lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information

Fan, Yongxian; Chen, Meijun; Zhu, Qingqi

doi:10.1109/access.2020.3007317

Cited by 16 publications

(14 citation statements)

References 72 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We analyze this issue in detail and address it by proposing a lightweight and multiscale architecture PyConvU-Net which replaces the traditional convolution layer with the pyramidal convolution layer. This network which can extract multiple sequence feature information [ 28 ] not only achieves improvements in the biomedical image segmentation tasks [ 29 ] but also reduces the number of parameters.…”

Section: Discussionmentioning

confidence: 99%

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

Fan

Cai

2021

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

show abstract

Section: Discussionmentioning

confidence: 99%

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

Fan

Cai

2021

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Likewise, Fan et al [209] developed a machine learning based methodology "lncLocPred" to accurately determine the sub-cellular localization of lncRNAs. They utilized three different sequence descriptors including K-mer, Pseudo Dinucleotide Composition (PseDNC), and Local Structure-Sequence Triplet Element to represent lncRNA sequences.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Non-coding RNAs (ncRNAs) are classified into different categories according to their length, function, and subcellular location, and the ncRNAs that have been widely studied by the biological community include long noncoding RNAs (lncRNAs), small nucleolar RNAs (snoRNAs) and microRNAs (miRNAs) [11]. For lncRNAs, Fan et al devised lncLocPred, a logistic regression-based machine learning predictor dedicated to predicting the subcellular localization of lncRNAs [12]. Addressing the challenge of limited samples in lncRNA subcellular localization, Cai et al presented GM-lncLoc, a meta-learning training model facilitating knowledge transfer through meta-parameters [13].…”

Section: Introductionmentioning

confidence: 99%

GP-HTNLoc: A Graph Prototype Head-Tail Network-based Model for Multi-label Subcellular Localization Prediction of ncRNAs

Han,

Liu

2024

Preprint

View full text Add to dashboard Cite

Numerous research findings demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. This study addresses the limitations of existing models by introducing a novel multi-label localization prediction model for ncRNAs, termed GP-HTNLoc. To alleviate class imbalance, the model adopts a separate training approach for head and tail class labels. In GP-HTNLoc, a pioneering graph prototype module is introduced for capturing potential association of ncRNA samples with labels. This module efficiently learns the graph structure and aggregates sample features. Notably, only few samples are required to obtain label prototypes containing rich information. These prototypes are then utilized to train a transfer learner, facilitating the transfer of meta-knowledge from the head class to the tail class. Experimental results demonstrate that GP-HTNLoc surpasses current state-of-the-art models across all datasets. Ablation study underscore the vital role played by the graph prototype module in enhancing the performance of GP-HTNLoc. The user-friendly online GP-HTNLoc web server can be accessed at https://56s8y85390.goho.co.

show abstract

lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information

Cited by 16 publications

References 72 publications

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

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

GP-HTNLoc: A Graph Prototype Head-Tail Network-based Model for Multi-label Subcellular Localization Prediction of ncRNAs

Contact Info

Product

Resources

About