Henry Loeffler-Wirth

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About Henry Loeffler-Wirth:

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SEEK ID: https://armlifebank.am/people/25

Location: Germany

ORCID: https://orcid.org/0000-0001-8239-440X

Joined: 9th May 2025

Expertise: Not specified

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Advanced Programmes list for this Person with search and filtering

Bioinformatics Tools for Analysis of Big Genomic Data

This Programme focuses on the development, implementation, and application of bioinformatics tools for the analysis of large-scale genomic datasets. It aims to facilitate the processing, integration, and interpretation of high-throughput sequencing data, including whole-genome, transcriptome, and epigenome analyses. The Programme encompasses computational approaches for variant discovery, gene expression profiling, network biology, and multi-omics data integration.

Projects: Biological pathway activity analysis, ML approaches for omic data analysis, Tools for Telomere Biology

Web page: Not specified

Cancer Omics

This Programme offers a comprehensive summary of various projects focused on deciphering the intricate molecular mechanisms of cancer development, progression, and response to treatments, utilizing bioinformatics, computational biology, and machine learning. These are multi-laboratory efforts carried out across several research institutions, supported by various grant programs.

Projects: Molecular Profiling of Cancer Metastases, Molecular characterization of cancers with long-read RNA sequencing, Multi-Omics Molecular Diversity of Cancers

Web page: Not specified

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ML approaches for omic data analysis

This project focuses on developing and applying machine learning (ML) methodologies to analyze complex omic datasets, including genomics, transcriptomics, proteomics, and metabolomics. By leveraging advanced ML techniques, the project aims to uncover novel biological insights, identify biomarkers, and enhance our understanding of molecular mechanisms underlying various diseases. The initiative is a collaborative effort maintained by the Institute of Molecular Biology NAS RA (IMB), ...

Programme: Bioinformatics Tools for Analysis of Big Genomic Data

Public web page: Not specified

Organisms: Not specified

Molecular characterization of cancers with long-read RNA sequencing

The overarching objective of this project is to utilize nanopore sequencing technology and to develop machine learning–based pipelines for the integrative analysis of heterogeneous biological "big data," including multi-omic and phenotypic datasets. The goal is to investigate the molecular mechanisms underlying selected cancers and to identify novel omics-based markers for cancer classification, diagnostics, and prognostics.

Programme: Cancer Omics

Public web page: Not specified

Start date: 1st Sep 2021

End date: 30th Sep 2026

Organisms: Human

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Interdisciplinary Center for Bioinformatics (IZBI)

Country: Germany

City: Leipzig

Web page: https://www.izbi.uni-leipzig.de/

Showing 5 out of a possible 9 Publications Advanced Publications list for this Person with search and filtering

Transcriptional states of CAR-T infusion relate to neurotoxicity – lessons from high-resolution single-cell SOM expression portraying

Multi-Omics Molecular Diversity of Cancers

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Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular …

Authors: Henry Loeffler-Wirth, Michael Rade, Arsen Arakelyan, Markus Kreuz, Markus Loeffler, Ulrike Koehl, Kristin Reiche, Hans Binder

Date Published: 28th Sep 2022

Publication Type: Journal

DOI: 10.3389/fimmu.2022.994885

Citation: Frontiers in Immunology,13

Created: 10th Sep 2025 at 07:31

The Evolving Faces of the SARS-CoV-2 Genome

COVID-19

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Surveillance of the evolving SARS-CoV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and …

Authors: Maria Schmidt, Mamoona Arshad, Stephan H. Bernhart, Siras Hakobyan, Arsen Arakelyan, Henry Loeffler-Wirth, Hans Binder

Date Published: 3rd Sep 2021

Publication Type: Journal

DOI: 10.3390/v13091764

Citation: Viruses,13(9):1764

Created: 19th Aug 2025 at 09:42

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy.

ML approaches for omic data analysis, Molecular characterization of cancers with long-read RNA sequencing

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Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a …

Authors: Siras Hakobyan, Henry Loeffler-Wirth, Arsen Arakelyan, Hans Binder, Manfred Kunz

Date Published: 2nd Jul 2021

Publication Type: Journal

PubMed ID: 34281234

DOI: 10.3390/ijms22137165

Citation: International journal of molecular sciences,22(13)

Created: 10th Sep 2025 at 09:16

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Multi-Omics Molecular Diversity of Cancers

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Molecular mechanisms of lower-grade (II–III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like …

Authors: Edith Willscher, Lydia Hopp, Markus Kreuz, Maria Schmidt, Siras Hakobyan, Arsen Arakelyan, Bettina Hentschel, David T. W. Jones, Stefan M. Pfister, Markus Loeffler, Henry Loeffler-Wirth, Hans Binder

Date Published: 26th Jun 2021

Publication Type: Journal

DOI: 10.3390/cancers13133198

Citation: Cancers,13(13):3198

Created: 27th Aug 2025 at 11:22

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.

Multi-Omics Molecular Diversity of Cancers

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Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and … epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications. Keywords: age; gene expression; immune response; lifestyle and obesity; omics and phenotype integration; self-organizing maps; subtypes. Copyright © 2020 Schmidt, Hopp, Arakelyan, Kirsten, Engel, Wirkner, Krohn, Burkhardt, Thiery, Loeffler, Loeffler-Wirth and Binder.

Authors: Maria Schmidt, Lydia Hopp, Arsen Arakelyan, Holger Kirsten, Christoph Engel, Kerstin Wirkner, Knut Krohn, Ralph Burkhardt, Joachim Thiery, Markus Loeffler, Henry Loeffler-Wirth, Hans Binder

Date Published: 30th Oct 2020

Publication Type: Journal

PubMed ID: 33693414

DOI: 10.3389/fdata.2020.548873

Citation: Frontiers in big data,3:548873

Created: 10th Sep 2025 at 09:20

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