A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology

Dual-channel hypergraph convolutional network for predicting herb-disease associations

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

Normalized L3-based link prediction in protein-protein interaction networks

BACKGROUND

Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, high-throughput experiments are often insufficient to complete the PPI interactome of different organisms. Computational techniques are thus used to infer missing data, with link prediction being one such approach that uses the structure of the network of PPIs known so far to identify non-edges whose addition to the network would make it more sound, according to some underlying assumptions. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. Interestingly, the L3 principle can be interpreted in another way, so that other signatures of PPI networks can also be characterized for PPI predictions. This alternative interpretation uncovers candidate PPIs that the current L3-based link predictors may not be able to fully capture, underutilizing the L3 principle.

RESULTS

In this article, we propose a formulation of link predictors that we call NormalizedL3 (L3N) which addresses certain missing elements within L3 predictors in the perspective of network modeling. Our computational validations show that the L3N predictors are able to find missing PPIs more accurately (in terms of true positives among the predicted PPIs) than the previously proposed methods on several datasets from the literature, including BioGRID, STRING, MINT, and HuRI, at the cost of using more computation time in some of the cases. In addition, we found that L3-based link predictors (including L3N) ranked a different pool of PPIs higher than the general-purpose link predictors did. This suggests that different types of PPIs can be predicted based on different topological assumptions, and that even better PPI link predictors may be obtained in the future by improved network modeling.

Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions

Long non-coding RNAs (lncRNAs) have attracted extensive attention due to their important roles in various biological processes, among which lncRNA-protein interaction plays an important regulatory role in plant immunity and life activities. Laboratory methods are time consuming and labor-intensive, so that many computational methods have gradually emerged as auxiliary tools to assist relevant research. However, there are relatively few methods to predict lncRNA-protein interaction of plant. Due to the lack of experimentally verified interactions data, there is an imbalance between known and unknown interaction samples in plant data sets. In this study, a multi-feature fusion method based on linear neighborhood propagation is developed to predict plant unobserved lncRNA-protein interaction pairs through known interaction pairs, called MPLPLNP. The linear neighborhood similarity of the feature space is calculated and the results are predicted by label propagation. Meanwhile, multiple feature training is integrated to better explore the potential interaction information in the data. The experimental results show that the proposed multi-feature fusion method can improve the performance of the model, and is superior to other state-of-the-art approaches. Moreover, the proposed approach has better performance and generalization ability on various plant datasets, which is expected to facilitate the related research of plant molecular biology.