Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

scholarly journals MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

2019 ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
Hai-Cheng Yi ◽  
Kai Zheng ◽  
Yan-Bin Wang
Keyword(s):  
Computational Models ◽  
Prediction Models ◽  
Graph Embedding ◽  
Computational Prediction ◽  
Additional Information ◽  
Great Hope ◽  
Node Classification ◽  
Relationship Network ◽  
The Relationship ◽  
Mesh Tree

AbstractMotivationEffectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify.ResultsIn this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe- and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network [email protected]

scholarly journals HOTAIR: An Oncogenic Long Non-Coding RNA in Human Cancer

2018 ◽  
Vol 47 (3) ◽  
pp. 893-913 ◽  
Author(s):  
Qing Tang ◽  
Swei Sunny Hann
Keyword(s):  
Drug Resistance ◽  
Human Cancer ◽  
Critical Role ◽  
Stem Cell Differentiation ◽  
Human Diseases ◽  
Biological Functions ◽  
Protein Coding ◽  
Non Coding Rna ◽  
And Function ◽  
Long Non Coding Rna

Long non-coding RNAs (LncRNAs) represent a novel class of noncoding RNAs that are longer than 200 nucleotides without protein-coding potential and function as novel master regulators in various human diseases, including cancer. Accumulating evidence shows that lncRNAs are dysregulated and implicated in various aspects of cellular homeostasis, such as proliferation, apoptosis, mobility, invasion, metastasis, chromatin remodeling, gene transcription, and post-transcriptional processing. However, the mechanisms by which lncRNAs regulate various biological functions in human diseases have yet to be determined. HOX antisense intergenic RNA (HOTAIR) is a recently discovered lncRNA and plays a critical role in various areas of cancer, such as proliferation, survival, migration, drug resistance, and genomic stability. In this review, we briefly introduce the concept, identification, and biological functions of HOTAIR. We then describe the involvement of HOTAIR that has been associated with tumorigenesis, growth, invasion, cancer stem cell differentiation, metastasis, and drug resistance in cancer. We also discuss emerging insights into the role of HOTAIR as potential biomarkers and therapeutic targets for novel treatment paradigms in cancer.

scholarly journals Human Endometrial Carcinogenesis Is Associated with Significant Reduction in Long Non-Coding RNA, TERRA

2020 ◽  
Vol 21 (22) ◽  
pp. 8686
Author(s):  
Meera Adishesh ◽  
Rafah Alnafakh ◽  
Duncan M. Baird ◽  
Rhiannon E. Jones ◽  
Shannon Simon ◽  
...  
Keyword(s):  
Cellular Proliferation ◽  
Telomerase Activity ◽  
Human Endometrium ◽  
Future Research ◽  
Healthy Human ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Length Analysis ◽  
Regulatory Functions ◽  
Long Non Coding Rna

Telomeres are transcribed as long non-coding RNAs called TERRAs (Telomeric repeat containing RNA) that participate in a variety of cellular regulatory functions. High telomerase activity (TA) is associated with endometrial cancer (EC). This study aimed to examine the levels of three TERRAs, transcribed at chromosomes 1q-2q-4q-10q-13q-22q, 16p and 20q in healthy (n = 23) and pathological (n = 24) human endometrium and to examine their association with cellular proliferation, TA and telomere lengths. EC samples demonstrated significantly reduced levels of TERRAs for Chromosome 16p (Ch-16p) (p < 0.002) and Chromosome 20q (Ch-20q) (p = 0.0006), when compared with the postmenopausal samples. No significant correlation was found between TERRA levels and TA but both Ch-16p and Ch-20q TERRA levels negatively correlated with the proliferative marker Ki67 (r = −0.35, p = 0.03 and r = −0.42, p = 0.01 respectively). Evaluation of single telomere length analysis (STELA) at XpYp telomeres demonstrated a significant shortening in EC samples when compared with healthy tissues (p = 0.002). We detected TERRAs in healthy human endometrium and observed altered individual TERRA-specific levels in malignant endometrium. The negative correlation of TERRAs with cellular proliferation along with their significant reduction in EC may suggest a role for TERRAs in carcinogenesis and thus future research should explore TERRAs as potential therapeutic targets in EC.

Recent advances in predicting protein-lncRNA interactions using machine learning methods

2021 ◽  
Vol 21 ◽  
Author(s):  
Han Yu ◽  
Zi-Ang Shen ◽  
Yuan-Ke Zhou ◽  
Pu-Feng Du
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Protein Interactions ◽  
Cellular Level ◽  
Future Research ◽  
Biological Processes ◽  
Learning Methods ◽  
Protein Coding ◽  
Machine Learning Methods ◽  
Non Coding Rnas

: Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

scholarly journals CROSSalive: a web server for predicting the in vivo structure of RNA molecules

2019 ◽  
Author(s):  
Riccardo Delli Ponti ◽  
Alexandros Armaos ◽  
Andrea Vandelli ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Cross Validation ◽  
Supplementary Information ◽  
Supplementary Data ◽  
High Confidence ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Motivation RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. Results Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. Availability and implementation CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm

2020 ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
De-Shuang Huang ◽  
Hai-Cheng Yi ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Computational Models ◽  
Prediction Models ◽  
Graph Embedding ◽  
Computational Prediction ◽  
Medical Subject Headings ◽  
Additional Information ◽  
Great Hope ◽  
Node Classification ◽  
Relationship Network ◽  
The Relationship

Abstract Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.

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