Shannon entropy, topological entropy, and the weighted average values of three propensities. Furthermore, our scheme is a linear classification method and hence requires computing simpler decision curves which are more robust as well as using fewer learning samples to compute. The simulation results of our scheme as well as some existing schemes demonstrate its effectiveness.
Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We develop a preprocessing process which exploits different sizes of sliding windows to capture various properties related to MoRFs. We then use the Bayes rule together with the outputs of two trained MLP neural networks to predict MoRFs. In comparison to several state-of-the-art methods, the simulation results show that our method is competitive.
BackgroundMolecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods.ResultsIn this study, a new sequence-based method, named as MoRFMPM, is proposed for predicting MoRFs. MoRFMPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRFCHiBi on the same test sets, MoRFMPM not only obtains higher AUC, but also obtains higher TPR at low FPR.ConclusionsThe features used in MoRFMPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRFMPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable.
As a novel generation of thermal separators, the Wave rotor refrigerator (WRR) has replaced the traditional pressure-wave thermal separator. However, the isentropic refrigeration efficiency still needs to be improved compared with expander. A novel WRR system cycle was built and the system performance was thermal analyzed under various parameters, such as expansion efficiency or compression efficiency of wave rotor. The results are used to compare with the traditional WRR system. It is shown that the advantage provided by this novel cycle over the traditional WRR is an expansion process and a compression process is integrated into one unit, with a higher energy transfer efficiency and simple structure. The isentropic refrigeration efficiency of this novel cycle can be more than twice of the traditional WRR under the pressure ratio of 1.1. The experimental works are carrying out.
The detection of short exons is a challenging open problem in the field of bioinformatics. Due to the fact that the weakness of existing model-independent methods lies in their inability to reliably detect small exons, a model-independent method based on the singularity detection with wavelet transform modulus maxima has been developed for detecting short coding sequences (exons) in eukaryotic DNA sequences. In the analysis of our method, the local maxima can capture and characterize singularities of short exons, which helps to yield significant patterns that are rarely observed with the traditional methods. In order to get some information about singularities on the differences between the exon signal and the background noise, the noise level is estimated by filtering the genomic sequence through a notch filter. Meanwhile, a fast method based on a piecewise cubic Hermite interpolating polynomial is applied to reconstruct the wavelet coefficients for improving the computational efficiency. In addition, the output measure of a paired-numerical representation calculated in both forward and reverse directions is used to incorporate a useful DNA structural property. The performances of our approach and other techniques are evaluated on two benchmark data sets. Experimental results demonstrate that the proposed method outperforms all assessed model-independent methods for detecting short exons in terms of evaluation metrics.
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