modeling potential habitats for pergularia tomentosa using maximum entropy model and effect of environmental variables on its quantitative characteristics in arid rangelands southeastern iran

Predicting the potential habitat of plants in arid regions, especially for medicinal ones, is very important. Although Pergularia tomentosa is a key species for medicinal purposes, it appears in very low density in the arid rangelands of Iran, needing an urgent ecological attention. In this study, we modeled and predicted the potential habitat of P. tomentosa using maximum entropy, and the effects of environmental factors (geology, geomorphology, altitude, and soil properties) on some characteristics of the species were determined. The results showed that P. tomentosa was absent in igneous formation while it appeared in conglomerate formation. In addition, among geomorphological units, the best quantitative characteristics of P. tomentosa was belonged to the conglomerate formation-small hill area (plant aerial parts = 57.63 and root length = 30.68 cm) with the highest electrical conductivity, silt, and CaCO3 content. Conversely, the species was not found in the mountainous area with igneous formation. Moreover, plant density, length of roots, and aerial parts of the species were negatively correlated with soil sand, while positive correlation was observed with CaCO3, EC, potassium, and silt content. The maximum entropy was found to be a reliable method (ROC = 0.91) for predicting suitable habitats for P. tomentosa. These results suggest that in evaluating the plant's habitat suitability in arid regions, contrary to the importance of the topography, some environmental variables such as geomorphology and geology can play the main role in rangeland plants' habitat suitability

Matrix factorization with denoising autoencoders for prediction of drug–target interactions

Drug–target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug–target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction. Since the interaction matrix is often extremely sparse, DTI prediction performance is significantly decreased with matrix factorization-based methods. Therefore, some matrix factorization methods utilize side information to address both the sparsity issue of the interaction matrix and the cold-start issue. By combining matrix factorization and autoencoders, we propose a hybrid DTI prediction model that simultaneously learn the hidden factors of drugs and targets from their side information and interaction matrix. The proposed method is composed of two steps: the pre-processing of the interaction matrix, and the hybrid model. We leverage the similarity matrices of both drugs and targets to address the sparsity problem of the interaction matrix. The comparison of our approach against other algorithms on the same reference datasets has shown good results regarding area under receiver operating characteristic curve and the area under precision–recall curve. More specifically, experimental results achieve high accuracy on golden standard datasets (e.g., Nuclear Receptors, GPCRs, Ion Channels, and Enzymes) when performed with five repetitions of tenfold cross-validation. Graphical abstract: [Figure not available: see fulltext.]Display graphical of the hybrid model of Matrix Factorization with Denoising Autoencoderswith the help side information of drugs and targets for Prediction of Drug-Target Interactions