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  1. Book ; Online ; Thesis: Die funktionserhaltende, integrative Genselektion: Eine Methode zur Reduktion von krankheitsbezogenen Gensätzen auf ihre Schlüsselkomponenten

    Lippmann, Catharina [Verfasser] / Ultsch, Alfred [Akademischer Betreuer]

    2020  

    Author's details Catharina Lippmann ; Betreuer: Alfred Ultsch
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language German
    Publisher Philipps-Universität Marburg
    Publishing place Marburg
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  2. Article ; Online: Computational functional genomics-based reduction of disease-related gene sets to their key components.

    Lippmann, Catharina / Ultsch, Alfred / Lötsch, Jörn

    Bioinformatics (Oxford, England)

    2018  Volume 35, Issue 14, Page(s) 2362–2370

    Abstract: Motivation: The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are ... ...

    Abstract Motivation: The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available.
    Results: We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes.
    Conclusions: A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes.
    Availability and implementation: The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Computational Biology ; Gene Expression ; Genomics ; Knowledge Bases ; Software
    Language English
    Publishing date 2018-11-27
    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/bty986
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  3. Article ; Online: Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

    Lippmann, Catharina / Kringel, Dario / Ultsch, Alfred / Lötsch, Jörn

    Pharmacogenomics

    2018  Volume 19, Issue 9, Page(s) 783–797

    Abstract: Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from ...

    Abstract Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.
    MeSH term(s) Analgesics/therapeutic use ; Animals ; Computational Biology/methods ; Data Mining/methods ; Drug Discovery/methods ; Drug Repositioning/methods ; Gene Expression/genetics ; Genomics/methods ; Humans ; Pain/drug therapy ; Pain/genetics ; Proteomics/methods
    Chemical Substances Analgesics
    Language English
    Publishing date 2018-05-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2019513-8
    ISSN 1744-8042 ; 1462-2416
    ISSN (online) 1744-8042
    ISSN 1462-2416
    DOI 10.2217/pgs-2018-0036
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    Zs.A 5247: Show issues Location:
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  4. Article: Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective.

    Lötsch, Jörn / Lippmann, Catharina / Kringel, Dario / Ultsch, Alfred

    Frontiers in molecular neuroscience

    2017  Volume 10, Page(s) 252

    Abstract: Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of "big data" enables novel research approaches to ... ...

    Abstract Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of "big data" enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about
    Language English
    Publishing date 2017-08-08
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452967-9
    ISSN 1662-5099
    ISSN 1662-5099
    DOI 10.3389/fnmol.2017.00252
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  5. Article: Development of an AmpliSeq

    Kringel, Dario / Kaunisto, Mari A / Lippmann, Catharina / Kalso, Eija / Lötsch, Jörn

    Frontiers in pharmacology

    2018  Volume 9, Page(s) 1008

    Abstract: Background: ...

    Abstract Background:
    Language English
    Publishing date 2018-09-19
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587355-6
    ISSN 1663-9812
    ISSN 1663-9812
    DOI 10.3389/fphar.2018.01008
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  6. Article ; Online: Olfactory drug effects approached from human-derived data.

    Lötsch, Jörn / Knothe, Claudia / Lippmann, Catharina / Ultsch, Alfred / Hummel, Thomas / Walter, Carmen

    Drug discovery today

    2015  Volume 20, Issue 11, Page(s) 1398–1406

    Abstract: The complexity of the sense of smell makes adverse olfactory effects of drugs highly likely, which can impact a patient's quality of life. Here, we present a bioinformatics approach that identifies drugs with potential olfactory effects by connecting ... ...

    Abstract The complexity of the sense of smell makes adverse olfactory effects of drugs highly likely, which can impact a patient's quality of life. Here, we present a bioinformatics approach that identifies drugs with potential olfactory effects by connecting drug target expression patterns in human olfactory tissue with drug-related information and the underlying molecular drug targets taken from publically available databases. We identified 71 drugs with listed olfactory effects and 147 different targets. Taking the target-based approach further, we found additional drugs with potential olfactory effects, including 152 different substances interacting with genes expressed in the human olfactory bulb. Our proposed bioinformatics approach provides plausible hypotheses about mechanistic drug effects for drug discovery and repurposing and, thus, would be appropriate for use during drug development.
    MeSH term(s) Animals ; Computational Biology/methods ; Drug Design ; Drug Discovery/methods ; Drug-Related Side Effects and Adverse Reactions/etiology ; Gene Expression Regulation/drug effects ; Humans ; Molecular Targeted Therapy ; Olfactory Bulb/drug effects ; Olfactory Bulb/physiology ; Olfactory Perception/drug effects ; Olfactory Perception/genetics ; Quality of Life
    Language English
    Publishing date 2015-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1324988-5
    ISSN 1878-5832 ; 1359-6446
    ISSN (online) 1878-5832
    ISSN 1359-6446
    DOI 10.1016/j.drudis.2015.06.012
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