Predicting primary sequence-based protein-protein interactions using a Mercer series representation of nonlinear support vector machine

© 2022 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/9956991The prediction of protein-protein interactions (PPIs) is essential to understand the cellular processes from a medical perspective. Among the various machine learning techniques, kernel-based Support Vector Machine (SVM) has been commonly employed to discriminate between interacting and non-interacting protein pairs. The main drawback of employing the kernel-based SVM to datasets with many features, such as the primary sequence-based protein-protein dataset, is the significant increase in computational time of training stage. This increase in computational time is mainly due to the presence of the kernel in solving the quadratic optimisation problem (QOP) involved in nonlinear SVM. In order to fix this issue, we propose a novel and efficient computational algorithm by approximating the kernel-based SVM using a low-rank truncated Mercer series as well as desired. As a result, the QOP for the approximated kernel-based SVM will be very tractable in the sense that there is a significant reduction in computational time of training and validating stages. We illustrate the novelty of the proposed method by predicting the PPIs of “S. Cerevisiae” where the protein features extracted using the multiscale local descriptor (MLD), and then we compare the predictive performance of the proposed low-rank approximation with the existing methods. Finally, the new method results in significant reduction in computational time for predicting PPIs with almost as accuracy as kernel-based SVM.The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number IF-2020-NBU-412.Published versio

Uncertainty Quantification of Hydro-morphodynamic Models using Probabilistic Surrogate Models

Quantifying uncertainty in complex hydro-morphodynamics models, particularly those governed by the Navier–Stokes partial differential equations (PDE), is a challenging task due to the complex and highly non-linear relationship of high-dimensional inputs and outputs, coupled with inherent computational complexity. Traditional surrogate models, which provide an efficient approximation of the underlying expensive model, exemplified by the Gaussian process (GP), encounter limitations in accurately capturing the non-Gaussian nature inherent in the input/output relationship. Such limitations restrict their applicability to simpler problems. Furthermore, the applicability of newer hybrid surrogates, such as physics-informed neural networks (PINNs), for uncertainty quantification (UQ) is hindered by the significant computational cost of quantifying uncertainty, which requires a large number of parameters to optimise. This research addresses these challenges by leveraging an efficient non-linear GP model known as the deep Gaussian process (deep GP), which is designed to the complexities of deep learning and modelling high-dimensional complex systems. This model is structured with multiple hidden layers interconnected by non-linear mappings. We explore the applicability of deep Gaussian processes, including their adaptation to replace a complex numerical model that solves the Navier–Stokes equations to model the hydro-morphodynamics around mangrove environments, and development of a novel UQ and uncertainty for deep GP for this high-resolution complex model. The derived findings reveal that the deep GP exhibits remarkable improvements in efficiency, significantly surpassing the baseline UQ method in terms of computational time and accuracy level. Concurrently, it demonstrates an accuracy improvement of over 5 orders of magnitude when contrasted with the standard GP model. Moreover, the deep GP exhibits superior robustness in quantifying uncertainty amidst diverse spatio-temporal complexities compared to its GP counterpart. This research significantly advances the understanding and application of uncertainty quantification in the field of hydro-morphodynamics with significant real-world implications for climate change adaptation and protection mitigation decisions