PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
Browse
16 results
Search Results
Article Citation - WoS: 26Citation - Scopus: 31miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking(PeerJ Inc., 2021-05-19) Yousef, M.; Göy, G.; Mitra, R.; Eischen, C.M.; Jabeer, A.; Bakir-Güngör, B.A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/ malikyousef/miRcorrNet. © 2021 Elsevier B.V., All rights reserved.Article Topological Feature Generation for Link Prediction in Biological Networks(PeerJ Inc, 2023-05-09) Temiz, Mustafa; Bakir-Gungor, Burcu; Sahan, Pinar Guner; Coskun, Mustafa; Güner Şahan, PınarGraph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predict potential interactions in networks. However, most graph embedding methods suffer from high computational costs in the form of high computational complexity of the embedding methods and learning times of the classifier, as well as the high dimensionality of complex biological networks. To address these challenges, in this study, we use the Chopper algorithm as an alternative approach to graph embedding, which accelerates the iterative processes and thus reduces the running time of the iterative algorithms for three different (nervous system, blood, heart) undirected protein-protein interaction (PPI) networks. Due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. We evaluated the performance of the proposed method by comparing it with state-of-the-art methods. Extensive experiments demonstrate that the proposed approach reduces the learning time of the classifier and performs better in link prediction. We have also shown that the proposed embedding method is faster than state-of-the-art methods on three different PPI datasets.Article Citation - WoS: 9Citation - Scopus: 13The Impact and Future of Artificial Intelligence in Medical Genetics and Molecular Medicine: An Ongoing Revolution(Springer Heidelberg, 2024-08) Ozcelik, Firat; Dundar, Mehmet Sait; Yildirim, A. Baki; Henehan, Gary; Vicente, Oscar; Sanchez-Alcazar, Jose A.; Dundar, MunisArtificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.Article Citation - WoS: 48Citation - Scopus: 65Review of Feature Selection Approaches Based on Grouping of Features(PeerJ Inc, 2023-07-17) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Bulut, Nurten; Qaqish, Bahjat; Yousef, MalikWith the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly -ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work's findings can guide effective design of new FS approaches using feature grouping.Article Citation - WoS: 18Citation - Scopus: 20Resveratrol Triggers Anti-Proliferative and Apoptotic Effects in FLT3-LTD Acute Myeloid Leukemia Cells via Inhibiting Ceramide Catabolism Enzymes(Humana Press inc, 2022-01-20) Ersoz, Nur Sebnem; Adan, AysunResveratrol possesses well-defined anti-carcinogenic activities. However, how resveratrol exerts its anti-leukemic actions by modulating anti-apoptotic ceramide catabolism enzymes, mainly sphingosine kinase (SK-1) and glucosylceramide synthase (GCS), in FLT3-ITD AML remains unclear. Resveratrol, SKI II (SK inhibitor) and PDMP (GCS inhibitor) were evaluated alone or in combinations for their effect on cell proliferation (MTT assay), apoptosis (annexin V-FITC/PI staining by flow cytometry) and cell cycle progression (PI staining by flow cytometry) in MOLM-13 and MV4-11 cells. The combination indexes (CIs) were calculated based on cell proliferation data using CompuSyn software. Caspase-3 and PARP activation, changes in SK-1 and GCS levels by resveratrol alone or PARP cleavage in co-treatments were determined by western blot. Resveratrol and inhibitors alone inhibited cell proliferation in a dose- and time-dependent manner. Resveratrol downregulated SK-1 and GCS expression in both cell lines. It induced apoptosis by phosphatidylserine (PS) exposure together with caspase-3 and PARP cleavage and arrested the cell cycle slightly at the S phase. Co-administrations intensified resveratrol's effect by inhibiting cell proliferation synergistically (A CI of < 1) or additively (A CI 1.0-1.1) and inducing apoptosis via PS relocalization and PARP cleavage. Resveratrol plus SKI II did not affect cell cycle progression significantly, however, resveratrol plus PDMP blocked cycle progression at G0/G1 and S phases for MOLM-13 cells and MV4-11 cells, respectively. Overall, resveratrol may inhibit FLT3-ITD AML cell proliferation by inhibiting ceramide catabolism and be evaluated as a chemopreventive after detailed analysis of the crosstalk between resveratrol and ceramide catabolism pathway.Article Citation - WoS: 4Citation - Scopus: 5Novel Insights Into Bacillus Thuringiensis: Beyond Its Role as a Bioinsecticide(Elsevier, 2025-03) Jouzani, Gholamreza Salehi; Sharafi, Reza; Argentel-Martinez, Leandris; Penuelas-Rubio, Ofelda; Ozkan, Ceyda; Incegul, Bengisu; Azizoglu, Ugur; Salehi Jouzani, GholamrezaThis review explores the diverse applications of Bacillus thuringiensis (Bt) beyond its traditional role as a bioinsecticide. Bt produces a variety of compounds with distinct chemical structures and biological activities. These include antimicrobial agents effective against plant pathogens and bioactive compounds that promote plant growth through the production of siderophores, hormones, and enzymes. Additionally, Bt's industrial potential is highlighted, encompassing biofuel production, bioplastics, nanoparticle synthesis, food preservation, anticancer therapies, and heavy metal bioremediation. This critical analysis emphasizes recent advancements and applications, providing insights into Bt's role in sustainable agriculture, biotechnology, and environmental management.Article Citation - WoS: 9Citation - Scopus: 11Inhibition of PI3K-AKT-mTOR Pathway and Modulation of Histone Deacetylase Enzymes Reduce the Growth of Acute Myeloid Leukemia Cells(Humana Press inc, 2023-12-26) Sansacar, Merve; Sagir, Helin; Akcok, Emel Basak Gencer; Gencer Akçok, Emel BaşakOne of the most widespread forms of blood cancer is known as acute myeloid leukemia (AML) which has an incidence of 80% with poor prognosis. Although there are different treatment methods for AML in clinic, the heterogeneity and complexity of the disease show that new treatments are needed. The aim of this study is to investigate the anticancer effects of inhibition of PI3K and HDAC enzymes on CMK and MOLM-13 AML cells lines. We demonstrated that the combination of LY294002 with SAHA and Tubastatin A significantly decreased the cell viability of both cell lines. In contrast, the LY294002 and PCI-34051 combination did not show a significant difference compared to the single LY294002 administration. The combination treatment of LY294002 and HDAC inhibitors did not induce apoptosis significantly. However, LY294002 + SAHA and LY294002 + PCI-34051 resulted in G0/G1 and G2/M cell cycle arrest in CMK cells, respectively. On the other hand, compared to control cells, LY294002 + SAHA and LY294002 + PCI-34051 led to G0/G1 phase arrest in MOLM-13. Furthermore, the LY294002 + PCI-34051 combination elevated the expression rate of LC3BII/I, an autophagy marker, in CMK cells by 2.5-fold. Our study revealed that the combinations of PI3K inhibitor and HDAC inhibitors showed a synergistic effect and caused a reduction in cell viability and increased cell cycle arrest on MOLM-13 and CMK cell lines. In addition, the expression of LC3BII was elevated in the CMK cell line. In conclusion, although more mechanistic studies are required, a combinational inhibition of PI3K and HDAC could be a promising approach for AML.Article Citation - WoS: 35Citation - Scopus: 42Inflammatory Bowel Disease Biomarkers of Human Gut Microbiota Selected via Different Feature Selection Methods(PeerJ Inc, 2022-04-25) Bakir-Gungor, Burcu; Lar, Hilal Hac; Jabeer, Amhar; Nalbantoglu, Ozkan Ufuk; Aran, Oya; Yousef, Malik; Hacilar, HilalThe tremendous boost in next generation sequencing and in the "omics" technologies makes it possible to characterize the human gut microbiome-the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn's disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.Article Identification of Nonsense Variants in the ATM Gene Mimicking SCID Phenotype: A Brief Report(Springer, 2025-05-16) Firtina, Sinem; Saritas, Merve; Ng, Yuk Yin; Nepesov, Serdar; Kiykim, Ayca; Bozkurt, Selcen; Sayitoglu, MugeSevere combined immunodeficiency (SCID) represents a life-threatening inborn error of immunity, necessitating rapid diagnosis and intervention to prevent fatal outcomes. While SCID is characterized by profound T-cell lymphopenia, it may overlap with other conditions like ataxia-telangiectasia (AT), which also presents with T-cell deficiencies. This study examines two cases of suspected SCID in infants, later identified as AT due to pathogenic variants in the ATM gene. Despite initial negative results from SCID-targeted gene panels, further genetic testing revealed nonsense mutations (p.Y2036X and p.E1996X) in the FAT domain of the ATM gene, confirmed by Sanger sequencing. The patients exhibited significant T-cell lymphopenia and reduced ATM protein activity, indicative of AT. These findings highlight the importance of comprehensive genetic screening beyond common SCID-associated genes, especially in patients with atypical presentations. Early and accurate diagnosis can prevent mismanagement and guide appropriate therapies, improving patient outcomes.Article Citation - WoS: 152Citation - Scopus: 157Computational Analysis of MicroRNA-Mediated Interactions in SARS-CoV Infection(PeerJ Inc, 2020-06-05) Demirci, Muserref Duygu Sacar; Adan, AysunMicroRNAs (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, MED 12L, MED 19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STATI 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 ORF lab, 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.