scholarly journals A learning-based framework for miRNA-disease association identification using neural networks

2019 ◽  
Vol 35 (21) ◽  
pp. 4364-4371 ◽  
Author(s):  
Jiajie Peng ◽  
Weiwei Hui ◽  
Qianqian Li ◽  
Bolin Chen ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Essential Feature ◽  
Interaction Network ◽  
Disease Association ◽  
Feature Representation ◽  
Supplementary Information ◽  
Similarity Network ◽  
Protein Protein Interaction ◽  
Non Coding Rna ◽  
Disease Similarity ◽  
Protein Protein Interaction Network

Abstract Motivation A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes. Results We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction. Availability and implementation The source code and data are available at https://github.com/Issingjessica/MDA-CNN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A learning-based framework for miRNA-disease association identification using neural networks

2018 ◽  
Author(s):  
Jiajie Peng ◽  
Weiwei Hui ◽  
Qianqian Li ◽  
Bolin Chen ◽  
Qinghua Jiang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Essential Feature ◽  
Source Code ◽  
Disease Association ◽  
Feature Representation ◽  
Supplementary Information ◽  
Feature Combination ◽  
Biological Processes ◽  
Non Coding Rna ◽  
Supplementary Material

AbstractMotivationA microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.ResultsWe propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures richer interaction features between diseases and miRNAs based on a three-layer network with an additional gene layer. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction.AvailabilityThe source code and data are available at https://github.com/Issingjessica/[email protected];[email protected];[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals A conserved long intergenic non-coding RNA containing snoRNA sequences, lncCOBRA1, affects Arabidopsis germination and development

2021 ◽  
Author(s):  
Marianne C Kramer ◽  
Hee Jong Kim ◽  
Kyle R Palos ◽  
Benjamin A Garcia ◽  
Eric Lyons ◽  
...  
Keyword(s):  
Brassica Rapa ◽  
Interaction Network ◽  
Crop Species ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
High Level ◽  
Protein Protein Interaction Network ◽  
Mammalian System

Long non-coding RNAs (lncRNAs) are an increasingly studied group of non-protein-coding transcripts with a wide variety of molecular functions gaining attention for their roles in numerous biological processes. Nearly 6,000 lncRNAs have been identified in Arabidopsis thaliana but many have yet to be studied. Here, we examine a class of previously uncharacterized lncRNAs termed CONSERVED IN BRASSICA RAPA (lncCOBRA) transcripts that were previously identified for their high level of sequence conservation in the related crop species Brassica rapa, their nuclear-localization and protein-bound nature. In particular, we focus on lncCOBRA1 and demonstrate that its abundance is highly tissue and developmental specific, with particularly high levels early in germination. lncCOBRA1 contains two snoRNAs domains within it, making it the first sno-lincRNA example in a non-mammalian system. However, we find that it is processed differently than its mammalian counterparts. We further show that plants lacking lncCOBRA1 display patterns of delayed gemination and are overall smaller than wild-type plants. Lastly, we identify the proteins that interact with lncCOBRA1 and examine the protein-protein interaction network of lncCOBRA1-interacting proteins.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

scholarly journals Diabetic Retinopathy and Laser Therapy in Rats: A Protein-Protein Interaction Network Analysis

2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Laser Therapy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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