Dataset Information

Prediction of lncRNA functions using deep neural networks based on multiple networks.

ABSTRACT:

Background

More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.

Results

In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.

Conclusions

DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

SUBMITTER: Deng L

PROVIDER: S-EPMC10636874 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of lncRNA functions using deep neural networks based on multiple networks.

Deng Lei L Ren Shengli S Zhang Jingpu J

BMC genomics 20231109 Suppl 6

<h4>Background</h4>More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.<h4>Results</h4>In this paper, we propose a new computational method based on global heterogeneous networks to pre ...[more]

PMID: 37946156

Similar Datasets

Project description:With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing and hardware-friendly representation of information. Among the proposed alternatives to the IEEE 754 standard regarding floating point representation of real numbers, the recently introduced Posit format has been theoretically proven to be really promising in satisfying the mentioned requirements. However, with the absence of proper hardware support for this novel type, this evaluation can be conducted only through a software emulation. While waiting for the widespread availability of the Posit Processing Units (the equivalent of the Floating Point Unit (FPU)), we can already exploit the Posit representation and the currently available Arithmetic-Logic Unit (ALU) to speed up DNNs by manipulating the low-level bit string representations of Posits. As a first step, in this paper, we present new arithmetic properties of the Posit number system with a focus on the configuration with 0 exponent bits. In particular, we propose a new class of Posit operators called L1 operators, which consists of fast and approximated versions of existing arithmetic operations or functions (e.g., hyperbolic tangent (TANH) and extended linear unit (ELU)) only using integer arithmetic. These operators introduce very interesting properties and results: (i) faster evaluation than the exact counterpart with a negligible accuracy degradation; (ii) an efficient ALU emulation of a number of Posits operations; and (iii) the possibility to vectorize operations in Posits, using existing ALU vectorized operations (such as the scalable vector extension of ARM CPUs or advanced vector extensions on Intel CPUs). As a second step, we test the proposed activation function on Posit-based DNNs, showing how 16-bit down to 10-bit Posits represent an exact replacement for 32-bit floats while 8-bit Posits could be an interesting alternative to 32-bit floats since their performances are a bit lower but their high speed and low storage properties are very appealing (leading to a lower bandwidth demand and more cache-friendly code). Finally, we point out how small Posits (i.e., up to 14 bits long) are very interesting while PPUs become widespread, since Posit operations can be tabulated in a very efficient way (see details in the text).

Dataset Information

Prediction of lncRNA functions using deep neural networks based on multiple networks.

Background

Results

Conclusions

Publications

Prediction of lncRNA functions using deep neural networks based on multiple networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets