ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab–based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.

“Rapiéçages faits avec sa propre étoffe”: Discontinuity and convergence in Romance prefixation

This article provides a comprehensive overview of prefixation in Romance languages putting in relation the differences between standard and non-standard varieties in the current synchronic stage and, from a diachronic perspective, the different productivity of verbal prefixation and nominal and adjectival prefixation over the history of Romance languages. The article also deals with the relations between system-internal factors, such as the delimitation and interaction between native and foreign word-formation, as well as the competition between verbal prefixation and other linguistic resources through which spatial information can be expressed. The focus will also be placed on system-external factors, including the diffusion in common language of learned terms which have contributed to revitalizing nominal and adjectival prefixation, although not verbal prefixation. Such an approach makes it possible to account for the higher productivity in current standard Romance languages of nominal and adjectival prefixation compared with verbal prefixation. Furthermore, it provides an explanation for the differences between standard and non-standard Romance languages with regard to the productivity of nominal and adjectival prefixation. The replacement of spatial verbal prefixes with verbs expressing path in the root is interpreted as the result of a more general restructuring of space encoding.