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Book: Computational biology

Fenyö, David

(Methods in molecular biology ; 673 ; Springer protocols)

2010

Author's details	ed. by David Fenyö
Series title	Methods in molecular biology ; 673 Springer protocols
Collection
Language	English
Size	XI, 327 S. : Ill., graph. Darst.
Publisher	Humana Press
Publishing place	New York u.a.
Publishing country	United States
Document type	Book
HBZ-ID	HT016381610
ISBN	978-1-60761-841-6 ; 9781607618423 ; 1-60761-841-9 ; 1607618427
Database	Catalogue ZB MED Medicine, Health

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Article ; Online: Proteogenomics Reveal the Overexpression of HLA-I in Cancer.

Wang, Ying / Fenyö, David

Journal of proteome research

2023 Volume 22, Issue 11, Page(s) 3625–3639

Abstract: An accurate quantification of HLA class I gene expression is important in understanding the interplay with the tumor microenvironment of antitumor cytotoxic T cell activities. Because HLA-I sequences are highly variable, standard RNAseq and mass ... ...

Abstract	An accurate quantification of HLA class I gene expression is important in understanding the interplay with the tumor microenvironment of antitumor cytotoxic T cell activities. Because HLA-I sequences are highly variable, standard RNAseq and mass spectrometry-based quantification workflows using common genome and protein sequence references do not provide HLA-I allele specific quantifications. Here, we used personalized HLA-I nucleotide and protein reference sequences based on the subjects' HLA-I genotypes and surveyed tumor and adjacent normal samples from patients across nine cancer types. Mass spectrometry using data dependent acquisition data was validated to be sufficient to estimate HLA-A protein expression at the allele level. We found that HLA-I proteins were present in significantly higher levels in tumors compared to adjacent normal tissues from 41 to 63% of head and neck squamous cell carcinoma, uterine corpus endometrial carcinoma, and clear cell renal cell carcinoma patients, and this was driven by increased levels of HLA-I gene transcripts. Most immune cell types are universally enriched in HLA-I high tumors, while endothelial and neuronal cells showed divergent relationships with HLA-I. Pathway analysis revealed that tumor senescence and autophagy activity influence the level of HLA-I proteins in glioblastoma. Genes correlated to HLA-I protein expression are mostly the ones directly involved in HLA-I function in immune response and cell death, while glycosylation genes are exclusively co-expressed with HLA-I at the protein level.
MeSH term(s)	Humans ; Histocompatibility Antigens Class I/genetics ; Histocompatibility Antigens Class I/analysis ; Carcinoma, Squamous Cell/metabolism ; Proteogenomics ; Carcinoma, Renal Cell/genetics ; Carcinoma, Renal Cell/pathology ; Kidney Neoplasms/pathology ; Tumor Microenvironment
Chemical Substances	Histocompatibility Antigens Class I
Language	English
Publishing date	2023-10-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.3c00491
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: When blockchain meets artificial intelligence: An application to cancer histopathology.

Hong, Runyu / Fenyö, David

Cell reports. Medicine

2022 Volume 3, Issue 6, Page(s) 100666

Abstract: A recent study by Saldanha et al. demonstrates that blockchain-based models outcompeted local models and performed similarly with merged models to predict molecular features from cancer histopathology images. The results reveal the capability of ... ...

Abstract	A recent study by Saldanha et al. demonstrates that blockchain-based models outcompeted local models and performed similarly with merged models to predict molecular features from cancer histopathology images. The results reveal the capability of decentralized models in molecular diagnosis of cancer.
MeSH term(s)	Artificial Intelligence ; Blockchain ; Humans ; Neoplasms/diagnosis
Language	English
Publishing date	2022-06-16
Publishing country	United States
Document type	Journal Article ; Comment
ISSN	2666-3791
ISSN (online)	2666-3791
DOI	10.1016/j.xcrm.2022.100666
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Proteogenomics unveils mechanistic insights to precision oncology.

Dou, Yongchao / Katsnelson, Lizabeth / Zhang, Bing / Fenyö, David / Liu, Tao

Clinical and translational medicine

2023 Volume 13, Issue 11, Page(s) e1477

MeSH term(s)	Humans ; Proteogenomics ; Neoplasms/genetics ; Precision Medicine ; Mass Spectrometry ; Biomarkers, Tumor
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2023-11-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2697013-2
ISSN	2001-1326 ; 2001-1326
ISSN (online)	2001-1326
ISSN	2001-1326
DOI	10.1002/ctm2.1477
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: L1EM: a tool for accurate locus specific LINE-1 RNA quantification.

McKerrow, Wilson / Fenyö, David

Bioinformatics (Oxford, England)

2019 Volume 36, Issue 4, Page(s) 1167–1173

Abstract: Motivation: LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE- ... ...

Abstract	Motivation: LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify LINE-1 RNA at specific loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA. Results: We provide a tool, L1EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each genomic locus, separating transcripts that are capable of generating retrotransposition from those that are not. We show the accuracy of L1EM on simulated data and against long read sequencing from HEK cells. Availability and implementation: L1EM is written in python. The source code along with the necessary annotations are available at https://github.com/FenyoLab/L1EM and distributed under GPLv3. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Genomics ; Long Interspersed Nucleotide Elements ; RNA ; Sequence Analysis, RNA ; Software
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2019-09-02
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz724
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

Okimoto, Leandro Y S / Mendonca-Neto, Rayol / Nakamura, Fabíola G / Nakamura, Eduardo F / Fenyö, David / Silva, Claudio T

BMC bioinformatics

2024 Volume 25, Issue 1, Page(s) 92

Abstract: Background: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, ... ...

Abstract	Background: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. Results: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. Conclusions: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.
MeSH term(s)	Humans ; Female ; Breast Neoplasms/genetics ; Breast Neoplasms/diagnosis ; Biomarkers, Tumor/genetics ; Proteomics ; Gene Expression Profiling/methods ; Genetic Techniques
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2024-03-01
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/s12859-024-05715-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: TranspoScope: interactive visualization of retrotransposon insertions.

Grivainis, Mark / Tang, Zuojian / Fenyö, David

Bioinformatics (Oxford, England)

2020 Volume 36, Issue 12, Page(s) 3877–3878

Abstract: Motivation: Retrotransposition is an important force in shaping the human genome and is involved in prenatal development, disease and aging. Current genome browsers are not optimized for visualizing the experimental evidence for retrotransposon ... ...

Abstract	Motivation: Retrotransposition is an important force in shaping the human genome and is involved in prenatal development, disease and aging. Current genome browsers are not optimized for visualizing the experimental evidence for retrotransposon insertions. Results: We have developed a specialized browser to visualize the evidence for retrotransposon insertions for both targeted and whole-genome sequencing data. Availability and implementation: TranspoScope's source code, as well as installation instructions, are available at https://github.com/FenyoLab/transposcope.
MeSH term(s)	Genome, Human ; Humans ; Retroelements/genetics ; Software ; Whole Genome Sequencing
Chemical Substances	Retroelements
Language	English
Publishing date	2020-04-16
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/btaa244
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Article ; Online: Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study.

Westcott, Jill M / Hughes, Francine / Liu, Wenke / Grivainis, Mark / Hoskins, Iffath / Fenyo, David

Journal of medical Internet research

2022 Volume 24, Issue 7, Page(s) e34108

Abstract: Background: Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States.: Objective: The aim of this paper is to use machine learning techniques to identify patients at risk for postpartum ... ...

Abstract	Background: Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States. Objective: The aim of this paper is to use machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery. Methods: Women aged 18 to 55 years delivering at a major academic center from July 2013 to October 2018 were included for analysis (N=30,867). A total of 497 variables were collected from the electronic medical record including the following: demographic information; obstetric, medical, surgical, and family history; vital signs; laboratory results; labor medication exposures; and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of ≥1000 mL at the time of delivery, regardless of delivery method, with 2179 (7.1%) positive cases observed. Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (21,606/30,867, 70%) and validation (4630/30,867, 15%) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (4631/30,867, 15%) determined final performance by assessing for accuracy, area under the receiver operating curve (AUROC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus models limited to data available prior to the second stage of labor or at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery. Results: Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUROC 0.979, 95% CI 0.971-0.986 vs AUROC 0.955, 95% CI 0.939-0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination but lacked the sensitivity necessary for clinical applicability. Conclusions: Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete data sets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery and to validate the findings of this study. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.
MeSH term(s)	Cohort Studies ; Female ; Humans ; Machine Learning ; Postpartum Hemorrhage/diagnosis ; Postpartum Hemorrhage/etiology ; Pregnancy ; Retrospective Studies ; Risk Assessment
Language	English
Publishing date	2022-07-18
Publishing country	Canada
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/34108
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Article ; Online: MED19 encodes two unique protein isoforms that confer prostate cancer growth under low androgen through distinct gene expression programs.

Ruoff, Rachel / Weber, Hannah / Wang, Ying / Huang, Hongying / Shapiro, Ellen / Fenyö, David / Garabedian, Michael J

Scientific reports

2023 Volume 13, Issue 1, Page(s) 18227

Abstract: MED19, a component of the mediator complex and a co-regulator of the androgen receptor (AR), is pivotal in prostate cancer cell proliferation. MED19 has two isoforms: a full-length "canonical" and a shorter "alternative" variant. Specific antibodies were ...

Abstract	MED19, a component of the mediator complex and a co-regulator of the androgen receptor (AR), is pivotal in prostate cancer cell proliferation. MED19 has two isoforms: a full-length "canonical" and a shorter "alternative" variant. Specific antibodies were developed to investigate these isoforms. Both exhibit similar expression in normal prostate development and adult prostate tissue, but the canonical isoform is elevated in prostate adenocarcinomas. Overexpression of canonical MED19 in LNCaP cells promotes growth under conditions of androgen deprivation in vitro and in vivo, mirroring earlier findings with alternative MED19-overexpressing LNCaP cells. Interestingly, alternative MED19 cells displayed strong colony formation in clonogenic assays under conditions of androgen deprivation, while canonical MED19 cells did not, suggesting distinct functional roles. These isoforms also modulated gene expression differently. Canonical MED19 triggered genes related to extracellular matrix remodeling while suppressing those involved in androgen-inactivating glucuronidation. In contrast, alternative MED19 elevated genes tied to cell movement and reduced those associated with cell adhesion and differentiation. The ratio of MED19 isoform expression in prostate cancers shifts with the disease stage. Early-stage cancers exhibit higher canonical MED19 expression than alternative MED19, consistent with canonical MED19's ability to promote cell proliferation under androgen deprivation. Conversely, alternative MED19 levels were higher in later-stage metastatic prostate cancer than in canonical MED19, reflecting alternative MED19's capability to enhance cell migration and autonomous cell growth. Our findings suggest that MED19 isoforms play unique roles in prostate cancer progression and highlights MED19 as a potential therapeutic target for both early and late-stage prostate cancer.
MeSH term(s)	Humans ; Male ; Androgens/metabolism ; Cell Line, Tumor ; Cell Proliferation/genetics ; Gene Expression ; Gene Expression Regulation, Neoplastic ; Mediator Complex/genetics ; Prostatic Neoplasms/genetics ; Prostatic Neoplasms/pathology ; Protein Isoforms/genetics ; Protein Isoforms/metabolism ; Receptors, Androgen/genetics ; Receptors, Androgen/metabolism
Chemical Substances	Androgens ; MED19 protein, human ; Mediator Complex ; Protein Isoforms ; Receptors, Androgen
Language	English
Publishing date	2023-10-25
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-45199-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Transposon insertion profiling by sequencing (TIPseq) identifies novel LINE-1 insertions in human sperm.

Berteli, Thalita S / Wang, Fang / McKerrow, Wilson / Navarro, Paula A / Fenyo, David / Boeke, Jef D / Kohlrausch, Fabiana B / Keefe, David L

Journal of assisted reproduction and genetics

2023 Volume 40, Issue 8, Page(s) 1835–1843

Abstract: Purpose: Long interspersed nuclear element-1 (LINE-1 or L1) comprises 17% of the human genome. Retrotransposons may perturb gene integrity or alter gene expression by altering regulatory regions in the genome. The germline employs a number of mechanisms, ...

Abstract	Purpose: Long interspersed nuclear element-1 (LINE-1 or L1) comprises 17% of the human genome. Retrotransposons may perturb gene integrity or alter gene expression by altering regulatory regions in the genome. The germline employs a number of mechanisms, including cytosine methylation, to repress retrotransposon transcription throughout most of life. Demethylation during germ cell and early embryo development de-represses retrotransposons. Intriguingly, de novo genetic variation appearing in sperm has been implicated in a number of disorders in offspring, including autism spectrum disorder, schizophrenia, and bipolar disorder. We hypothesize that human sperm exhibit de novo retrotransposition and employ a new sequencing method, single cell transposon insertion profiling by sequencing (scTIPseq) to map them in small amounts of human sperm. Methods: Cross-sectional case-control study of sperm samples (n=10 men; ages 32-55 years old) from consenting men undergoing IVF at NYU Langone Fertility Center. scTIPseq identified novel LINE-1 insertions in individual sperm and TIPseqHunter, a custom bioinformatics pipeline, compared the architecture of sperm LINE-1 to known LINE-1 insertions from the European database of Human specific LINE-1 (L1Hs) retrotransposon insertions (euL1db). Results: scTIPseq identified 17 novel insertions in sperm. New insertions were mainly intergenic or intronic. Only one sample did not exhibit new insertions. The location or number of novel insertions did not differ by paternal age. Conclusion: This study for the first time reports novel LINE-1 insertions in human sperm, demonstrating the feasibility of scTIPseq, and identifies new contributors to genetic diversity in the human germ line.
MeSH term(s)	Humans ; Male ; DNA Transposable Elements ; Spermatozoa ; Long Interspersed Nucleotide Elements ; Adult ; Middle Aged ; Sequence Analysis, DNA
Chemical Substances	DNA Transposable Elements
Language	English
Publishing date	2023-06-13
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1112577-9
ISSN	1573-7330 ; 1058-0468
ISSN (online)	1573-7330
ISSN	1058-0468
DOI	10.1007/s10815-023-02852-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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