RESEARCH ON TRANSBOUNDARY REGULATION OF PLANT-DERIVED EXOGENOUS MIRNA BASED ON BIOLOGICAL BIG DATA - HINDAWI.COM

Hindawi
Journal of Healthcare Engineering
Volume 2021, Article ID 6656763, 13 pages
https://doi.org/10.1155/2021/6656763

Research Article
Research on Transboundary Regulation of Plant-Derived
Exogenous MiRNA Based on Biological Big Data

 Zhi Li ,1 Xu Wei,2 Shuyi Li,3 Jiashi Zhao,1 Xiang Li,1 and Liwan Zhu1
 1
 School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
 2
 College of Computer Science and Technology, Jilin University, Changchun 130012, China
 3
 School of Foreign Languages, Harbin Institute of Technology, Harbin 150001, China

 Correspondence should be addressed to Zhi Li; 153659073@qq.com

 Received 27 November 2020; Revised 6 January 2021; Accepted 13 January 2021; Published 31 January 2021

 Academic Editor: Le Zhang

 Copyright © 2021 Zhi Li et al. This is an open access article distributed under the Creative Commons Attribution License, which
 permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 In recent years, researchers have discovered plant miRNA (plant xenomiR) in mammalian samples, but it is unclear whether it exists
 stably and participates in regulation. In this paper, a cross-border regulation model of plant miRNAs based on biological big data is
 constructed to study the possible cross-border regulation of plant miRNAs. Firstly, a variety of human edible plants were selected, and
 based on the miRNA data detected in human experimental studies, screening was performed to obtain the plant xenomiR that may
 stably exist in the human body. Then, we use plant and animal target gene prediction methods to obtain the mRNAs of animals and
 plants that may be regulated, respectively. Finally, we use GO (Gene Ontology) and the Multiple Dimensional Scaling (MDS)
 algorithm to analyze the biological processes regulated by plants and animals. We obtain the relationship between different biological
 processes and explore the regulatory commonality and individuality of plant xenomiR in plants and humans. Studies have shown that
 the development and metabolic functions of the human body are affected by daily eating habits. Soybeans, corn, and rice can not only
 affect the daily development and metabolism of the human body but also regulate biological processes such as protein modification
 and mitosis. This conclusion explains the reasons for the different physiological functions of the human body. This research is an
 important meaning for the design of small RNA drugs in Chinese herbal medicine and the treatment of human nutritional diseases.

1. Introduction In the process of studying the regulatory functions of
 miRNAs, plant miRNAs were found in mammalian samples
MicroRNA (miRNA) is a type of noncoding single-stranded [3–8]. Researchers infer that these plant miRNAs enter the
small RNA with a size of 21–23 nucleotides. It binds to animal’s body through food. Researchers call these exoge-
mRNA through the base complementation rule, degrades nous miRNAs of plant origin (plant xenomiR).
mRNA, inhibits mRNA translation, and ultimately regulates Although there are many studies on plant xenomiR
gene expression [1]. It plays a role in all aspects of the life transboundary regulation, they are all based on a single plant
cycle of biological cells. With the development of genome miRNA. However, studies have found that not all plant
sequencing technology and the emergence of miRNA ver- miRNAs can enter the human body. The human body
ification methods based on high-throughput biological big specifically absorbs plant miRNA. Therefore, it is meaningful
data, researchers have discovered more miRNAs and studied to select plant miRNAs that exist in animals for subsequent
them in depth. The expression regulation function of analysis. The determination of plant xenomiR is the key to
miRNA has become a research hotspot in this field [2]. this research [9–11].
 Studies have shown that miRNAs can not only function in In this article, on the basis of plant xenomiR trans-
their own body but also perform cross-border regulation. As an boundary regulation, a research model of plant xenomiR
exogenous biological activity unit, it can affect the expression of transboundary regulation based on biological big data is
heterologous mRNA through base complementation. proposed and constructed. The possible regulatory functions

2 Journal of Healthcare Engineering

of plant XenomiR in animals and plants were studied, re- 2.2. Data Acquisition and Preprocessing. Data needed for
spectively, and the Multiple Dimensional Scaling (MDS) study: miRNA and mRNA of edible plants (soybean, rice,
algorithm was used to analyze the regulatory functions of and corn), sequencing data based on high-throughput hu-
different species, to study the cross-species regulation man miRNA, human mRNA data, gene annotation files of
mechanism of XenomiR on different kinds of edible plants. edible plants and humans.
Not only the regulation effect of plant xenomiR on the plant The miRNA data of plant crops comes from the miRBase
body but also its regulation effect on the human body can be database (http://www.mirbase.org/); the database version is
obtained. Explore these plant xenomiR regulatory charac- 22.1 (October 2018). The study is to explore the role of plant
teristics. This model can be applied to the research of cross- miRNAs obtained by humans through food. Therefore, the
species regulation mechanism, Chinese herbal medicine necessary conditions for selecting crops for research are as
efficacy, nutrition, and other fields. follows: crops that are often eaten by humans in daily life and
 related data are perfect for subsequent analysis. After
 screening, the final eligible crops are as follows: soybeans,
2. Materials and Methods rice, and corn.
 Related information is shown in Table 1.
2.1. Technical Route. In this paper, a research model of plant
 The research needs to obtain plant miRNA data found in
xenomiR transboundary regulation based on biological big
 human samples. Qi Zhao and others in our laboratory
data is constructed. Firstly, select the miRNA data of various
 analyzed 388 human small RNA sequencing data and de-
human edible plants, and use the miRNA data detected in
 tected a total of 484 plant miRNAs, including 166 unique
the human body obtained by the second-generation se-
 miRNA sequences [12].
quencing technology to perform data screening to obtain the
 The relevant plant mRNA data were downloaded from
plant xenomiR data present in human samples. Then, using
 NCBI (https://www.ncbi.nlm.nih.gov/), and the human
plant and animal target gene prediction methods, the plant
 mRNA data were downloaded from GENCODE (https://
and animal mRNA that may be regulated are obtained,
 ucscgenomics.soe.ucsc.edu/gencode/). The relevant data
respectively. Finally, perform functional analysis of mRNA,
 statistics are shown in Table 2.
use GO enrichment analysis and the Multiple Dimensional
 Compare the obtained miRNA data of soybean, rice, and
Scaling (MDS) algorithm to analyze its biological processes
 corn with the 166 plant miRNA data found in human
in plants and animals from the same perspective, and obtain
 samples to obtain the source of plant xenomiR. The sta-
the relationship between different biological processes. Look
 tistical results are shown in Table 3.
for the regulatory commonalities and individualities of plant
miRNAs that coexist in plants and humans.
 The technical route is shown in Figure 1. 2.3. Target Gene Prediction. Find the target genes of plants
 and humans corresponding to the target of the corre-
 (1) Data acquisition: choose a variety of plants that are
 sponding plant xenomiR.
 edible by humans. Obtain human miRNA second-
 In this paper, the tapir algorithm based on RNAHybrid
 generation sequencing data, miRNA sequence data,
 was used to predict the target gene [13, 14]. The Tapir al-
 and mRNA data of related plants from professional
 gorithm can set its own parameters to make the results more
 public databases and literature data. Search for data
 accurate. The Tapir algorithm is specially designed for plant
 related to plant miRNA in human miRNA second-
 miRNAs and has a good predictive effect on plant miRNA
 generation sequencing data.
 target genes. The screening criteria used are as follows: exact
 (2) Target gene prediction: use the target gene prediction match of seed region; remove the results with more than 3
 algorithm for xenomiR mRNA comparison and bases of mismatches; set the MFE (minimum free energy)
 statistical screening to obtain the target gene data of threshold of the result to −25; and set the p value threshold
 plant xenomiR in plants and humans. to 0.05. When the above conditions are met, it is deemed to
 (3) Core node screening: use the LeaderRank algorithm meet the standards.
 to calculate the score of each node and find the core The prediction process used in this paper is shown in
 node of the network. Figure 2.
 (4) Core network construction and function enrichment The relevant statistical results are shown in Table 4.
 analysis: build a biological regulatory network Since three edible plants were selected in this article, all
 through core nodes and combine it with function experimental results in this article are divided into six
 enrichment analysis to study the similarities and groups.
 differences between these miRNAs participating in
 the biological processes of plants and humans. 2.4. Core Node Screening. The currently acquired data
 (5) GO analysis: analyze the similarities and differences contains a large number of independent nodes or nodes that
 of the data in different comparison groups, and are not closely related to other network nodes. This infor-
 analyze the GO semantic relationship of these data mation makes the biological pathways unobvious and fails to
 by using the MDS algorithm. Explore the com- reflect the core of the network, making it difficult to un-
 monalities and individual differences of its function derstand the regulatory network. Therefore, it is necessary to
 of gene regulation. find the core node after filtering the nodes.

Journal of Healthcare Engineering 3

 Data Data Target gene The core GO semantic
 collection preprocessing prediction node filter analysis

 Threshold MDS
 miRBase Formatting and setting algorithm
 standardization Algorithm
 NCBI selection Leader
 rank
 Data
 deduplication
 GENCODE Database
 comparison
 Construction
 Data filtering of the core
 Plant network
 miRNA Screening of
 sequences target genes
 Classification Functional
 inhuman
 and sorting enrichment
 samples
 analysis

 Figure 1: Research model of plant xenomiR transboundary regulation based on biological big data.

 Table 1: Plant information table. The LeaderRank algorithm is used to score and rank the
 nodes required by the biological regulatory network. It adds
Name Scientific name Quantity the background node b based on the PageRank algorithm
Soybeans Glycine max (Linn.) Merr. 756 and connects it with other nodes to form a strong con-
Rice Oryza sativa japonica group 738 nection graph. The degree of each node is greater than 0, to
corn Zea mays Linn. 325
 avoid the existence of isolated nodes, so that the results can
 converge faster [15, 16].
 Set the LeaderRank score of node b as Sb � 0 and the
 scores of other n nodes as Si � 1, through the iterative
 Table 2: Plant, crop, and human mRNA data. formula:
Species name Soybean Rice Corn Human
 n+1 aji
Quantity 71149 42474 56152 206694
 Si (t) � S (t − 1). (1)
 j�1
 Oj j

 Table 3: Statistics of plant xenomiR. After the element value gradually converges, we use tc to
 represent the number of convergence and then obtain the
Plant name Soybean Rice Corn
 score of each node:
Quantity 42 46 29
Percentage 5.56% 6.23% 8.92% Sb tc 
 Si � Si tc + . (2)
 n
 After using the above method to operate, the changes in
 the number of nodes before and after are shown in Table 5.
 Database
 Tapir comparison
 RNAHybrid 2.5. GO Analysis Based on Multiple Dimensional Scaling
 David String Algorithm. By performing functional enrichment analysis
 Plant miRNA on multiple sets of data, information such as the contained
 biological processes can be obtained. However, usually due
 Reliable to the diversity of results, it is difficult to dig out meaningful
 Plant mRNA
 Target gene screening
 target genes biological information from a large amount of information.
 The dimensionality reduction processing of the data
 Human
 mRNA Seed region Reliability through the MDS algorithm can ensure that the distance
 match threshold between the data samples after dimensionality reduction
 Thermodynamic Number of
 does not change compared with that before dimensionality
 parameters mismatches reduction, which is very helpful for GO semantic analysis
 [17].
 In the MDS algorithm, assuming that there are m
 Figure 2: Plant xenomiR target gene prediction process. samples in the data, their sample space is

4 Journal of Healthcare Engineering

 Table 4: Statistics of target gene prediction results.
 Soybean-soybean Rice-rice Corn-corn Soybean-person Rice-person Corn-person
Target 1119 767 443 1419 2409 1343
GeneID 380 396 214 378 632 358

 Table 5: Statistics of the number of nodes before and after screening.
 Soybean-soybean Rice-rice Corn-corn Soybean-person Rice-person Corn-person
Before screening 380 396 214 378 632 358
After screening 156 129 54 260 231 43

 T � x1 ,x2 , x3 , x4 , . . . , xm , xi ∈ Rd . (3) 1 m 2
 d2i. � d ,
 m j�1 ij
 Assuming that the matrix D is the distance between
 (13)
samples and D ∈ Rm×m , the distance between sample xi and
 1 m
sample xj is the element dij in D. The sample in the new d2.j � d2ij ,
space can be expressed as m i�1
 ′
 Z ∈ Rd ×m , 1 m m 2
 (4) d2.. � dij . (14)
 d′ ≤ d. m2 i�1 j�1
 The Euclidean distance between the new and old data in In summary,
the two spaces is the same as
 1
 Zi − Zj � dij . (5) bij � − d2ij − d2i. − d2.j + d2.. . (15)
 2
 Let the inner product matrix of the sample after di- In this algorithm, D is kept unchanged, that is, the
mensionality reduction be distance between samples is unchanged, and so the inner
 product matrix B can be calculated by D. At the same time,
 B � ZT Z ∈ Rm×m . (6)
 because B is a symmetric matrix, it can be feature decom-
 And bij � ZTi Zj ; then, position, and we can get

 d2ij � Zi 2 + Zj 2 − 2ZTi Zj � bii + bjj − 2bij . (7) B � VΔVT , (16)

 where ∧ is the diagonal matrix, which is composed of ei-
 When the sample Z after dimensionality reduction is
 genvalues, and V is the corresponding eigenvector matrix.
centered mi�1 Zi � 0, the sum of the rows and columns of Assuming that there are d∗ nonzero eigenvalues, they
matrix B is 0; that is,
 can form a diagonal matrixΛ∗ . Assuming Λ∗ is the corre-
 m m
 sponding eigenvector matrix, the sample in the new space
 bij � bij � 0. (8) represents Z:
 i�1 j�1
 ∗
 Z � Λ1/2 T
 ∗ V∗ ∈ R
 d ×m
 . (17)
 When using tr () to represent the matrix,
 m
 trB � Z2i . (9)
 j�1
 3. Results and Discussion
 Available: 3.1. Functional Enrichment Analysis of Plant-Plant Group.
 For the plant’s own regulation mode, we select the data with
 m
 d2ij � tr(B) + mbjj , p value ≤0.05 for analysis, take log10 p value, and sort them
 (10)
 i�1
 from large to small, combined with the number of genes in
 the relevant biological process.
 m The statistical results of soybean-soybean biological
 d2ij � tr(B) + mbii , (11) process information are shown in Figure 3.
 j�1 The statistical results of the soybean protein interaction
 network are shown in Figure 4.
 m m
 The statistical results of rice-rice biological process in-
 d2ij � 2mtr(B). (12)
 i�1 j�1
 formation are shown in Figure 5.
 The statistical results of the rice-protein interaction
 Make network are shown in Figure 6.

Count

 0
 5
 10
 15
 20
 25
 GO:0006725
 GO:1901360
 GO:0044237
 GO:0044260
 GO:0050789
 GO:0008152
 GO:0071704

 –log10p (value)
 GO:0010467
 Journal of Healthcare Engineering

 GO:0010468 Count
 GO:0090304
 GO:0009987
 0
 5
 10
 15
 20
 25
 30

 GO:0034645
 GO:0008150
 GO:0044238
 GO:0034641 GO:0048366
 GO:0050794 GO:0080060
 GO:0044249 GO:0045962
 GO:1901576 GO:0048653
 GO:0006351 GO:0009965
 GO:0045926
 GO:0016070 GO:0048263
 GO:0006355 GO:0006468
 GO:0044763 GO:0010073
 –log10p (value)
 GO:0044271 GO:2000025
 GO:0046274 GO:0051570
 GO:0051716 GO:0009855
 GO:0044248 GO:0010072
 GO:0050896 GO:0010228
 GO:1901575 GO:0009880
 GO:0007275 GO:0046274
 GO:0009791 GO:0009887
 GO:0048731 GO:0043068
 GO:0046907 GO:0010928
 GO:0051641 GO:0010051
 GO:0048513 GO:0007155
 GO:0016482 GO:0009856
 GO:0061458 GO:0006952
 GO:0044710 GO:0010162
 GO:0048569 GO:0055114
 GO:0007165 GO:0030154
 GO:0044700 GO:0031935
 GO:0016458 GO:0009955
 GO:0048519 GO:0048235
 GO:0010014
 GO:0007154 GO:0055085
 GO:0006886 GO:0045010
 GO:0034613
 Figure 4: Soybean protein interaction network.
 GO:1902582
 GO:0009888
 2
 4
 6
 8
 10
 12

 GO:0017038
 GO:0071702
 GO:0006950 –log10 (pvalue)
 GO:0006259
 GO:0009653
 GO:0033365
 GO:0044267
 GO:0048438
 GO:0072594
 GO:0051603

Figure 5: Rice-Rice biological process information statistics map (total of 59 items).
 GO:0010016
 Figure 3: Soybean-soybean biological process information statistics map (total of 33 items).

 2
 4
 6
 8
 10

 –log10 (pvalue)
 5

6 Journal of Healthcare Engineering

 process, protein ubiquitination, regulation of mitotic cell
 cycle, and so forth. There is no obvious correlation between
 various biological processes (see Figure 16).

 3.3.2. Rice. Rice miRNAs regulate the biological metabolism
 and development of rice itself, as well as humans. At the
 same time, it can also regulate the response of cells to ex-
 ternal stimuli, cell processes, and cell communication.
 Most of the regulatory effects of rice miRNA are re-
 lated to metabolism and development. Metabolism in-
Figure 6: Rice-protein interaction network (metabolism-related). cludes the metabolic process of cellular aromatic
 compounds, the metabolic process of organic cyclic
 compounds, the cellular metabolic process, the cellular
 The statistical results of corn-corn biological process macromolecular metabolic process, and the organic
information are shown in Figure 7. matter metabolic processes. Development-related in-
 The statistical results of the corn protein interaction cludes multicellular organism development, flower de-
network are shown in Figure 8. velopment, postembryonic development, system
 development, organ development, reproductive system
3.2. Functional Enrichment Analysis of Plant-Human Group. development, postembryonic organ development, and
In the experiment of plant and colleagues, the data tissue development (see Figure 17)
p value ≤ 0.05 are also selected for analysis. Most of the regulatory effects of rice miRNA in the
 The statistical results of soybean-human biological human body are also related to metabolism and devel-
process information are shown in Figure 9. opment. Metabolism includes organic matter metabolism,
 The statistical results of the soybean-human protein primary metabolism, cellular macromolecular meta-
interaction network are shown in Figure 10. bolism, cell metabolism, nitrogen compound metabolism,
 According to its biological process information, rice has and phosphorus metabolism process. Development-re-
a particularly obvious regulation in human development. lated includes nervous system development, neuron
The protein interaction network for nervous system de- production, cell differentiation, brain development and
velopment is shown in Figure 11. ventricular system development, and lateral ventricle
 The statistical results of rice-human biological process development. At the same time, it can also regulate the
information are shown in Figure 11. response of cells to stimuli and regulate cell processes,
 The statistical results of rice-human-protein interaction cell-to-cell communication, and cell division cycles (see
network are shown in Figure 12. Figure 18).
 The statistical results of corn-human biological process
information are shown in Figure 13. 3.3.3. Corn. Maize miRNAs can regulate metabolism in
 The statistical results of corn-human-protein interaction both plants and humans, and they can also regulate bio-
network are shown in Figure 14. logical processes.
 Most of the regulatory effects of maize miRNA in maize
3.3. Biological Process and GO Semantic Analysis. In the GO are related to metabolism and transcription and translation,
semantic analysis graph, color is used to represent the such as lignin catabolism, phenyl propane catabolism, cel-
change of log10 p value, the larger the value, the warmer the lular aromatic compound metabolism, phenyl propane
 metabolism, and organic cyclic compound metabolism.
color, and the radius is used to represent the value of log size.
 Transcription and translation-related include nucleic acid
 template transcription, biosynthetic process, nucleic acid
3.3.1. Soybean. Soybean miRNAs play different regulatory template transcription, regulation of gene expression in
roles in plants and humans, and their regulatory roles in biosynthesis process, regulation of nucleic acid template
humans are quite different. transcription, and regulation of RNA biosynthesis process
 Soybean miRAN plays a regulatory role in soybean that (see Figure 19).
is mostly related to plant development, such as leaf devel- The miRNA of corn can regulate the response to
opment, integument development, another development, stimuli in the human body, such as cell response to en-
and positive developmental regulation (see Figure 15). dogenous stimuli, cell response to peptide hormone
 The regulatory role of soybean miRNA in the human stimulation, cell response to growth factor stimulation,
body is related to cell protein modification process, cell cell response to organic matter, and cell response reaction
response to external stimuli, cell protein metabolism to chemical stimuli. It can regulate metabolism, such as

Count

 0
 2
 4
 6
 8
 10

 GO:0010468
 GO:0051171
 Journal of Healthcare Engineering

 Count GO:0080098
 GO:0031323

 0
 10
 40
 50
 70
 80
 GO:0060255
 GO:0019222

 –log10p (value)
 GO:0006355
 GO:0006464 GO:2001141
 GO:1903506
 GO:0051252
 GO:0043412 GO:0019219
 GO:2000112
 GO:0010556

 –log10p (value)
 GO:0031326
 GO:0071498 GO:0009889
 GO:0006351
 GO:0097659
 GO:0007346 GO:0032774
 GO:0050794
 GO:0050789
 GO:0016567 GO:0016070
 GO:0032502
 GO:0006725
 GO:0032446 GO:1901360
 GO:0065007
 GO:0034654
 GO:0046274
 GO:0044267 GO:0046271
 GO:0090304

 Figure 8: Corn protein interaction network.
 GO:0019438
 GO:0009808
 GO:0018130

 1.70
 1.75
 1.80
 1.85
 1.90
 GO:1901362
 –log10 (pvalue) GO:0009698
 GO:0006139
 GO:0019748
 4
 5
 6
 7

 Figure 7: Corn-corn biological process information statistics map (total of 37 entries).

 –log10 (pvalue)

Figure 9: Soybean-human biological process information statistics map (total of 7 entries).
 7

8

 Count

 0
 50
 100
 150
 200
 GO:0030011
 GO:0071704
 GO:0071840
 GO:0044238
 GO:0007154
 GO:0008152
 GO:0009966

 –log10p (value)
 GO:0010646
 GO:0016043
 GO:0023051
 GO:0033043
 GO:0035556
 GO:0035556
 GO:0038061
 GO:0043087
 GO:0043170
 GO:0044237
 GO:0044260
 GO:0051716
 GO:0071496
 GO:0048731
 GO:0006793
 GO:0006807
 GO:0007166
 GO:0007399
 GO:0009987
 GO:0023052
 GO:0048583
 GO:0050789
 GO:0060322
 GO:0022008
 GO:0048699
 GO:2000725
 GO:0006464
 GO:0006468
 GO:0006796
 GO:0007165
 GO:0008277
 GO:0009967
 GO:0010611
 GO:0010611
 GO:0010647
 GO:0016310
 GO:0019538
 GO:0022603
 GO:0023056
 GO:0032502
 GO:0043502
 GO:0043547
 GO:0044267
 GO:0048522
 GO:0050793
 Figure 10: Soybean-human protein interaction network (related to protein ubiquitination).

Figure 12: Rice-human-protein interaction network (related to nervous system development).
 1.7
 1.8
 1.9
 2.0
 2.1
 2.2
 2.3
 2.4
 2.5

 –log10 (pvalue)

 Figure 11: Rice-human biological process information statistics map (total of 90 entries showing only partial information).
 Journal of Healthcare Engineering

Journal of Healthcare Engineering 9

 1.42
 40

 35 1.40

 30 1.38

 25
 1.36

 Count
 20
 1.34
 15
 1.32
 10
 1.30
 5

 0 128

 GO:0000209
 GO:0001817
 GO:0001818
 GO:0001932
 GO:0001934
 GO:0002698
 GO:0006464
 GO:0006511
 GO:0006897
 GO:0006898
 GO:0006909
 GO:0006911
 GO:0006950
 GO:0007154
 GO:0007165
 GO:0007166
 GO:0007167
 GO:0007178
 GO:0007179
 GO:0007346
 GO:0009605
 GO:0009719
 GO:0009987
 GO:0010256
 GO:0010714
 GO:0016073
 GO:0016477
 GO:0019220
 GO:0019835
 –log10p (value)

 Figure 13: Corn-human biological process information statistics map (total of 86 entries showing only partial information).

 Figure 14: Corn-human-protein interaction network.

 cell adh transmembrane transport

 4 Lignin batabolism
 Pollination
 Seed dormancy process
 2 Meristem maintenance
 Vegetative to reproductive phase transition of meristem

 Animal organ morphogenesis
 0
 Protein phosphorylation Leaf development

 Cell differentiation
 –2 Determination of dorsal identity
 Embryonic pattern specification
 –4
 Pollen sperm cell differentiation
 Adaxial/abaxial pattern specification
 –6 Actin nucleation Positive regulation of development, heterochronic

 Negative regulation of growth
 –8
 Positive regulation of programmed cell death

 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7
 Figure 15: Soybean-soybean GO semantic analysis diagram.

10 Journal of Healthcare Engineering

 5
 Regulation of mitotic cell cycle
 4

 3 Protein ubiquitination

 2
 Cellular protein modification process
 1
 Cellular protein metabolism
 0

 –1

 –2

 –3 Macromolecule modification
 Cellular response to external stimulas

 –5 –4 –3 –2 –1 0 1 2 3
 Figure 16: Semantic analysis diagram of soybean-human GO.

 Tissue development Flower development

 6 Cellular aromatic compound metabolism
 Cellular nitrogen compound metabolism
 Postembryonic development
 Reproductive system development Cellular biosynthesis
 4 Postembryonic animal orgen development
 Cellular nitrogen compound biosynthesis
 Cellular macromolecule biosynthesis
 2 Cellular macromolecule metabolism
 Nucleic acid metabolism
 RNA metabolism
 0 DNA metabolism
 Cellular protein metabolism

 Response to stress
 –2

 –4
 Protein import
 Organic substance transport
 Negative regulation of biological process
 –6 Cellular localization

 Intracellular transport

 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7
 Figure 17: Semantic analyses of rice GO.

cell protein metabolism process, positive regulation of functions, such as regulation of multicellular tissue
phosphoric acid metabolism process, and organic nitro- processes and regulation of cytokine production (see
gen compound metabolism process. It can regulate cell Figure 20).

Journal of Healthcare Engineering 11

 Cellular response to external stimulus
 Response to external stimulus
 6

 4
 Cellular response to stress

 NIK/NF-kappaB signaling
 2 Protein phosphorylation
 Regulation of signal transduction
 Phosphorus metabolism

 0 Regulation of cell communcation
 Regulation of cellular process
 Regulation of mititic cell cycle
 –2 Regulation of cellular component organization

 –4

 Lateral ventricle development
 –6 System development
 Neuroblast division in subventricular zone

 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5
 Figure 18: Semantic analysis diagram of rice-human GO.

 7
 Cellular aromatic compound metabolism
 Regulation of cellular process
 6
 Regulation of gene expression
 5 RNA biosynthesis

 4
 3 RNA metabolism

 Regulation of metabolism
 2
 Lignin catabolism
 1
 0
 –1
 Organic cyclic compound metabolism
 Biological regulation
 –2
 –3
 –4
 Developmental process
 –5

 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7
 Figure 19: Semantic analysis diagram of corn-maize GO.

12 Journal of Healthcare Engineering

 Regulstion of cytokine production
 Data Availability
 Regulstion of multicellular organismal process Secretion by cell
 6 Regulstion of phosphate metabolism
 Secretion All personnel can access the data that include plant miRNA
 Regulstion of multiorganism process
 and mRNA information and related processing procedures.
 Regulstion of biological process
 4 Regulstion of catalytic activity The data download address is https://pan.baidu.com/s/
 1YlOzS4LsWEmQNuTln7yemA with code ‘5b2e’.
 2
 Conflicts of Interest
 0 Cell surface receptor signaling pathway
 The authors declare that there are no conflicts of interest
 regarding the publication of this paper.
 –2 NIK/NF-kappaB signaling

 Acknowledgments
 –4
 Response to stress
 This study was financed by the Key Research and Devel-
 Response to virus
 –6 Response to endogenous stimulus
 opment Projects of China’s Jilin Province Science and
 Technology Development Plan (no. 20200401078GX).
 Response to externsl stimulus Response to laminar fluid shear stress
 –8 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5
 References
 Figure 20: Semantic analysis diagram of corn-human GO. [1] V. N. Kim and V. Narry, “MicroRNA biogenesis: coordinated
 cropping and dicing,” Nature Reviews Molecular Cell Biology,
 vol. 6, no. 5, pp. 376–385, 2005.
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