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Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases

Li, Ping (author)
College of Intelligence and Computing, Tianjin University, Tianjin, PR China
Tiwari, Prayag, 1991- (author)
Högskolan i Halmstad,Akademin för informationsteknologi
Xu, Junhai (author)
College of Intelligence and Computing, Tianjin University, Tianjin, PR China
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Qian, Yuqing (author)
College of Intelligence and Computing, Tianjin University, Tianjin, PR China
Ai, Chengwei (author)
College of Intelligence and Computing, Tianjin University, Tianjin, PR China
Ding, Yijie (author)
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, PR China
Guo, Fei (author)
School of Computer Science and Engineering, Central South University, Changsha, PR China
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 (creator_code:org_t)
Amsterdam : Elsevier, 2022
2022
English.
In: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 258
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Current human biomedical research shows that human diseases are closely related to non-coding RNAs, so it is of great significance for human medicine to study the relationship between diseases and non-coding RNAs. Current research has found associations between non-coding RNAs and human diseases through a variety of effective methods, but most of the methods are complex and targeted at a single RNA or disease. Therefore, we urgently need an effective and simple method to discover the associations between non-coding RNAs and human diseases. In this paper, we propose a sparse regularized joint projection model (SRJP) to identify the associations between non-coding RNAs and diseases. First, we extract information through a series of ncRNA similarity matrices and disease similarity matrices and assign average weights to the similarity matrices of the two sides. Then we decompose the similarity matrices of the two spaces into low-rank matrices and put them into SRJP. In SRJP, we innovatively use the projection matrix to combine the ncRNA side and the disease side to identify the associations between ncRNAs and diseases. Finally, the regularization term in SRJP effectively improves the robustness and generalization ability of the model. We test our model on different datasets involving three types of ncRNAs: circRNA, microRNA and long non-coding RNA. The experimental results show that SRJP has superior ability to identify and predict the associations between ncRNAs and diseases. © 2022 The Author(s)

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)

Keyword

Human disease
Joint projection learning
Non-encoding RNA
Sparse regression
Gene–disease network
Information driven care
Informationsdriven vård

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ref (subject category)
art (subject category)

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