MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists.

Perfect match between borophene and aluminium in AlB3 heterostructure with covalent Al-B bonds, multiple Dirac points and high fermi velocity

By performing swarm-intelligent global structure search combined with first-principles calculations, a stable two-dimensional (2D) AlB3 heterostructure with directed, covalent Al-B bond forms due to a nearly perfect lattice match between...

The Christian Contribution to the Changes of the Development Aid System: The Lutheran Approach

Unlike the previous decades, the global development aid system is more willing to admit a significant role of faith-based organisations in promoting development thinking and in the distribution of development aid. The Lutheran World Federation (LWF) approach significantly contributes to this new thinking, especially as the theological background, global structures, and long-year experience in diaconal work enable the LWF's experts to make credible and feasible utterances in the field of development aid. The article outlines the meaning and global structure of the development aid and contrasts it with the Lutheran, Christian approach to development. It stresses the significance of the theological background of such terms as sustainability and sustainable development and specific assets ascribed to faith-based organisations. The text synthesizes information and observations from relevant literature on development and selected documents of the LWF.

Structure Analysis of Deep Water Spar Platform

In order to study calculation technology of global structure strength for the deep water typical Spar platform its global structural strength analysis is completed. The dynamic-part and a low frequency-part loads are considered in this analysis. First, according to the 100-year return storm design wave parameters are obtained through search. wave loads with design wave parameters are calculated, and are applied to the structural model built. The stresses of global structure are gotten by finite element structural analysis. Then, low frequency-part loads which include wind, current and mooring forces also are applied to the structural model. The stresses produced by low frequency-part loads are gotten by finite element structural analysis. Finally the stresses produced by dynamic-part and a low frequency-part loads are combined to form total stresses of structure of Spar, and evaluation of structural strength of Spar is made in term of the rule. Analysis method for the structural strength of the deepwater typical Spar platform can be used as reference for relative technical people.

L1-Norm Distance Discriminant Analysis with Multiple Adaptive Graphs and Sample Reconstruction

Linear discriminant analysis (LDA) is sensitive to noise and its performance may decline greatly. Recursive discriminative subspace learning method with an L1-norm distance constraint (RDSL) formulates LDA with the maximum margin criterion and becomes robust to noise by applying L1-norm and slack variables. However, the method only considers inter-class separation and intra-class compactness and ignores the intra-class manifold structure and the global structure of data. In this paper, we present L1-norm distance discriminant analysis with multiple adaptive graphs and sample reconstruction (L1-DDA) to deal with the problem. We use multiple adaptive graphs to preserve intra-class manifold structure and simultaneously apply the sample reconstruction technique to preserve the global structure of data. Moreover, we use an alternating iterative technique to obtain projection vectors. Experimental results on three real databases demonstrate that our method obtains better classification performance than RDSL.

Cutting L1-Norm Distance Discriminant Analysis with Sample Reconstruction

Dimensionality reduction plays an important role in the fields of pattern recognition and computer vision. Recursive discriminative subspace learning with an L1-norm distance constraint (RDSL) is proposed to robustly extract features from contaminated data and L1-norm and slack variables are utilized for accomplishing the goal. However, its performance may decline when too many outliers are available. Moreover, the method ignores the global structure of the data. In this paper, we propose cutting L1-norm distance discriminant analysis with sample reconstruction (C-L1-DDA) to solve the two problems. We apply cutting L1-norm to measure within-class and between-class distances and thus outliers may be strongly suppressed. Moreover, we use cutting squared L2-norm to measure reconstruction errors. In this way, outliers may be constrained and the global structure of data may be approximately preserved. Finally, we give an alternating iterative algorithm to extract feature vectors. Experimental results on two publicly available real databases verify the feasibility and effectiveness of the proposed method.

On the Autoregressive Time Series Model Using Real and Complex Analysis

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.