PMID- 36850816
OWN - NLM
STAT- MEDLINE
DCOM- 20230307
LR  - 20230307
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 4
DP  - 2023 Feb 16
TI  - An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based 
      on Artificial Gorilla Troops Algorithm.
LID - 10.3390/s23042219 [doi]
LID - 2219
AB  - MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective 
      alteration might result in abnormal gene manifestation in the downstream pathway 
      of their target. miRNA gene variants can impact miRNA transcription, maturation, 
      or target selectivity, impairing their usefulness in plant growth and stress 
      responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced 
      functional marker that has recently been used in plant breeding. MicroRNA and 
      long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play 
      a vital role in controlling the biological processes of animals and plants. 
      According to recent studies, the major objective for decoding their functional 
      activities is predicting the relationship between lncRNA and miRNA. Traditional 
      feature-based classification systems' prediction accuracy and reliability are 
      frequently harmed because of the small data size, human factors' limits, and huge 
      quantity of noise. This paper proposes an optimized deep learning model built 
      with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural 
      Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. 
      The deep learning ensemble model automatically investigates the function 
      characteristics of genetic sequences. The proposed model's main advantage is the 
      enhanced accuracy in plant miRNA-IncRNA prediction due to optimal hyperparameter 
      tuning, which is performed by the artificial Gorilla Troops Algorithm and the 
      proposed intelligent preying algorithm. IndRNN is adapted to derive the 
      representation of learned sequence dependencies and sequence features by 
      overcoming the inaccuracies of natural factors in traditional feature 
      architecture. Working with large-scale data, the suggested model outperforms the 
      current deep learning model and shallow machine learning, notably for extended 
      sequences, according to the findings of the experiments, where we obtained an 
      accuracy of 97.7% in the proposed method.
FAU - Hamdy, Walid
AU  - Hamdy W
AD  - Faculty of Science, Port Said University, Port Said 42511, Egypt.
FAU - Ismail, Amr
AU  - Ismail A
AD  - Faculty of Science, Port Said University, Port Said 42511, Egypt.
FAU - Awad, Wael A
AU  - Awad WA
AD  - Faculty of Computers and Artificial Intelligence, Damietta University, El-Gadeeda 
      34519, Egypt.
FAU - Ibrahim, Ali H
AU  - Ibrahim AH
AD  - Faculty of Science, Port Said University, Port Said 42511, Egypt.
FAU - Hassanien, Aboul Ella
AU  - Hassanien AE
AUID- ORCID: 0000-0002-9989-6681
AD  - Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, 
      Egypt.
LA  - eng
PT  - Journal Article
DEP - 20230216
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
RN  - 0 (MicroRNAs)
RN  - 0 (RNA, Long Noncoding)
SB  - IM
MH  - Animals
MH  - Humans
MH  - Algorithms
MH  - *Deep Learning
MH  - *MicroRNAs/genetics
MH  - Reproducibility of Results
MH  - *RNA, Long Noncoding/genetics
MH  - *Plant Physiological Phenomena/genetics
PMC - PMC9964106
OTO - NOTNLM
OT  - CNN model
OT  - IndRNN
OT  - deep learning
OT  - ensemble learning
OT  - lncRNA
OT  - microRNA
OT  - plant
COIS- The authors declare that they have no conflicts of interest to report regarding 
      the present study.
EDAT- 2023/03/01 06:00
MHDA- 2023/03/03 06:00
PMCR- 2023/02/16
CRDT- 2023/02/28 01:23
PHST- 2023/01/21 00:00 [received]
PHST- 2023/02/11 00:00 [revised]
PHST- 2023/02/14 00:00 [accepted]
PHST- 2023/02/28 01:23 [entrez]
PHST- 2023/03/01 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
PHST- 2023/02/16 00:00 [pmc-release]
AID - s23042219 [pii]
AID - sensors-23-02219 [pii]
AID - 10.3390/s23042219 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Feb 16;23(4):2219. doi: 10.3390/s23042219.