Gene2DisCo: Gene to disease using disease commonalities

Frasca, Marco

doi:10.1016/j.artmed.2017.08.001

Cited by 7 publications

(4 citation statements)

References 73 publications

(86 reference statements)

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“…Discovering the associated factors with various diseases is an important task in bioinformatics. At present, the existing methods of disease association prediction mainly include matrix decomposition ( Koren et al, 2009 ; Wang et al, 2017 ) network propagation ( Lee et al, 2011 ; Guan et al, 2012 ; Li and Li, 2012 ; Sun et al, 2014 ; Zhou et al, 2015 ), and machine learning ( Luo et al, 2016 ; Zhou and Skolnick, 2016 ; Frasca, 2017 ; Xuan et al, 2019b ; Jiang and Zhu, 2020 ). Essentially, some machine learning methods are also based on similarity measures and matrix decomposition.…”

Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

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Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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“…On the other side, we tested two choices for σ: the harmonic mean (σ 1 ) and mean functions (σ 2 ). Furthermore, another central factor of our model is the computation of the task similarity matrix S, which can be computed by using several metrics (see for instance [15]), and how to group the tasks that should be learned together. We employed in this work the Jaccard similarity measure, since it performed nicely in hierarchical contexts [15,37,14], defined as follows:…”

Section: Model Configurationmentioning

confidence: 99%

“…Multitasking thus plays an important role in a variety of practical situations, including: the prediction of user ratings for unseen items based on rating information from related users [32], the simultaneously forecasting of many related financial indicators [19], the categorization of genes associated with a genetic disorder by exploiting genes associated with related diseases [15].…”

Section: Introductionmentioning

confidence: 99%

Multitask Hopfield Networks

Frasca¹,

Grossi²,

Valentini³

2019

Preprint

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Multitask algorithms typically use task similarity information as a bias to speed up and improve the performance of learning processes. Tasks are learned jointly, sharing information across them, in order to construct models more accurate than those learned separately over single tasks. In this contribution, we present the first multitask model, to our knowledge, based on Hopfield Networks (HNs), named HoMTask. We show that by appropriately building a unique HN embedding all tasks, a more robust and effective classification model can be learned. HoMTask is a transductive semi-supervised parametric HN, that minimizes an energy function extended to all nodes and to all tasks under study. We provide theoretical evidence that the optimal parameters automatically estimated by HoMTask make coherent the model itself with the prior knowledge (connection weights and node labels). The convergence properties of HNs are preserved, and the fixed point reached by the network dynamics gives rise to the prediction of unlabeled nodes. The proposed model improves the classification abilities of singletask HNs on a preliminary benchmark comparison, and achieves competitive performance with state-of-the-art semi-supervised graph-based algorithms.

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“…These computational solutions can save resources by excluding genes unlikely to be associated with diseases. These approaches build on different machine learning techniques (Sun et al, 2011;Zhou and Skolnick, 2016;Frasca, 2017), such as network propagation (Vanunu et al, 2010;Wang et al, 2011;Qian et al, 2014;Jiang, 2015), matrix factorization (Natarajan and Dhillon, 2014), data fusion (Pletscher-Frankild et al, 2015), and deep neural networks (DNNs) (Yang et al, 2018;Luo et al, 2019). They mainly use gene-disease associations (GDAs) collected from public databases [i.e., DisGeNET (Piñero et al, 2020) and OMIM (Hamosh et al, 2005)].…”

Section: Introductionmentioning

confidence: 99%

IsoDA: Isoform–Disease Association Prediction by Multiomics Data Fusion

Huang

Wang

Zhang

et al. 2021

Journal of Computational Biology

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A gene can be spliced into different isoforms by alternative splicing, which contributes to the functional diversity of protein species. Computational prediction of gene-disease associations (GDAs) has been studied for decades. However, the process of identifying the isoform-disease associations (IDAs) at a large scale is rarely explored, which can decipher the pathology at a more granular level. The main bottleneck is the lack of IDAs in current databases and the multilevel omics data fusion. To bridge this gap, we propose a computational approach called Isoform-Disease Association prediction by multiomics data fusion (IsoDA) to predict IDAs. Based on the relationship between a gene and its spliced isoforms, IsoDA first introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to their hosting genes. At the same time, it fuses the genome, transcriptome, and proteome data by joint matrix factorization to improve the prediction of IDAs. Experimental results show that IsoDA significantly outperforms the related state-of-the-art methods at both the gene level and isoform level. A case study further shows that IsoDA credibly identifies three isoforms spliced from apolipoprotein E, which have individual associations with Alzheimer's disease, and two isoforms spliced from vascular endothelial growth factor A, which have different associations with coronary heart disease. The codes of IsoDA are available at http://mlda.swu.edu.cn/codes.php?name=IsoDA

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Gene2DisCo: Gene to disease using disease commonalities

Abstract: Objective. Finding the human genes co-causing complex diseases,

Cited by 7 publications

References 73 publications

Graph Neural Networks and Their Current Applications in Bioinformatics

Graph Neural Networks and Their Current Applications in Bioinformatics

Multitask Hopfield Networks

IsoDA: Isoform–Disease Association Prediction by Multiomics Data Fusion

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