Identification of long noncoding RNAs with machine learning methods: a review

2021 ◽  
Author(s):  
Lei Xu ◽  
Shihu Jiao ◽  
Dandan Zhang ◽  
Song Wu ◽  
Haihong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Noncoding Rnas ◽  
Experimental Methods ◽  
Long Noncoding Rnas ◽  
Biological Functions ◽  
Prediction Tools ◽  
Machine Learning Methods ◽  
High Resource ◽  
Future Work

Abstract Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.

Recent Advances on the Machine Learning Methods in Identifying Phage Virion Proteins

2020 ◽  
Vol 15 (7) ◽  
pp. 657-661
Author(s):  
Yingjuan Yang ◽  
Chunlong Fan ◽  
Qi Zhao
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Experimental Methods ◽  
Host Cells ◽  
Learning Methods ◽  
Research Directions ◽  
Antibacterial Drugs ◽  
Machine Learning Methods ◽  
Recent Advances

In the field of bioinformatics, the prediction of phage virion proteins helps us understand the interaction between phage and its host cells and promotes the development of new antibacterial drugs. However, traditional experimental methods to identify phage virion proteins are expensive and inefficient, more researchers are working to develop new computational methods. In this review, we summarized the machine learning methods for predicting phage virion proteins during recent years, and briefly described their advantages and limitations. Finally, some research directions related to phage virion proteins are listed.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

2020 ◽  
Vol 26 (26) ◽  
pp. 3049-3058
Author(s):  
Ting Liu ◽  
Hua Tang
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Future Development ◽  
Malaria Parasite ◽  
Mitochondrial Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Effective Drugs ◽  
Construction Strategies ◽  
Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

scholarly journals Biological functions and clinical significance of long noncoding RNAs in bladder cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yan Zhang ◽  
Xianwu Chen ◽  
Juntao Lin ◽  
Xiaodong Jin
Keyword(s):  
Bladder Cancer ◽  
Clinical Significance ◽  
Noncoding Rna ◽  
Epithelial Mesenchymal Transition ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Research Progress ◽  
High Morbidity ◽  
Biological Functions ◽  
Mesenchymal Transition

AbstractBladder cancer (BCa) is one of the 10 most common cancers with high morbidity and mortality worldwide. Long noncoding RNAs (lncRNAs), a large class of noncoding RNA transcripts, consist of more than 200 nucleotides and play a significant role in the regulation of molecular interactions and cellular pathways during the occurrence and development of various cancers. In recent years, with the rapid advancement of high-throughput gene sequencing technology, several differentially expressed lncRNAs have been discovered in BCa, and their functions have been proven to have an impact on BCa development, such as cell growth and proliferation, metastasis, epithelial-mesenchymal transition (EMT), angiogenesis, and drug-resistance. Furthermore, evidence suggests that lncRNAs are significantly associated with BCa patients’ clinicopathological characteristics, especially tumor grade, TNM stage, and clinical progression stage. In addition, lncRNAs have the potential to more accurately predict BCa patient prognosis, suggesting their potential as diagnostic and prognostic biomarkers for BCa patients in the future. In this review, we briefly summarize and discuss recent research progress on BCa-associated lncRNAs, while focusing on their biological functions and mechanisms, clinical significance, and targeted therapy in BCa oncogenesis and malignant progression.

scholarly journals Real-time prediction of inpatient length of stay for discharge prioritization

2015 ◽  
Vol 23 (e1) ◽  
pp. e2-e10 ◽  
Author(s):  
Sean Barnes ◽  
Eric Hamrock ◽  
Matthew Toerper ◽  
Sauleh Siddiqui ◽  
Scott Levin
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Health Information ◽  
Patient Flow ◽  
Supervised Machine Learning ◽  
Youden Index ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Resource ◽  
Sensitivity Specificity

Abstract Objective Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. Materials and Methods The authors use supervised machine learning methods to predict patients’ likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden’s Index (i.e., sensitivity + specificity – 1), and aggregate accuracy measures. Results The model compared to clinician predictions demonstrated significantly higher sensitivity ( P  < .01), lower specificity ( P  < .01), and a comparable Youden Index ( P  > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. Conclusions There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

scholarly journals Long noncoding RNAs in intestinal epithelium homeostasis

2019 ◽  
Vol 317 (1) ◽  
pp. C93-C100 ◽  
Author(s):  
Lan Xiao ◽  
Myriam Gorospe ◽  
Jian-Ying Wang
Keyword(s):  
Intestinal Epithelium ◽  
Rna Binding ◽  
Noncoding Rnas ◽  
The Body ◽  
Long Noncoding Rnas ◽  
Biological Functions ◽  
Mucosal Regeneration ◽  
Epithelial Barrier Function ◽  
Epithelial Homeostasis ◽  
Stressful Environments

The epithelium of the mammalian intestinal mucosa is a rapidly self-renewing tissue in the body, and its homeostasis is preserved through well-controlled mechanisms. Long noncoding RNAs (lncRNAs) regulate a variety of biological functions and are intimately involved in the pathogenesis of diverse human diseases. Here we highlight the roles of several lncRNAs expressed in the intestinal epithelium, including uc.173, SPRY4-IT1, H19, and Gata6, in maintaining the integrity of the intestinal epithelium, focusing on the emerging evidence of lncRNAs in the regulation of intestinal mucosal regeneration and epithelial barrier function. We also discuss recent results that the interactions between lncRNAs with microRNAs and the RNA-binding protein HuR influence epithelial homeostasis. With rapidly advancing knowledge of lncRNAs, there is also growing recognition that lncRNAs in the intestinal epithelium might be promising therapeutic targets in our efforts to protect the integrity of the intestinal epithelium in response to stressful environments.

scholarly journals Long Noncoding RNAs as Novel Biomarkers Have a Promising Future in Cancer Diagnostics

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ting Shi ◽  
Ge Gao ◽  
Yingli Cao
Keyword(s):  
Noncoding Rnas ◽  
High Sensitivity ◽  
High Specificity ◽  
Body Fluids ◽  
Cancer Diagnostics ◽  
Long Noncoding Rnas ◽  
Detection Methods ◽  
Tumor Biomarkers ◽  
Biological Functions ◽  
Sample Collection

Cancers have a high mortality rate due to lack of suitable specific early diagnosis tumor biomarkers. Emerging evidence is accumulating that lncRNAs (long noncoding RNAs) are involved in tumorigenesis, tumor cells proliferation, invasion, migration, apoptosis, and angiogenesis. Furthermore, extracellular lncRNAs can circulate in body fluids; they can be detected and strongly resist RNases. Many researchers have found that lncRNAs could be good candidates for tumor biomarkers and possessed high specificity, high sensitivity, and noninvasive characteristics. In this review, we summarize the detection methods and possible sources of circulating lncRNAs and outline the biological functions and expression level of the most significant lncRNAs in tissues, cell lines, and body fluids (whole blood, plasma, urine, gastric juice, and saliva) of different kinds of tumors. We evaluate the diagnostic performance of lncRNAs as tumor biomarkers. However, the biological functions and the mechanisms of circulating lncRNAs secretion have not been fully understood. The uniform normalization protocol of sample collection, lncRNAs extraction, endogenous control selection, quality assessment, and quantitative data analysis has not been established. Therefore, we put forward some recommendations that might be investigated in the future if we want to adopt lncRNAs in clinical practice.

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