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Book ; Online ; E-Book: Enträtselung der genetischen Variation von Subulicystidium longisporum

Lysenko, Ludmila

(BestMasters,)

2020

Abstract: In diesem Buch beschäftigt sich Ludmila Lysenko mit der Frage, ob es sich bei Subulicystidium ... Fachkräfte aus den Bereichen Mikrobiologie und Labordiagnostik Die Autorin Ludmila Lysenko studierte Biologie ... Untersuchungsmethoden. Sie beschäftigte sich in ihren Arbeiten aus dem Fachbereich Ökologie intensiv mit Arten ...

Author's details	von Ludmila Lysenko
Series title	BestMasters,
Abstract	In diesem Buch beschäftigt sich Ludmila Lysenko mit der Frage, ob es sich bei Subulicystidium longisporum um eine kryptische Spezies handelt. Die Autorin untersucht hierzu erstmals Proben mit einer breiten geographischen Abdeckung unter Verwendung diverser phylogenetischer Analyseverfahren auf ihre Sequenzunterschiede im Locus ITS. Zur Unterstützung der molekulargenetischen Daten wird die Form und Größe der Basidiosporen hinzugezogen. Der Inhalt Die kryptische Spezies Subulicystidium longisporum ITS (internal transcribed spacer) als molekulargenetisches Tool im Reich der Fungi DNA-Barcoding im Reich der Fungi Schwierigkeiten der morphologischen Artabgrenzung Vor- und Nachteile der molekulargenetischen Untersuchung zur Speziesidentifizierung Die Zielgruppen Dozierende und Studierende der Mykologie, Biologie und Molekularbiologie Fachkräfte aus den Bereichen Mikrobiologie und Labordiagnostik Die Autorin Ludmila Lysenko studierte Biologie an der Universität Kassel, ihr Forschungsschwerpunkt ist die Biodiversität. Sie verfügt über Kenntnisse der klassischen Artbestimmung basierend auf morphologischen Merkmalen als auch durch diverse molekulargenetische Untersuchungsmethoden. Sie beschäftigte sich in ihren Arbeiten aus dem Fachbereich Ökologie intensiv mit Arten der corticioiden Basidiomycota.
Keywords	Ecology ; Microbiology ; Bioinformatics ; Ecology
Subject code	333.95
Language	German
Size	1 online resource (XVII, 87 S. 1 Abb.)
Edition	1st ed. 2020.
Publisher	Springer Fachmedien Wiesbaden ; Imprint: Springer Spektrum
Publishing place	Wiesbaden
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
ISBN	3-658-29224-5 ; 3-658-29223-7 ; 978-3-658-29224-9 ; 978-3-658-29223-2
DOI	10.1007/978-3-658-29224-9
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Advances in AI and machine learning for predictive medicine.

Sharma, Alok / Lysenko, Artem / Jia, Shangru / Boroevich, Keith A / Tsunoda, Tatsuhiko

Journal of human genetics

2024

Abstract: The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data ... ...

Abstract	The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.
Language	English
Publishing date	2024-02-29
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1425192-9
ISSN	1435-232X ; 1434-5161
ISSN (online)	1435-232X
ISSN	1434-5161
DOI	10.1038/s10038-024-01231-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.

Sharma, Alok / Lysenko, Artem / Boroevich, Keith A / Tsunoda, Tatsuhiko

Scientific reports

2023 Volume 13, Issue 1, Page(s) 2483

Abstract: ... other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development ...

Abstract	Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.
MeSH term(s)	Humans ; Deep Learning ; Multiomics ; Neoplasms/drug therapy ; Neural Networks, Computer ; Antineoplastic Agents/pharmacology ; Antineoplastic Agents/therapeutic use
Chemical Substances	Antineoplastic Agents
Language	English
Publishing date	2023-02-11
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-29644-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Alok Sharma / Artem Lysenko / Keith A. Boroevich / Tatsuhiko Tsunoda

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 14

Abstract: ... other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development ...

Abstract	Abstract Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.

Jia, Shangru / Lysenko, Artem / Boroevich, Keith A / Sharma, Alok / Tsunoda, Tatsuhiko

Briefings in bioinformatics

2023 Volume 24, Issue 5

Abstract: ... reached 87.5%, which is more than 7% higher compared with the state-of-the-art methods. ...

Abstract	Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering algorithms, which project single-cell expression data into a lower dimensional space and then cluster cells based on their distances from each other. However, as these methods do not use reference datasets, they can only achieve a rough classification of cell-types, and it is difficult to improve the recognition accuracy further. To effectively solve this issue, we propose a novel supervised annotation method, scDeepInsight. The scDeepInsight method is capable of performing manifold assignments. It is competent in executing data integration through batch normalization, performing supervised training on the reference dataset, doing outlier detection and annotating cell-types on query datasets. Moreover, it can help identify active genes or marker genes related to cell-types. The training of the scDeepInsight model is performed in a unique way. Tabular scRNA-seq data are first converted to corresponding images through the DeepInsight methodology. DeepInsight can create a trainable image transformer to convert non-image RNA data to images by comprehensively comparing interrelationships among multiple genes. Subsequently, the converted images are fed into convolutional neural networks such as EfficientNet-b3. This enables automatic feature extraction to identify the cell-types of scRNA-seq samples. We benchmarked scDeepInsight with six other mainstream cell annotation methods. The average accuracy rate of scDeepInsight reached 87.5%, which is more than 7% higher compared with the state-of-the-art methods.
MeSH term(s)	Deep Learning ; Single-Cell Gene Expression Analysis ; Algorithms ; Benchmarking ; Cluster Analysis ; Sequence Analysis, RNA ; Gene Expression Profiling
Language	English
Publishing date	2023-07-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad266
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A comparative multi-level toxicity assessment of carbon-based Gd-free dots and Gd-doped nanohybrids from coffee waste: hematology, biochemistry, histopathology and neurobiology study.

Kuznietsova, Halyna / Dziubenko, Natalia / Paliienko, Konstantin / Pozdnyakova, Natalia / Krisanova, Natalia / Pastukhov, Artem / Lysenko, Tetiana / Dudarenko, Marina / Skryshevsky, Valeriy / Lysenko, Vladimir / Borisova, Tatiana

Scientific reports

2023 Volume 13, Issue 1, Page(s) 9306

Abstract: Here, a comparative toxicity assessment of precursor carbon dots from coffee waste (cofCDs) obtained using green chemistry principles and Gd-doped nanohybrids (cofNHs) was performed using hematological, biochemical, histopathological assays in vivo (CD1 ... ...

Abstract	Here, a comparative toxicity assessment of precursor carbon dots from coffee waste (cofCDs) obtained using green chemistry principles and Gd-doped nanohybrids (cofNHs) was performed using hematological, biochemical, histopathological assays in vivo (CD1 mice, intraperitoneal administration, 14 days), and neurochemical approach in vitro (rat cortex nerve terminals, synaptosomes). Serum biochemistry data revealed similar changes in cofCDs and cofNHs-treated groups, i.e. no changes in liver enzymes' activities and creatinine, but decreased urea and total protein values. Hematology data demonstrated increased lymphocytes and concomitantly decreased granulocytes in both groups, which could evidence inflammatory processes in the organism and was confirmed by liver histopathology; decreased red blood cell-associated parameters and platelet count, and increased mean platelet volume, which might indicate concerns with platelet maturation and was confirmed by spleen histopathology. So, relative safety of both cofCDs and cofNHs for kidney, liver and spleen was shown, whereas there were concerns about platelet maturation and erythropoiesis. In acute neurotoxicity study, cofCDs and cofNHs (0.01 mg/ml) did not affect the extracellular level of L-[
MeSH term(s)	Rats ; Mice ; Animals ; Coffee ; Carbon ; Neurobiology ; Liver/pathology ; Hematology
Chemical Substances	Coffee ; Carbon (7440-44-0)
Language	English
Publishing date	2023-06-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-36496-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DeepFeature: feature selection in nonimage data using convolutional neural network.

Sharma, Alok / Lysenko, Artem / Boroevich, Keith A / Vans, Edwin / Tsunoda, Tatsuhiko

Briefings in bioinformatics

2021 Volume 22, Issue 6

Abstract: Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. ... ...

Abstract	Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications of this type of networks to high-dimensional omics data and, most importantly, meaningful interpretation of the results returned from such models in a biomedical context remains an open problem. Here we present, an approach applying a CNN to nonimage data for feature selection. Our pipeline, DeepFeature, can both successfully transform omics data into a form that is optimal for fitting a CNN model and can also return sets of the most important genes used internally for computing predictions. Within the framework, the Snowfall compression algorithm is introduced to enable more elements in the fixed pixel framework, and region accumulation and element decoder is developed to find elements or genes from the class activation maps. In comparative tests for cancer type prediction task, DeepFeature simultaneously achieved superior predictive performance and better ability to discover key pathways and biological processes meaningful for this context. Capabilities offered by the proposed framework can enable the effective use of powerful deep learning methods to facilitate the discovery of causal mechanisms in high-dimensional biomedical data.
MeSH term(s)	Algorithms ; Deep Learning ; Humans ; Neural Networks, Computer
Language	English
Publishing date	2021-08-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab297
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A comparative multi-level toxicity assessment of carbon-based Gd-free dots and Gd-doped nanohybrids from coffee waste

Halyna Kuznietsova / Natalia Dziubenko / Konstantin Paliienko / Natalia Pozdnyakova / Natalia Krisanova / Artem Pastukhov / Tetiana Lysenko / Marina Dudarenko / Valeriy Skryshevsky / Vladimir Lysenko / Tatiana Borisova

Scientific Reports, Vol 13, Iss 1, Pp 1-

hematology, biochemistry, histopathology and neurobiology study

2023 Volume 14

Abstract: Abstract Here, a comparative toxicity assessment of precursor carbon dots from coffee waste (cofCDs) obtained using green chemistry principles and Gd-doped nanohybrids (cofNHs) was performed using hematological, biochemical, histopathological assays in ... ...

Abstract	Abstract Here, a comparative toxicity assessment of precursor carbon dots from coffee waste (cofCDs) obtained using green chemistry principles and Gd-doped nanohybrids (cofNHs) was performed using hematological, biochemical, histopathological assays in vivo (CD1 mice, intraperitoneal administration, 14 days), and neurochemical approach in vitro (rat cortex nerve terminals, synaptosomes). Serum biochemistry data revealed similar changes in cofCDs and cofNHs-treated groups, i.e. no changes in liver enzymes' activities and creatinine, but decreased urea and total protein values. Hematology data demonstrated increased lymphocytes and concomitantly decreased granulocytes in both groups, which could evidence inflammatory processes in the organism and was confirmed by liver histopathology; decreased red blood cell-associated parameters and platelet count, and increased mean platelet volume, which might indicate concerns with platelet maturation and was confirmed by spleen histopathology. So, relative safety of both cofCDs and cofNHs for kidney, liver and spleen was shown, whereas there were concerns about platelet maturation and erythropoiesis. In acute neurotoxicity study, cofCDs and cofNHs (0.01 mg/ml) did not affect the extracellular level of L-[14C]glutamate and [3H]GABA in nerve terminal preparations. Therefore, cofNHs demonstrated minimal changes in serum biochemistry and hematology assays, had no acute neurotoxicity signs, and can be considered as perspective biocompatible non-toxic theragnostic agent.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Arete - candidate gene prioritization using biological network topology with additional evidence types.

Lysenko, Artem / Boroevich, Keith Anthony / Tsunoda, Tatsuhiko

BioData mining

2017 Volume 10, Page(s) 22

Abstract: Background: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the ... ...

Abstract	Background: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data. Results: We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system. Conclusions: We provide efficient reference implementations of two popular gene prioritization algorithms - DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches.
Language	English
Publishing date	2017-07-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2438773-3
ISSN	1756-0381
ISSN	1756-0381
DOI	10.1186/s13040-017-0141-9
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Article ; Online: An integrative machine learning approach for prediction of toxicity-related drug safety.

Lysenko, Artem / Sharma, Alok / Boroevich, Keith A / Tsunoda, Tatsuhiko

Life science alliance

2018 Volume 1, Issue 6, Page(s) e201800098

Abstract: Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading ... ...

Abstract	Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.
Language	English
Publishing date	2018-11-28
Publishing country	United States
Document type	Journal Article
ISSN	2575-1077
ISSN (online)	2575-1077
DOI	10.26508/lsa.201800098
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