Heterogeneous networks integration for disease–gene prioritization with node kernels

Abstract Motivation The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online.

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PAFway: pairwise associations between functional annotations in biological networks and pathways

Bioinformatics ◽

10.1093/bioinformatics/btaa639 ◽

2020 ◽

Vol 36 (19) ◽

pp. 4963-4964

Author(s):

Mahiar Mahjoub ◽

Daphne Ezer

Keyword(s):

Arabidopsis Thaliana ◽

Biological Networks ◽

Gene Networks ◽

Gene Network ◽

Supplementary Information ◽

Supplementary Data ◽

Specific Function ◽

Biological Functions ◽

Functional Annotations ◽

Large Gene

Abstract Motivation Large gene networks can be dense and difficult to interpret in a biologically meaningful way. Results Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network. Availability and implementation PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway. Supplementary information Supplementary data are available at Bioinformatics online.

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Enhancing the prediction of disease–gene associations with multimodal deep learning

Bioinformatics ◽

10.1093/bioinformatics/btz155 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3735-3742 ◽

Cited By ~ 12

Author(s):

Ping Luo ◽

Yuanyuan Li ◽

Li-Ping Tian ◽

Fang-Xiang Wu

Keyword(s):

Disease Gene ◽

Computational Prediction ◽

Integrative Model ◽

Supplementary Information ◽

Disease Genes ◽

Protein Protein Interaction ◽

Gene Associations ◽

Deep Belief Net ◽

Latent Representations ◽

Disease Associated Genes

Abstract Motivation Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease–gene associations. Results In this study, we propose a method to predict disease–gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease–gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease–gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease–gene pairs also demonstrates the ability of dgMDL in predicting new disease–gene associations. Availability and implementation Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL. Supplementary information Supplementary data are available at Bioinformatics online.

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BaiHui: cross-species brain-specific network built with hundreds of hand-curated datasets

Bioinformatics ◽

10.1093/bioinformatics/bty1001 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2486-2488 ◽

Cited By ~ 3

Author(s):

Hong-Dong Li ◽

Tianjian Bai ◽

Erin Sandford ◽

Margit Burmeister ◽

Yuanfang Guan

Keyword(s):

Gene Networks ◽

Genomic Data ◽

Enrichment Analysis ◽

Functional Gene ◽

Supplementary Information ◽

Model Organisms ◽

Brain Disorders ◽

Multiple Model ◽

Network Building ◽

Disease Gene Prioritization

Abstract Motivation Functional gene networks, representing how likely two genes work in the same biological process, are important models for studying gene interactions in complex tissues. However, a limitation of the current network-building scheme is the lack of leveraging evidence from multiple model organisms as well as the lack of expert curation and quality control of the input genomic data. Results Here, we present BaiHui, a brain-specific functional gene network built by probabilistically integrating expertly-hand-curated (by reading original publications) heterogeneous and multi-species genomic data in human, mouse and rat brains. To facilitate the use of this network, we deployed a web server through which users can query their genes of interest, visualize the network, gain functional insight from enrichment analysis and download network data. We also illustrated how this network could be used to generate testable hypotheses on disease gene prioritization of brain disorders. Availability and implementation BaiHui is freely available at: http://guanlab.ccmb.med.umich.edu/BaiHui/. Supplementary information Supplementary data are available at Bioinformatics online.

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How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽

10.3390/electronics10060741 ◽

2021 ◽

Vol 10 (6) ◽

pp. 741

Author(s):

Yuseok Ban ◽

Kyungjae Lee

Keyword(s):

High Precision ◽

Recommender System ◽

Visual Information ◽

Experimental Results ◽

Supplementary Information ◽

Digital Platform ◽

Different Types ◽

Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

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The Role of Understanding on Architectural Beauty: Evidence From the Impact of Semantic Description on the Aesthetic Evaluation of Architecture

Psychological Reports ◽

10.1177/00332941211002135 ◽

2021 ◽

pp. 003329412110021

Author(s):

Sizhe Liu ◽

Wei Zhang ◽

Xianyou He ◽

Xiaoxiang Tang ◽

Shuxian Lai ◽

...

Keyword(s):

Aesthetic Experience ◽

Supplementary Information ◽

Aesthetic Evaluation ◽

Abstract Description ◽

Social Meanings ◽

Different Types ◽

Appearance Design ◽

The Aesthetic ◽

The Impact

There is evidence that greater aesthetic experience can be linked to artworks when their corresponding meanings can be successfully inferred and understood. Modern cultural-expo architecture can be considered a form of artistic creation and design, and the corresponding design philosophy may be derived from representational objects or abstract social meanings. The present study investigates whether cultural-expo architecture with an easy-to-understand architectural appearance design is perceived as more beautiful and how architectural photographs and different types of descriptions of architectural appearance designs interact and produce higher aesthetic evaluations. The results showed an obvious aesthetic preference for cultural-expo architecture with an easy-to-understand architectural appearance design (Experiment 1). Moreover, we found that the aesthetic rating score of architectural photographs accompanied by an abstract description was significantly higher than that of those accompanied by a representational description only under the difficult-to-understand design condition (Experiment 2). The results indicated that people preferred cultural-expo architecture with an easy-to-understand architectural appearance design due to a greater understanding of the design, providing further evidence that abstract descriptions can provide supplementary information and explanation to enhance the sense of beauty of abstract cultural-expo architecture.

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MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Scientific Reports ◽

10.1038/s41598-021-87987-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Léo Pio-Lopez ◽

Alberto Valdeolivas ◽

Laurent Tichit ◽

Élisabeth Remy ◽

Anaïs Baudot

Keyword(s):

Heterogeneous Networks ◽

Heterogeneous Network ◽

Link Prediction ◽

Network Embedding ◽

Multiplex Networks ◽

Multiplex Network ◽

Gene Associations ◽

Different Types ◽

Embedding Methods ◽

Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

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