Predicting antimicrobial peptides (AMPs’) function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the ‘CNN-BiLSTM-SVM classifier’ and ‘cellular automata image’, a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.

No abstract

Predicting price of contemporary ceramic artworks is an important and difficult problem, particularly when every object is unique and potential bidder’s tastes may exhibit substantial variation. In recent years, China’s ceramic art market has shown a considerable developing trend, but at the same time, there are also problems that severely restrict its development, such as the chaotic price system. As cultural products, contemporary ceramic artworks have the value of cultural services. Unfortunately, the existing price evaluation models all ignore cultural services. By introducing the “cultural services” and “gray model GM(1, N, x(1)),” a new predictor, called CCCAP-Pre, has been developed to predict prices of contemporary ceramic artworks. As demonstrated, the minimum error, the maximum error, and the average relative error of CCCAP-Pre were 0.02%, 6.19%, and 1.40% on ceramic sculpture artworks and 0.06%, 9.11%, and 3.63% on ceramic painting artworks, respectively. It will provide a reference for the benign development of ceramic art market.

No abstract

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called "pLoc_bal-mHum" was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a "deep learning", a very powerful technique developed recently. The present study was devoted to incorporate the "deep-learning" technique and develop a new predictor called "pLoc_Deep-mHum". The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.