Diseasome

2011 ◽  
Vol 29 (1) ◽  
pp. 55-72 ◽  
Author(s):  
Kenneth Wysocki ◽  
Leslie Ritter
Keyword(s):  
Open Source ◽  
Protein Interaction ◽  
Molecular Interaction ◽  
Disease Gene ◽  
Disease Network ◽  
Computational Tools ◽  
Protein Protein Interaction ◽  
Complex Interactions ◽  
Gene Associations ◽  
Human Disease Network

Using bioinformatics computational tools, network maps that integrate the complex interactions of genetics and diseases have been developed. The purpose of this review is to introduce the reader to new approaches in understanding disease–gene associations using network maps, with an emphasis on how the human disease network (HDN) map (or diseasome) was constructed. A search was conducted in PubMed using the years 1999–2011 and using key words diseasome, molecular interaction, interactome, protein–protein interaction, and gene. The information reviewed included journal reviews, open source and webbased databases, and open source computational tools.

scholarly journals The expanded human disease network combining protein–protein interaction information

2011 ◽  
Vol 19 (7) ◽  
pp. 783-788 ◽  
Author(s):  
Xuehong Zhang ◽  
Ruijie Zhang ◽  
Yongshuai Jiang ◽  
Peng Sun ◽  
Guoping Tang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Human Disease ◽  
Disease Network ◽  
Interaction Information ◽  
Protein Protein Interaction ◽  
Human Disease Network

scholarly journals The translational network for metabolic disease – from protein interaction to disease co-occurrence

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yonghyun Nam ◽  
Dong-gi Lee ◽  
Sunjoo Bang ◽  
Ju Han Kim ◽  
Jae-Hoon Kim ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Human Disease ◽  
Metabolic Diseases ◽  
Interaction Network ◽  
Disease Network ◽  
Protein Protein Interaction ◽  
Primary Disease ◽  
Human Disease Network ◽  
Protein Protein Interaction Network ◽  
Random Guessing

Abstract Background The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing ‘n-of-1 utility’ (n potential diseases of one patient) to human disease network—the translational disease network. Results We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. Conclusions The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

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

2019 ◽  
Vol 35 (19) ◽  
pp. 3735-3742 ◽  
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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