Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks

In this study, membrane proteins were classified using the information hidden in their sequences. It was achieved by applying the wavelet analysis to the sequences and consequently extracting several features, each of them revealing a proportion of the information content present in the sequence. The resultant features were made normalized and subsequently fed into a cascaded model developed in order to reduce the effect of the existing bias in the dataset, rising from the difference in size of the membrane protein classes. The results indicate an improvement in prediction accuracy of the model in comparison with similar works. The application of the presented model can be extended to other fields of structural biology due to its efficiency, simplicity and flexibility.     

Molecular phylogenetic study of the Iranians based on polymorphic markers

The application of polymorphic markers in construction of phylogenetic trees has been documented. Five polymorphic markers located in the PAH gene region including PAH-BglII, PAH-PvuII(A), PAH-EcoRI, PAH-MspI and PAH-STR were selected for analysis of phylogenetic relationships of the Iranians with 15 other populations of the world. The lowest genetic distance was observed between the Iranians and populations residing in Adygei (an ethnic group of the Russian Caucasus), Russia and Druze (a Middle Eastern group). However, East Asian populations including Han, Japanese and Cambodians, Khmer or the Oceanians (Melanesian, Nasioi) showed high genetic distance with the Iranians. The data suggested that the Iranians might have relatively close evolutionary history with the populations residing in Russia rather than East Asian populations. This study provided the first new molecular insight into the evolutionary history of the Iranian population.     

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.     

IL-35, a double-edged sword in cancer

Interleukin 35 (IL-35), a cytokine mainly produced by regulatory T cells (Treg cells), is composed of an Epstein-Barr virus–induced gene 3 β-chain and an IL-12 p35 α-chain. IL-35 causes tumorigenicity in cancer, protects cancer cells against apoptosis, and facilitates cancer progression. However, a few reports have referred to its contradictory roles in cancer prevention. Therefore, the exact purpose of this cytokine in cancer development has become a fundamental question that needs to be answered. In this review, we explain the structure of IL-35 and its receptors and their different signaling pathways. Finally, the function of IL-35 in some cancers and the possible application of this cytokine in approaches for cancer therapy have been discussed.