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Sparse regularized ...
Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases
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- Li, Ping (författare)
- College of Intelligence and Computing, Tianjin University, Tianjin, PR China
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- Tiwari, Prayag, 1991- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Xu, Junhai (författare)
- College of Intelligence and Computing, Tianjin University, Tianjin, PR China
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- Qian, Yuqing (författare)
- College of Intelligence and Computing, Tianjin University, Tianjin, PR China
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- Ai, Chengwei (författare)
- College of Intelligence and Computing, Tianjin University, Tianjin, PR China
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- Ding, Yijie (författare)
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, PR China
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- Guo, Fei (författare)
- School of Computer Science and Engineering, Central South University, Changsha, PR China
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(creator_code:org_t)
- Amsterdam : Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 258
- Relaterad länk:
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https://doi.org/10.1...
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https://hh.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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)
Ämnesord
- 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)
Nyckelord
- Human disease
- Joint projection learning
- Non-encoding RNA
- Sparse regression
- Gene–disease network
- Information driven care
- Informationsdriven vård
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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