scholarly journals Study of miRNA – mRNA interactions related with cancer

2013 ◽  
Author(s):  
Νέστορας Καραθανάσης
Keyword(s):  
Target Prediction ◽  
Large Family ◽  
Prediction Algorithm ◽  
Normal Tissues ◽  
Mrna Targets ◽  
Non Coding Rnas ◽  
Cancer Phenotypes ◽  
Biology Lab ◽  
Target Prediction Algorithm ◽  
Target Cancer

MicroRNAs belong to the large family of small non coding RNAs. They regulateprotein synthesis by binding to their mRNA targets causing mRNA degradationor translational repression. A large number of miRNAs have been associated withcancer because they are often found to be located within cancer associatedgenomic region (CAGRs/FRA) to target cancer-related genes, and to bedifferentially expressed in tumor compared to normal tissues. Previous work inthe Computational Biology lab had identified four new putative miRNA genesthat were located within CAGR. However their mature molecules and theirassociation with cancer phenotypes were unknown. My thesis focuses onresolving these two issues, using a combination of theoretical and experimentaltechniques. The specific aims of this work are: The development of a mature miRNA prediction algorithm(Chapter II, III) The identification of the mature miRNA molecules of the newly identifiedmiRNA genes via a combination of computational and experimentalmethods (Chapter IV) The utilization of a target prediction algorithm to predict andexperimentally verify interactions between the mature molecules andcancer-related genes Chapter IV).

scholarly journals Use of Deep Multi-Target Prediction to Identify Learning Styles

2020 ◽  
Vol 10 (5) ◽  
pp. 1756 ◽  
Author(s):  
Everton Gomede ◽  
Rodolfo Miranda de Barros ◽  
Leonardo de Souza Mendes
Keyword(s):  
Learning Styles ◽  
Target Prediction ◽  
Learning Processes ◽  
Learning Systems ◽  
Prediction Algorithm ◽  
Traditional Learning ◽  
Multiple Target ◽  
Learning Evaluation ◽  
E Learning ◽  
Target Prediction Algorithm

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS

2010 ◽  
Vol 08 (04) ◽  
pp. 763-788 ◽  
Author(s):  
YUN ZHENG ◽  
WEIXIONG ZHANG
Keyword(s):  
Drosophila Melanogaster ◽  
Protein Level ◽  
Mirna Target ◽  
Homo Sapiens ◽  
Target Prediction ◽  
Prediction Algorithm ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Microrna Target Prediction ◽  
Target Prediction Algorithm

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.

Research on target prediction algorithm of shipboard photoelectric tracking equipment

2017 ◽  
Vol 25 (2) ◽  
pp. 519-528 ◽  
Author(s):  
周俊鹏 ZHOU Jun-peng ◽  
陈 健 CHEN Jian ◽  
李 焱 LI Yan ◽  
董宇星 DONG Yu-xing ◽  
陈 娟 CHEN Juan ◽  
...  
Keyword(s):  
Target Prediction ◽  
Prediction Algorithm ◽  
Target Prediction Algorithm

MicroRNAs as Biomarker for Breast Cancer

2020 ◽  
Vol 20 (10) ◽  
pp. 1597-1610 ◽  
Author(s):  
Taru Aggarwal ◽  
Ridhima Wadhwa ◽  
Riya Gupta ◽  
Keshav Raj Paudel ◽  
Trudi Collet ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Association Studies ◽  
Large Family ◽  
Breast Cancer Diagnosis ◽  
Cell Multiplication ◽  
Cell Physiology ◽  
Gene Transcript ◽  
Genome Wide Association Studies ◽  
Non Coding Rnas

Regardless of advances in detection and treatment, breast cancer affects about 1.5 million women all over the world. Since the last decade, genome-wide association studies (GWAS) have been extensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosis and therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation of key cellular processes such as cell multiplication, differentiation, and death. They cause a disturbance in the cell physiology by interfering directly with the translation and stability of a targeted gene transcript. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate target gene expression and protein levels that affect several human diseases and are suggested as the novel markers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not only associated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis or prognosis. These are explained in detail in the following review. This review will also provide an impetus to study the role of microRNAs in breast cancer.

scholarly journals The Long Non-Coding RNA H19 Drives the Proliferation of Diffuse Intrinsic Pontine Glioma with H3K27 Mutation

2021 ◽  
Vol 22 (17) ◽  
pp. 9165
Author(s):  
David Roig-Carles ◽  
Holly Jackson ◽  
Katie F. Loveson ◽  
Alan Mackay ◽  
Rebecca L. Mather ◽  
...  
Keyword(s):  
Large Family ◽  
Diffuse Intrinsic Pontine Glioma ◽  
Locked Nucleic Acid ◽  
Normal Brain ◽  
Pontine Glioma ◽  
Incurable Cancer ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Diffuse intrinsic pontine glioma (DIPG) is an incurable paediatric malignancy. Identifying the molecular drivers of DIPG progression is of the utmost importance. Long non-coding RNAs (lncRNAs) represent a large family of disease- and tissue-specific transcripts, whose functions have not yet been elucidated in DIPG. Herein, we studied the oncogenic role of the development-associated H19 lncRNA in DIPG. Bioinformatic analyses of clinical datasets were used to measure the expression of H19 lncRNA in paediatric high-grade gliomas (pedHGGs). The expression and sub-cellular location of H19 lncRNA were validated in DIPG cell lines. Locked nucleic acid antisense oligonucleotides were designed to test the function of H19 in DIPG cells. We found that H19 expression was higher in DIPG vs. normal brain tissue and other pedHGGs. H19 knockdown resulted in decreased cell proliferation and survival in DIPG cells. Mechanistically, H19 buffers let-7 microRNAs, resulting in the up-regulation of oncogenic let-7 target (e.g., SULF2 and OSMR). H19 is the first functionally characterized lncRNA in DIPG and a promising therapeutic candidate for treating this incurable cancer.

scholarly journals Genetic variants in miR-146a, miR-149, miR-196a2, miR-499 and their influence on relative expression in lung cancers

2011 ◽  
Vol 49 (12) ◽  
Author(s):  
Serena Vinci ◽  
Stefania Gelmini ◽  
Nicola Pratesi ◽  
Simona Conti ◽  
Francesca Malentacchi ◽  
...  
Keyword(s):  
Small Cell Lung Carcinoma ◽  
Sequence Variants ◽  
Genotype Distribution ◽  
Nucleotide Polymorphisms ◽  
Cell Lung Carcinoma ◽  
Lung Cancers ◽  
Relative Expression ◽  
Normal Tissues ◽  
Mrna Targets ◽  
Paired Samples

AbstractThe presence of sequence variants in miRNA genes may influence their processing, expression and binding to target mRNAs. Since single miRNA can have a large number of potential mRNA targets, even minor variations in its expression can have influences on hundreds of putative mRNAs.Here, we evaluated 101 paired samples (cancer and normal tissues) from non-small cell lung carcinoma (NSCLC) patients to study the genotype distribution of single nucleotide polymorphisms (SNPs) inRelative expression ofOur results seem to demonstrate that sequence variants of

Sign in / Sign up

Export Citation Format

Share Document