PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article Citation - WoS: 3Citation - Scopus: 4Measuring the Performance of an Artificial Intelligence-Based Robot That Classifies Blood Tubes and Performs Quality Control in Terms of Preanalytical Errors: a Preliminary Study(Oxford University Press, 2024) Şişman,A.R.; Başok,B.I.; Karakoyun,I.; Çolak,A.; Bilge,U.; Demirci,F.; Başoglu,N.Objectives: Artificial intelligence-based robotic systems are increasingly used in medical laboratories. This study aimed to test the performance of KANKA (Labenko), a stand-alone, artificial intelligence-based robot that performs sorting and preanalytical quality control of blood tubes. Methods: KANKA is designed to perform preanalytical quality control with respect to error control and preanalytical sorting of blood tubes. To detect sorting errors and preanalytical inappropriateness within the routine work of the laboratory, a total of 1000 blood tubes were presented to the KANKA robot in 7 scenarios. These scenarios encompassed various days and runs, with 5 repetitions each, resulting in a total of 5000 instances of sorting and detection of preanalytical errors. As the gold standard, 2 experts working in the same laboratory identified and recorded the correct sorting and preanalytical errors. The success rate of KANKA was calculated for both the accurate tubes and those tubes with inappropriate identification. Results: KANKA achieved an overall accuracy rate of 99.98% and 100% in detecting tubes with preanalytical errors. It was found that KANKA can perform the control and sorting of 311 blood tubes per hour in terms of preanalytical errors. Conclusions: KANKA categorizes and records problem-free tubes according to laboratory subunits while identifying and classifying tubes with preanalytical inappropriateness into the correct error sections. As a blood acceptance and tube sorting system, KANKA has the potential to save labor and enhance the quality of the preanalytical process. © 2024 The Author(s).Article Citation - WoS: 11Citation - Scopus: 12Phytohormone Release by Three Isolated Lichen Mycobionts and the Effects of Indole-3 Acid on Their Compatible Photobionts(Springer, 2020) Candotto Carniel, Fabio; Muggia, Lucia; Ametrano, Claudio Gennaro; Kranner, Ilse; Çimen, Tuğçe; Pichler, Gregor; Stoggl, Wolfgang; Trippel, DanielaEvidence is emerging that phytohormones represent key inter-kingdom signalling compounds supporting chemical communication between plants, fungi and bacteria. The roles of phytohormones for the lichen symbiosis are poorly understood, particularly in the process of lichenization, i.e. the key events which lead free-living microalgae and fungi to recognize each other, make physical contact and start developing a lichen thallus. Here, we studied cellular and extracellularly released phytohormones in three lichen mycobionts, Cladonia grayi, Xanthoria parietina and Tephromela atra, grown on solid medium, and the effects of indole-3-acetic acid (IAA) on their respective photobionts, Asterochloris glomerata, Trebouxia decolorans, Trebouxia sp. Using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) we found that mycobionts produced IAA, salicylic acid (SA) and jasmonic acid (JA). IAA represented the most abundant phytohormone produced and released by all mycobionts, whereas SA was released by X. parietina and T. atra, and JA was released by C. grayi only. With a half-life of 5.2 days, IAA degraded exponentially in solid BBM in dim light. When IAA was exogenously offered to the mycobionts' compatible photobionts at "physiological" concentrations (as released by their respective mycobionts and accumulated in the medium over seven days), the photobionts' water contents increased up to 4.4%. Treatment with IAA had no effects on the maximum quantum yield of photosystem II, dry mass, and the contents of photosynthetic pigments and alpha-tocopherol of the photobionts. The data presented may be useful for designing studies aimed at elucidating the roles of phytohormones in lichens.Editorial Citation - WoS: 2Computational Mirnomics - Integrative Approaches(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2017) Hofestaedt, Ralf; Schreiber, Falk; Sommer, Bjoern; Allmer, JensWith this special issue on Computational miRNomics, we would like to start a new generation of publications in the Journal of Integrative Bioinformatics (JIB). From 2017 onwards, JIB will be published by De Gruyter which is one of the largest Open Access publishers in Germany with a long history. Established in 1918 with roots reaching even further back, the JIB editorial board decided that De Gruyter is the perfect partner to increase the level of professionalism for our publication processing and journal development.Article Citation - WoS: 4Citation - Scopus: 9Pgminer Reloaded, Fully Automated Proteogenomic Annotation Tool Linking Genomes To Proteomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Has, Canan; Lashin, Sergey A.; Kochetov, Alexey; Allmer, JensImprovements in genome sequencing technology increased the availability of full genomes and transcriptomes of many organisms. However, the major benefit of massive parallel sequencing is to better understand the organization and function of genes which then lead to understanding of phenotypes. In order to interpret genomic data with automated gene annotation studies, several tools are currently available. Even though the accuracy of computational gene annotation is increasing, a combination of multiple lines of experimental evidences should be gathered. Mass spectrometry allows the identification and sequencing of proteins as major gene products; and it is only these proteins that conclusively show whether a part of a genome is a coding region or not to result in phenotypes. Therefore, in the field of proteogenomics, the validation of computational methods is done by exploiting mass spectrometric data. As a result, identification of novel protein coding regions, validation of current gene models, and determination of upstream and downstream regions of genes can be achieved. In this paper, we present new functionality for our proteogenomic tool, PGMiner which performs all proteogenomic steps like acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, mapping confident peptides to gene models, and result visualization. The extensions cover determining proteotypic peptides and thus unambiguous protein identification. Furthermore, peptides conflicting with gene models can now automatically assessed within the context of predicted alternative open reading frames.Article Citation - WoS: 7Citation - Scopus: 5A Machine Learning Approach for Microrna Precursor Prediction in Retro-Transcribing Virus Genomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Saçar Demirci, Müşerref Duygu; Toprak, Mustafa; Allmer, JensIdentification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.Article Citation - WoS: 14Visualization and Analysis of Micrornas Within Kegg Pathways Using Vanesa(Walter de Gruyter GmbH, 2017) Hamzeiy, Hamid; Suluyayla, Rabia; Brinkrolf, Christoph; Janowski, Sebastian Jan; Hofestaedt, Ralf; Allmer, JensMicroRNAs (miRNAs) are small RNA molecules which are known to take part in post-transcriptional regulation of gene expression. Here, VANESA, an existing platform for reconstructing, visualizing, and analysis of large biological networks, has been further expanded to include all experimentally validated human miRNAs available within miRBase, TarBase and miRTarBase. This is done by integrating a custom hybrid miRNA database to DAWIS-M.D., VANESA's main data source, enabling the visualization and analysis of miRNAs within large biological pathways such as those found within the Kyoto Encyclopedia of Genes and Genomes (KEGG). Interestingly, 99.15 % of human KEGG pathways either contain genes which are targeted by miRNAs or harbor them. This is mainly due to the high number of interaction partners that each miRNA could have (e.g.: hsa-miR-335-5p targets 2544 genes and 71 miRNAs target NUFIP2). We demonstrate the usability of our system by analyzing the measles virus KEGG pathway as a proof-of-principle model and further highlight the importance of integrating miRNAs (both experimentally validated and predicted) into biological networks for the elucidation of novel miRNA-mRNA interactions of biological importance.Article Citation - WoS: 4Citation - Scopus: 4Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for Pre-Microrna Detection(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2017) Saçar Demirci, Müşerref Duygu; Allmer, JensMicroRNAs (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.Article Citation - WoS: 11Citation - Scopus: 11Mice With Catalytically Inactive Cathepsin a Display Neurobehavioral Alterations(Hindawi Publishing Corporation, 2017) Çalhan, Osman Yipkin; Seyrantepe, VolkanThe lysosomal carboxypeptidase A, Cathepsin A (CathA), is a serine protease with two distinct functions. CathA protects β-galactosidase and sialidase Neu1 against proteolytic degradation by forming a multienzyme complex and activates sialidase Neu1. CathA deficiency causes the lysosomal storage disease, galactosialidosis. These patients present with a broad range of clinical phenotypes, including growth retardation, and neurological deterioration along with the accumulation of the vasoactive peptide, endothelin-1, in the brain. Previous in vitro studies have shown that CathA has specific activity against vasoactive peptides and neuropeptides, including endothelin-1 and oxytocin. A mutant mouse with catalytically inactive CathA enzyme (CathAS190A) shows increased levels of endothelin-1. In the present study, we elucidated the involvement of CathA in learning and long-term memory in 3-, 6-, and 12-month-old mice. Hippocampal endothelin-1 and oxytocin accumulated in CathAS190A mice, which showed learning impairments as well as long-term and spatial memory deficits compared with wild-type littermates, suggesting that CathA plays a significant role in learning and in memory consolidation through its regulatory role in vasoactive peptide processing.Article Citation - WoS: 25Citation - Scopus: 26Generation of Acid Mine Lakes Associated With Abandoned Coal Mines in Northwest Turkey(Springer Verlag, 2016) Şanlıyüksel Yücel, Deniz; Balcı, Nurgül; Baba, AlperA total of five acid mine lakes (AMLs) located in northwest Turkey were investigated using combined isotope, molecular, and geochemical techniques to identify geochemical processes controlling and promoting acid formation. All of the investigated lakes showed typical characteristics of an AML with low pH (2.59-3.79) and high electrical conductivity values (1040-6430 μS/cm), in addition to high sulfate (594-5370 mg/l) and metal (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], and zinc [Zn]) concentrations. Geochemical and isotope results showed that the acid-generation mechanism and source of sulfate in the lakes can change and depends on the age of the lakes. In the relatively older lakes (AMLs 1 through 3), biogeochemical Fe cycles seem to be the dominant process controlling metal concentration and pH of the water unlike in the younger lakes (AMLs 4 and 5). Bacterial species determined in an older lake (AML 2) indicate that biological oxidation and reduction of Fe and S are the dominant processes in the lakes. Furthermore, O and S isotopes of sulfate indicate that sulfate in the older mine lakes may be a product of much more complex oxidation/dissolution reactions. However, the major source of sulfate in the younger mine lakes is in situ pyrite oxidation catalyzed by Fe(III) produced by way of oxidation of Fe(II). Consistent with this, insignificant fractionation between δ34SSO4 and δ34 SFeS2 values indicated that the oxidation of pyrite, along with dissolution and precipitation reactions of Fe(III) minerals, is the main reason for acid formation in the region. Overall, the results showed that acid generation during early stage formation of an AML associated with pyrite-rich mine waste is primarily controlled by the oxidation of pyrite with Fe cycles becoming the dominant processes regulating pH and metal cycles in the later stages of mine lake development.Article Citation - WoS: 25Citation - Scopus: 21Can Mirbase Provide Positive Data for Machine Learning for the Detection of Mirna Hairpins?(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2013) Demirci, Müşerref Duygu Saçar; Hamzeiy, Hamid; Allmer, JensExperimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.