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Article ; Online: Computational Detection of Pre-microRNAs.

Methods in molecular biology (Clifton, N.J.)

2021 Volume 2257, Page(s) 167–174

Abstract: MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for ... ...

Abstract	MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed.
MeSH term(s)	Computational Biology ; Machine Learning ; MicroRNAs/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2021-08-25
Publishing country	United States
Document type	Journal Article ; Review
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1170-8_8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computational prediction of microRNAs in Histoplasma capsulatum.

Saçar Demirci, Müşerref Duygu

Microbial pathogenesis

2020 Volume 148, Page(s) 104433

Abstract: MicroRNAs (miRNAs) are small and non-coding RNAs that regulate gene expression through post-transcriptional regulation. Although, the standard miRNA repository, MiRBase, lists more than 200 organisms having miRNA mediated regulation mechanism and ... ...

Abstract	MicroRNAs (miRNAs) are small and non-coding RNAs that regulate gene expression through post-transcriptional regulation. Although, the standard miRNA repository, MiRBase, lists more than 200 organisms having miRNA mediated regulation mechanism and thousands of miRNAs, there is not enough information about miRNAs of fungal species. Considering that there are various fungal pathogens causing disease phenotypes, it is important to search for miRNAs of those organisms. The leading cause of endemic mycosis in the USA is a fungal disease known as histoplasmosis, which is resulted by infection with a fungal intracellular parasite, Histoplasma capsulatum (H. capsulatum). In this work, genomes of H. capsulatum strains NAm1 and G217B were explored for potential miRNA like sequences and structures. Through a complex workflow involving miRNA detection and target prediction, several miRNA candidates of H. capsulatum and their possible targets in human were identified. The results presented here indicate that H. capsulatum might be one of the fungal pathogens having a miRNA based post-transcriptional gene regulation mechanism and it might have a miRNA mediated host - parasite interaction with human.
MeSH term(s)	Histoplasma/genetics ; Histoplasmosis ; Humans ; MicroRNAs/genetics ; RNA, Fungal/genetics
Chemical Substances	MicroRNAs ; RNA, Fungal
Language	English
Publishing date	2020-08-25
Publishing country	England
Document type	Journal Article
ZDB-ID	632772-2
ISSN	1096-1208 ; 0882-4010
ISSN (online)	1096-1208
ISSN	0882-4010
DOI	10.1016/j.micpath.2020.104433
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Article ; Online: NeRNA: A negative data generation framework for machine learning applications of noncoding RNAs.

Orhan, Mehmet Emin / Demirci, Yılmaz Mehmet / Saçar Demirci, Müşerref Duygu

Computers in biology and medicine

2023 Volume 159, Page(s) 106861

Abstract: Many supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them ... ...

Abstract	Many supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including microRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is performed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naïve Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.
MeSH term(s)	Bayes Theorem ; Machine Learning ; Algorithms ; RNA, Untranslated/genetics ; MicroRNAs/genetics ; RNA, Long Noncoding/genetics ; RNA, Circular
Chemical Substances	RNA, Untranslated ; MicroRNAs ; RNA, Long Noncoding ; RNA, Circular
Language	English
Publishing date	2023-04-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.106861
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Article ; Online: MicroRNA prediction based on 3D graphical representation of RNA secondary structures.

Saçar Demirci, Müşerref Duygu

Turkish journal of biology = Turk biyoloji dergisi

2019 Volume 43, Issue 4, Page(s) 274–280

Abstract: MicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an ... ...

Abstract	MicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naïve Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.
Language	English
Publishing date	2019-08-05
Publishing country	Turkey
Document type	Journal Article
ZDB-ID	2046470-8
ISSN	1303-6092 ; 1303-6092
ISSN (online)	1303-6092
ISSN	1303-6092
DOI	10.3906/biy-1904-59
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Article ; Online: Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.

Demirci, Yılmaz Mehmet / Saçar Demirci, Müşerref Duygu

Journal of integrative bioinformatics

2021 Volume 18, Issue 1, Page(s) 45–50

Abstract: Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 ... ...

Abstract	Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.
MeSH term(s)	COVID-19/diagnosis ; COVID-19/genetics ; COVID-19/virology ; Humans ; MicroRNAs/genetics ; RNA, Circular/genetics ; RNA, Messenger/genetics ; SARS-CoV-2/genetics ; SARS-CoV-2/pathogenicity
Chemical Substances	MicroRNAs ; RNA, Circular ; RNA, Messenger
Language	English
Publishing date	2021-03-17
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2147212-9
ISSN	1613-4516 ; 1613-4516
ISSN (online)	1613-4516
ISSN	1613-4516
DOI	10.1515/jib-2020-0047
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Article: Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection.

Saçar Demirci, Müşerref Duygu / Adan, Aysun

PeerJ

2020 Volume 8, Page(s) e9369

Abstract: MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA ... ...

Abstract	MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED12L, MED19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STAT1 were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF1ab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based therapies have been paid attention, based on the findings of this study, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics.
Keywords	covid19
Language	English
Publishing date	2020-06-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.9369
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Article ; Online: Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection

Müşerref Duygu Saçar Demirci / Aysun Adan

PeerJ, Vol 8, p e

2020 Volume 9369

Abstract	MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED12L, MED19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STAT1 were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF1ab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based ...
Keywords	SARS-CoV-2 ; MicroRNA ; COVID19 ; Host–virus interaction ; Medicine ; R ; Biology (General) ; QH301-705.5 ; covid19
Subject code	572
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	PeerJ Inc.
Document type	Article ; Online
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Article ; Online: Computational Prediction of Functional MicroRNA-mRNA Interactions.

Saçar Demirci, Müşerref Duygu / Yousef, Malik / Allmer, Jens

Methods in molecular biology (Clifton, N.J.)

2019 Volume 1912, Page(s) 175–196

Abstract: Proteins have a strong influence on the phenotype and their aberrant expression leads to diseases. MicroRNAs (miRNAs) are short RNA sequences which posttranscriptionally regulate protein expression. This regulation is driven by miRNAs acting as ... ...

Abstract	Proteins have a strong influence on the phenotype and their aberrant expression leads to diseases. MicroRNAs (miRNAs) are short RNA sequences which posttranscriptionally regulate protein expression. This regulation is driven by miRNAs acting as recognition sequences for their target mRNAs within a larger regulatory machinery. A miRNA can have many target mRNAs and an mRNA can be targeted by many miRNAs which makes it difficult to experimentally discover all miRNA-mRNA interactions. Therefore, computational methods have been developed for miRNA detection and miRNA target prediction. An abundance of available computational tools makes selection difficult. Additionally, interactions are not currently the focus of investigation although they more accurately define the regulation than pre-miRNA detection or target prediction could perform alone. We define an interaction including the miRNA source and the mRNA target. We present computational methods allowing the investigation of these interactions as well as how they can be used to extend regulatory pathways. Finally, we present a list of points that should be taken into account when investigating miRNA-mRNA interactions. In the future, this may lead to better understanding of functional interactions which may pave the way for disease marker discovery and design of miRNA-based drugs.
MeSH term(s)	Animals ; Computational Biology/instrumentation ; Computational Biology/methods ; Databases, Genetic ; Gene Expression Profiling/instrumentation ; Gene Expression Profiling/methods ; Gene Regulatory Networks ; High-Throughput Nucleotide Sequencing/instrumentation ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Machine Learning ; MicroRNAs/isolation & purification ; MicroRNAs/metabolism ; RNA, Messenger/isolation & purification ; RNA, Messenger/metabolism ; Sequence Analysis, RNA/instrumentation ; Sequence Analysis, RNA/methods ; Software
Chemical Substances	MicroRNAs ; RNA, Messenger
Language	English
Publishing date	2019-01-11
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-8982-9_7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for pre-microRNA Detection.

Demirci, Müşerref Duygu Saçar / Allmer, Jens

Journal of integrative bioinformatics

2017 Volume 14, Issue 2

Abstract: MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections ...

Abstract	MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.
MeSH term(s)	Datasets as Topic/standards ; Humans ; Machine Learning ; MicroRNAs/analysis ; MicroRNAs/genetics ; Registries
Chemical Substances	MicroRNAs
Language	English
Publishing date	2017-07-28
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2147212-9
ISSN	1613-4516 ; 1613-4516
ISSN (online)	1613-4516
ISSN	1613-4516
DOI	10.1515/jib-2017-0032
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection

Saçar Demirci, Müşerref Duygu / Adan, Aysun

PeerJ

2020 Volume 8, Page(s) e9369

Abstract	MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED12L, MED19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STAT1 were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF1ab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based therapies have been paid attention, based on the findings of this study, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics.
Keywords	General Biochemistry, Genetics and Molecular Biology ; General Neuroscience ; General Agricultural and Biological Sciences ; General Medicine ; covid19
Language	English
Publisher	PeerJ
Publishing country	us
Document type	Article ; Online
ZDB-ID	2703241-3
ISSN	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.9369
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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