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Article ; Online: MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding.

Binatlı, Oğuz C / Gönen, Mehmet

2023 Volume 24, Issue 1, Page(s) 276

Abstract: Background: In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this ... ...

Abstract	Background: In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously. Results: We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .
MeSH term(s)	Drug Development/methods ; Algorithms ; Drug Discovery/methods ; Computational Biology/methods ; Drug Interactions
Language	English
Publishing date	2023-07-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05401-1
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Article ; Online: A novel approach for the production of zinc borate (4ZnO·B2O3·H2O) using a single-step hydrothermal method

Yalçın Ali / Gönen Mehmet

Main Group Metal Chemistry, Vol 44, Iss 1, Pp 1-

2021 Volume 8

Abstract: Zinc borate having the formula of 4ZnO·B2O3·H2O has been used as a fire retardant for polymers requiring high processing temperatures since it has a high dehydration temperature (around 415°C). The effects of reaction time, reaction temperature were ... ...

Abstract	Zinc borate having the formula of 4ZnO·B2O3·H2O has been used as a fire retardant for polymers requiring high processing temperatures since it has a high dehydration temperature (around 415°C). The effects of reaction time, reaction temperature were investigated on the heterogeneous reaction between solid zinc oxide and boric acid solution. A stoichiometric amount of zinc oxide and 5.0% excess boric acid were used in experiments and the other parameters, mixing speed (1700 rpm), the solid-liquid ratio of 20%, and the amount of seed crystal (3.9% wt) were kept constant for all experiments. A 91.1% conversion was obtained at 120°C for 5 h of reaction time. Precipitated product was filtered and washed by hot water to remove the excess boric acid. Finally it was dried until reaching to a constant mass in an air circulating oven at 105°C. Powder products were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). FTIR spectrum and XRD pattern of powders are consistent with data of the zinc borate given in the literature. According to SEM analysis, whiskers are less than 1 μm in diameter and their lengths are in the range of 1–10 μm.
Keywords	zinc borate ; fire retardant ; whisker ; hydrothermal method ; heterogeneous reaction ; Chemistry ; QD1-999
Subject code	660
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
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Article ; Online: Efficient Multitask Multiple Kernel Learning With Application to Cancer Research.

Rahimi, Arezou / Gonen, Mehmet

IEEE transactions on cybernetics

2022 Volume 52, Issue 9, Page(s) 8716–8728

Abstract: Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily ... ...

Abstract	Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily produce interpretable results. Restricting the solutions to the set of interpretable solutions increases the computational burden of the learning problem significantly, leading to computationally prohibitive run times for some important biomedical applications. That is why we propose a multitask MKL formulation with a clustering of tasks and develop a highly time-efficient solution approach for it. Our solution method is based on the Benders decomposition and treating the clustering problem as finding a given number of tree structures in a graph; hence, it is called the forest formulation. We use our method to discriminate early-stage and late-stage cancers using genomic data and gene sets and compare our algorithm against two other algorithms. The two other algorithms are based on different approaches for linearization of the problem while all algorithms make use of the cutting-plane method. Our results indicate that as the number of tasks and/or the number of desired clusters increase, the forest formulation becomes increasingly favorable in terms of computational performance.
MeSH term(s)	Algorithms ; Cluster Analysis ; Neoplasms/genetics
Language	English
Publishing date	2022-08-18
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2021.3052357
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Article ; Online: PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.

Bektaş, Ayyüce Begüm / Gönen, Mehmet

BMC bioinformatics

2021 Volume 22, Issue 1, Page(s) 537

Abstract: Background: Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is ... ...

Abstract	Background: Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. Results: In this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. Conclusions: PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.
MeSH term(s)	Algorithms ; Humans ; Machine Learning ; Neoplasms/genetics ; Oncogenes ; Tumor Burden
Language	English
Publishing date	2021-11-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04460-6
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Article ; Online: Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation.

Gönen, Mehmet

BMC bioinformatics

2016 Volume 17, Issue Suppl 16

Abstract: Background: Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes ... ...

Abstract	Background: Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data. (ii) Gene set analysis methods are used to find gene sets on which phenotypes are linearly dependent by bringing prior biological knowledge into the analysis, which may not capture more complex nonlinear dependencies. Thus, formulating an integrated model of gene set analysis and nonlinear predictive modeling is of great practical importance. Results: In this study, we propose a Bayesian binary classification framework to integrate gene set analysis and nonlinear predictive modeling. We then generalize this formulation to multitask learning setting to model multiple related datasets conjointly. Our main novelty is the probabilistic nonlinear formulation that enables us to robustly capture nonlinear dependencies between genomic data and phenotype even with small sample sizes. We demonstrate the performance of our algorithms using repeated random subsampling validation experiments on two cancer and two tuberculosis datasets by predicting important disease phenotypes from genome-wide gene expression data. Conclusions: We are able to obtain comparable or even better predictive performance than a baseline Bayesian nonlinear algorithm and to identify sparse sets of relevant genes and gene sets on all datasets. We also show that our multitask learning formulation enables us to further improve the generalization performance and to better understand biological processes behind disease phenotypes.
MeSH term(s)	Adult ; Algorithms ; Bayes Theorem ; Child ; Computational Biology/methods ; Gene Expression Profiling/methods ; Humans ; Neoplasms/genetics ; Neoplasms/metabolism ; Neoplasms/pathology ; Phenotype ; Transcriptome ; Tuberculosis/genetics ; Tuberculosis/metabolism ; Tuberculosis/pathology
Language	English
Publishing date	2016-12-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-016-1311-3
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Article ; Online: Nek2A prevents centrosome clustering and induces cell death in cancer cells via KIF2C interaction.

Kalkan, Batuhan Mert / Ozcan, Selahattin Can / Cicek, Enes / Gonen, Mehmet / Acilan, Ceyda

Cell death & disease

2024 Volume 15, Issue 3, Page(s) 222

Abstract: Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal ... ...

Abstract	Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal segregation of genomic material by coalescing their centrosomes into two poles. This unique trait of cancer cells presents a promising target for cancer therapy, focusing on selectively attacking cells with supernumerary centrosomes. Nek2A is a kinase involved in mitotic regulation, including the centrosome cycle, where it phosphorylates linker proteins to separate centrosomes. In this study, we investigated if Nek2A also prevents clustering of supernumerary centrosomes, akin to its separation function. Reduction of Nek2A activity, achieved through knockout, silencing, or inhibition, promotes centrosome clustering, whereas its overexpression results in inhibition of clustering. Significantly, prevention of centrosome clustering induces cell death, but only in cancer cells with supernumerary centrosomes, both in vitro and in vivo. Notably, none of the known centrosomal (e.g., CNAP1, Rootletin, Gas2L1) or non-centrosomal (e.g., TRF1, HEC1) Nek2A targets were implicated in this machinery. Additionally, Nek2A operated via a pathway distinct from other proteins involved in centrosome clustering mechanisms, like HSET and NuMA. Through TurboID proximity labeling analysis, we identified novel proteins associated with the centrosome or microtubules, expanding the known interaction partners of Nek2A. KIF2C, in particular, emerged as a novel interactor, confirmed through coimmunoprecipitation and localization analysis. The silencing of KIF2C diminished the impact of Nek2A on centrosome clustering and rescued cell viability. Additionally, elevated Nek2A levels were indicative of better patient outcomes, specifically in those predicted to have excess centrosomes. Therefore, while Nek2A is a proposed target, its use must be specifically adapted to the broader cellular context, especially considering centrosome amplification. Discovering partners such as KIF2C offers fresh insights into cancer biology and new possibilities for targeted treatment.
MeSH term(s)	Humans ; Cell Cycle ; Cell Death ; Centrosome/metabolism ; Cluster Analysis ; Kinesins/genetics ; Kinesins/metabolism ; Microtubules/metabolism ; Mitosis ; Neoplasms/genetics ; Neoplasms/metabolism ; Spindle Apparatus/metabolism
Chemical Substances	KIF2C protein, human ; Kinesins (EC 3.6.4.4) ; NEK2 protein, human (EC 2.7.11.1)
Language	English
Publishing date	2024-03-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2541626-1
ISSN	2041-4889 ; 2041-4889
ISSN (online)	2041-4889
ISSN	2041-4889
DOI	10.1038/s41419-024-06601-0
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Article: Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.

Mokhtaridoost, Milad / Maass, Philipp G / Gönen, Mehmet

Cancers

2022 Volume 14, Issue 19

Abstract: MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in ... ...

Abstract	MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA-mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA-mRNA regulatory modules separately. We tested the model's ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA-mRNA signatures.
Language	English
Publishing date	2022-10-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14194939
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Article ; Online: Fast and interpretable genomic data analysis using multiple approximate kernel learning.

Bektaş, Ayyüce Begüm / Ak, Çiğdem / Gönen, Mehmet

Bioinformatics (Oxford, England)

2022 Volume 38, Issue Suppl 1, Page(s) i77–i83

Abstract: Motivation: Dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased ... ...

Abstract	Motivation: Dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calculating distinct kernel matrices. Results: To test our computational framework, namely, Multiple Approximate Kernel Learning (MAKL), we demonstrated our experiments on three cancer datasets and showed that MAKL is capable to outperform the baseline algorithm while using only a small fraction of the input features. We also reported selection frequencies of approximated kernel matrices associated with feature subsets (i.e. gene sets/pathways), which helps to see their relevance for the given classification task. Our fast and interpretable MKL algorithm producing sparse solutions is promising for computational biology applications considering its scalability and highly correlated structure of genomic datasets, and it can be used to discover new biomarkers and new therapeutic guidelines. Availability and implementation: MAKL is available at https://github.com/begumbektas/makl together with the scripts that replicate the reported experiments. MAKL is also available as an R package at https://cran.r-project.org/web/packages/MAKL. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Data Analysis ; Genomics ; Humans ; Machine Learning
Language	English
Publishing date	2022-06-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac241
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Article ; Online: An efficient framework to identify key miRNA-mRNA regulatory modules in cancer.

Mokhtaridoost, Milad / Gönen, Mehmet

Bioinformatics (Oxford, England)

2020 Volume 36, Issue Suppl_2, Page(s) i592–i600

Abstract: Motivation: Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a ...

Abstract	Motivation: Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA-mRNA regulatory modules. Results: We presented a two-step framework to model miRNA-mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supported miRTarBase database. Availability and implementation: Our implementation of proposed two-step RFR algorithm in R is available at https://github.com/MiladMokhtaridoost/2sRFR together with the scripts that replicate the reported experiments. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Computational Biology ; Gene Expression Profiling ; Gene Expression Regulation ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Humans ; MicroRNAs/genetics ; Neoplasms/genetics ; RNA, Messenger/genetics
Chemical Substances	MicroRNAs ; RNA, Messenger
Language	English
Publishing date	2020-12-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa798
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Article ; Online: A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers.

Rahimi, Arezou / Gönen, Mehmet

Bioinformatics (Oxford, England)

2020 Volume 36, Issue 12, Page(s) 3766–3772

Abstract: Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of ... ...

Abstract	Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction. Results: We studied the problem of discriminating early- and late-stage tumors of several cancers using genomic information while enforcing interpretability on the solutions. To this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to identify the relationships between the input tasks and kernels. We tested our algorithm on 15 cancer cohorts and observed that, in most cases, MTMKL outperforms other algorithms (including random forests, support vector machine and single-task multiple kernel learning) in terms of predictive power. Using the aggregate results from multiple replications, we also derived similarity matrices between cancer cohorts, which are, in many cases, in agreement with available relationships reported in the relevant literature. Availability and implementation: Our implementations of support vector machine and multiple kernel learning algorithms in R are available at https://github.com/arezourahimi/mtgsbc together with the scripts that replicate the reported experiments. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Cluster Analysis ; Humans ; Machine Learning ; Neoplasms/diagnosis ; Neoplasms/genetics ; Support Vector Machine
Language	English
Publishing date	2020-03-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa168
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