KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization

Zhang, Shengli; Qiao, Huijuan

doi:10.1016/j.ab.2020.113995

Cited by 18 publications

(3 citation statements)

References 77 publications

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“…To observe the accuracy and robustness of the model, cross-validation is considered the most efficient method, which consists of jackknife test, independent dataset test and k-fold cross-validation [32][33][34][35][36][37][38][39][40]. In this work, 10-fold cross-validation is used to validate the baseline dataset, and the where T P , T N , F P and F N represent the number of true positive, true negative, false positive, and false negative, respectively.…”

Section: Model Evaluationmentioning

confidence: 99%

Fer-COCL: A Novel Method Based on Multiple Deep Learning Algorithms for Identifying Fertility-Related Proteins

Zhang¹,

Li²,

Shi³

et al. 2023

MATCH

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The survival of species depends on the fertility of organisms. It is also worthwhile to study the proteins that can regulate the reproductive activity of organisms. Since biological experiments are laborious to confirm proteins, it has become a priority that develop relevant computational models to predict the function of fertility-related proteins. With the development of machine learning, pertinent various algorithms can be the key to identifying fertility-related proteins. In this work, we develop a model Fer-COCL based on deep learning. The model consists of multiple features as well as multiple deep learning algorithms. First, we extract features using Amino acid composition (AAC), Dipeptide composition (DPC), CTD transition (CTDT) and deviation between the dipeptide and the expected mean (DDE). After that, the spliced features are fed into the classifier. The data processed jointly by convolutional neural network and long short-term memory is input to the fully connected layer for classification. After evaluating the model using 10-fold cross-validation, the accuracy of the two data sets reaches 97.1% and 98.3%, respectively. The results indicate that the model is efficient and accurate, facilitating biologists' research on biological fertility. In addition, a free online tool for predicting the function of fertility-related proteins is available at http://fercocl.zhanglab.site/.

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Section: Model Evaluationmentioning

confidence: 99%

Fer-COCL: A Novel Method Based on Multiple Deep Learning Algorithms for Identifying Fertility-Related Proteins

Zhang¹,

Li²,

Shi³

et al. 2023

MATCH

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“…Considering that sequence descriptors introduce significant bias and irrelevant features as well as generating encoding, the use of feature selection approaches soon became a frontier in the development of robust lncRNA sub-cellular localization prediction approaches. In this regard, Zhang et al [207] developed a machine learning methodology "KD-KLNMF" for accurate determination of lncRNA sub-cellular localization. They utilized a data augmentation approach to balance the imbalance dataset.…”

Section: Long Non-coding Rna Sub-cellular Localizationmentioning

confidence: 99%

“…Turning towards the second part of Figure 4, computational predictors including lncLocPred [209], iLoc-LncRNA [205], Locate-R [208], and KD-KLNMF [207] are evaluated on benchmark datasets annotated against four distinct sub-cellular locations such as Nucleus, Cytoplasm, Ribosome, and Exosome. Among all four approaches, the performance of two computational predictors lncLocPred [209] and KD-KLNMF [207] is additionally analyzed on the independent test set as well. For each dataset, the number of sequences against four different sub-cellular locations is depicted in the bar graph (Figure 4).…”

Section: Benchmark Sub Cellular Localization Datasetsmentioning

confidence: 99%

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Asim

Ibrahim

Malik

et al. 2021

IJMS

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Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

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iAHTP-LH: Integrating Low-Order and High-Order Correlation Information for Identifying Antihypertensive Peptides

Zou

2022

Int J Pept Res Ther

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KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization

Cited by 18 publications

References 77 publications

Fer-COCL: A Novel Method Based on Multiple Deep Learning Algorithms for Identifying Fertility-Related Proteins

Fer-COCL: A Novel Method Based on Multiple Deep Learning Algorithms for Identifying Fertility-Related Proteins

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

iAHTP-LH: Integrating Low-Order and High-Order Correlation Information for Identifying Antihypertensive Peptides

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