As more and more genomes have been discovered in recent years, there is an urgent need to develop a reliable method to predict the subcellular localization for the explosion of newly found proteins. However, many well-known prediction methods based on amino acid composition have problems utilizing the sequence-order information. Here, based on the concept of Chou's pseudo amino acid composition (PseAA), a new feature extraction method, the multi-scale energy (MSE) approach, is introduced to incorporate the sequence-order information. First, a protein sequence was mapped to a digital signal using the amino acid index. Then, by wavelet transform, the mapped signal was broken down into several scales in which the energy factors were calculated and further formed into an MSE feature vector. Following this, combining this MSE feature vector with amino acid composition (AA), we constructed a series of MSEPseAA feature vectors to represent the protein subcellular localization sequences. Finally, according to a new kind of normalization approach, the MSEPseAA feature vectors were normalized to form the improved MSEPseAA vectors, named as IEPseAA. Using the technique of IEPseAA, C-support vector machine (C-SVM) and three multi-class SVMs strategies, quite promising results were obtained, indicating that MSE is quite effective in reflecting the sequence-order effects and might become a useful tool for predicting the other attributes of proteins as well.

The observation that real estate agents sell their clients' homes cheaper and faster than their own homes has been well identified in the literature and interpreted as evidence of an agency problem originated from information asymmetry. This article studies whether this well‐known result holds true for all types of agents and clients, and whether information asymmetry is the full story. By using the Multiple Listing Service (MLS) data from Indiana, we find that, after controlling for observables, mainly homes owned by institutional clients are sold cheaper and faster than agent‐owned homes, and the differences are mainly driven by less and moderately experienced agents. Besides information asymmetry, we also find evidence of motivation heterogeneity—institutions themselves are very motivated to sell, and therefore are willing to sell cheaper in order to sell faster.

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