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  1. Article ; Online: Genetic dissection of complex traits using hierarchical biological knowledge.

    Hidenori Tanaka / Jason F Kreisberg / Trey Ideker

    PLoS Computational Biology, Vol 17, Iss 9, p e

    2021  Volume 1009373

    Abstract: Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical ... ...

    Abstract Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.
    Keywords Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Opportunities for basic, clinical, and bioethics research at the intersection of machine learning and genomics

    Shurjo K. Sen / Eric D. Green / Carolyn M. Hutter / Mark Craven / Trey Ideker / Valentina Di Francesco

    Cell Genomics, Vol 4, Iss 1, Pp 100466- (2024)

    2024  

    Abstract: Summary: The data-intensive fields of genomics and machine learning (ML) are in an early stage of convergence. Genomics researchers increasingly seek to harness the power of ML methods to extract knowledge from their data; conversely, ML scientists ... ...

    Abstract Summary: The data-intensive fields of genomics and machine learning (ML) are in an early stage of convergence. Genomics researchers increasingly seek to harness the power of ML methods to extract knowledge from their data; conversely, ML scientists recognize that genomics offers a wealth of large, complex, and well-annotated datasets that can be used as a substrate for developing biologically relevant algorithms and applications. The National Human Genome Research Institute (NHGRI) inquired with researchers working in these two fields to identify common challenges and receive recommendations to better support genomic research efforts using ML approaches. Those included increasing the amount and variety of training datasets by integrating genomic with multiomics, context-specific (e.g., by cell type), and social determinants of health datasets; reducing the inherent biases of training datasets; prioritizing transparency and interpretability of ML methods; and developing privacy-preserving technologies for research participants’ data.
    Keywords Genetics ; QH426-470 ; Internal medicine ; RC31-1245
    Subject code 006
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Network approaches and applications in biology.

    Trey Ideker / Ruth Nussinov

    PLoS Computational Biology, Vol 13, Iss 10, p e

    2017  Volume 1005771

    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2017-10-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Hierarchical association of COPD to principal genetic components of biological systems.

    Daniel E Carlin / Simon J Larsen / Vikram Sirupurapu / Michael H Cho / Edwin K Silverman / Jan Baumbach / Trey Ideker

    PLoS ONE, Vol 18, Iss 5, p e

    2023  Volume 0286064

    Abstract: Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which ... ...

    Abstract Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which a regular association test is first performed to identify variants associated with the disease phenotype, followed by a test for functional enrichment within the genes implicated by those variants. Here we introduce a concise one-step approach, Hierarchical Genetic Analysis (Higana), which directly computes phenotype associations against each function in the large hierarchy of biological functions documented by the Gene Ontology. Using this approach, we identify risk genes and functions for Chronic Obstructive Pulmonary Disease (COPD), highlighting microtubule transport, muscle adaptation, and nicotine receptor signaling pathways. Microtubule transport has not been previously linked to COPD, as it integrates genetic variants spread over numerous genes. All associations validate strongly in a second COPD cohort.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Hierarchical association of COPD to principal genetic components of biological systems

    Daniel E. Carlin / Simon J. Larsen / Vikram Sirupurapu / Michael H. Cho / Edwin K. Silverman / Jan Baumbach / Trey Ideker

    PLoS ONE, Vol 18, Iss

    2023  Volume 5

    Abstract: Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which ... ...

    Abstract Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which a regular association test is first performed to identify variants associated with the disease phenotype, followed by a test for functional enrichment within the genes implicated by those variants. Here we introduce a concise one-step approach, Hierarchical Genetic Analysis (Higana), which directly computes phenotype associations against each function in the large hierarchy of biological functions documented by the Gene Ontology. Using this approach, we identify risk genes and functions for Chronic Obstructive Pulmonary Disease (COPD), highlighting microtubule transport, muscle adaptation, and nicotine receptor signaling pathways. Microtubule transport has not been previously linked to COPD, as it integrates genetic variants spread over numerous genes. All associations validate strongly in a second COPD cohort.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: HiDeF

    Fan Zheng / She Zhang / Christopher Churas / Dexter Pratt / Ivet Bahar / Trey Ideker

    Genome Biology, Vol 22, Iss 1, Pp 1-

    identifying persistent structures in multiscale ‘omics data

    2021  Volume 15

    Abstract: Abstract In any ‘omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from ...

    Abstract Abstract In any ‘omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.
    Keywords Systems biology ; Multiscale ; Persistent homology ; Community detection ; Resolution ; Single-cell clustering ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Rare variant phasing using paired tumor:normal sequence data

    Alexandra R. Buckley / Trey Ideker / Hannah Carter / Nicholas J. Schork

    BMC Bioinformatics, Vol 20, Iss 1, Pp 1-

    2019  Volume 11

    Abstract: Abstract Background In standard high throughput sequencing analysis, genetic variants are not assigned to a homologous chromosome of origin. This process, called haplotype phasing, can reveal information important for understanding the relationship ... ...

    Abstract Abstract Background In standard high throughput sequencing analysis, genetic variants are not assigned to a homologous chromosome of origin. This process, called haplotype phasing, can reveal information important for understanding the relationship between genetic variants and biological phenotypes. For example, in genes that carry multiple heterozygous missense variants, phasing resolves whether one or both gene copies are altered. Here, we present a novel approach to phasing variants that takes advantage of unique properties of paired tumor:normal sequencing data from cancer studies. Results VAF phasing uses changes in variant allele frequency (VAF) between tumor and normal samples in regions of somatic chromosomal gain or loss to phase germline variants. We apply VAF phasing to 6180 samples from the Cancer Genome Atlas (TCGA) and demonstrate that our method is highly concordant with other standard phasing methods, and can phase an average of 33% more variants than other read-backed phasing methods. Using variant annotation tools designed to score gene haplotypes, we find a suggestive association between carrying multiple missense variants in a single copy of a cancer predisposition gene and earlier age of cancer diagnosis. Conclusions VAF phasing exploits unique properties of tumor genomes to increase the number of germline variants that can be phased over standard read-backed methods in paired tumor:normal samples. Our phase-informed association testing results call attention to the need to develop more tools for assessing the joint effect of multiple genetic variants.
    Keywords Variant phasing ; Cancer germline ; Cancer genomics ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 572
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Multiscale community detection in Cytoscape.

    Akshat Singhal / Song Cao / Christopher Churas / Dexter Pratt / Santo Fortunato / Fan Zheng / Trey Ideker

    PLoS Computational Biology, Vol 16, Iss 10, p e

    2020  Volume 1008239

    Abstract: Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques ...

    Abstract Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques and their accompanying visualization schemes available to a broad range of biologists. Here we present a service-oriented, end-to-end software framework, CDAPS (Community Detection APplication and Service), that integrates the identification, annotation, visualization, and interrogation of multiscale network communities, accessible within the popular Cytoscape network analysis platform. With novel design principles, CDAPS addresses unmet new challenges, such as identifying hierarchical community structures, comparison of outputs generated from diverse network resources, and easy deployment of new algorithms, to facilitate community-sourced science. We demonstrate that the CDAPS framework can be applied to high-throughput protein-protein interaction networks to gain novel insights, such as the identification of putative new members of known protein complexes.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2020-10-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Analysis of Matched Tumor and Normal Profiles Reveals Common Transcriptional and Epigenetic Signals Shared across Cancer Types.

    Andrew M Gross / Jason F Kreisberg / Trey Ideker

    PLoS ONE, Vol 10, Iss 11, p e

    2015  Volume 0142618

    Abstract: To identify the transcriptional regulatory changes that are most widespread in solid tumors, we performed a pan-cancer analysis using over 600 pairs of tumors and adjacent normal tissues profiled in The Cancer Genome Atlas (TCGA). Frequency of ... ...

    Abstract To identify the transcriptional regulatory changes that are most widespread in solid tumors, we performed a pan-cancer analysis using over 600 pairs of tumors and adjacent normal tissues profiled in The Cancer Genome Atlas (TCGA). Frequency of upregulation was calculated across mRNA expression levels, microRNA expression levels and CpG methylation sites and is provided here as a resource. Frequent tumor-associated alterations were identified using a simple statistical approach. Many of the identified changes were consistent with the increased rate of cell division in cancer, such as the overexpression of cell cycle genes and hypermethylation of PRC2 binding sites. However, we also identified proliferation-independent alterations, which highlight novel pathways essential to tumor formation. Nearly all of the GABA receptors are frequently downregulated, with the gene encoding the delta subunit (GABRD) strongly upregulated as the notable exception. Metabolic genes are also frequently downregulated, particularly alcohol dehydrogenases and others consistent with the decreased role of oxidative phosphorylation in cancerous cells. Alterations in the composition of GABA receptors and metabolism may play a key role in the differentiation of cancer cells, independent of proliferation.
    Keywords Medicine ; R ; Science ; Q
    Subject code 570
    Language English
    Publishing date 2015-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Protein networks as logic functions in development and cancer.

    Janusz Dutkowski / Trey Ideker

    PLoS Computational Biology, Vol 7, Iss 9, p e

    2011  Volume 1002180

    Abstract: Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the ... ...

    Abstract Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic.
    Keywords Biology (General) ; QH301-705.5
    Subject code 612
    Language English
    Publishing date 2011-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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