Monitoring changes in the Gene Ontology and their impact on genomic data analysis

Jacobson, Matthew P.; Sedeño-Cortés, Adriana E.; Pavlidis, Paul

doi:10.1093/gigascience/giy103

Cited by 11 publications

(11 citation statements)

References 27 publications

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“…www.nature.com/scientificreports/ > Level2GOTermBP(level = 17, organism = "Human") [1] "GO:2000321" "GO:0010880" "GO:2000320" "GO:0045630" "GO:2000703" [6] "GO:2000734" "GO:0031587" "GO:0045627" "GO:0045629" "GO:0045626" [11] "GO:0021808" "GO:0060315" "GO:0060316" "GO:0021836" "GO:0021972" [16] "GO:0031586" "GO:0021817" "GO:0097379" "GO:0021816" "GO:0097380"…”

Section: Structural Exploration Of Go Inmentioning

confidence: 99%

“…This allows the connection between genes and GO-terms for deriving organism-specific information. Currently, GO is the most comprehensive and widely used knowledge base concerning functional information about genes [3][4][5][6] .…”

mentioning

confidence: 99%

“…5 College of Artificial Intelligence, Nankai University, Tianjin 300350, China. 6 Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720 Tampere, Finland. * email: frank.emmert-streib@tuni.fi only very basic functions are available for obtaining structural information [18][19][20] .…”

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confidence: 99%

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Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Manjang

Tripathi

Yli‐Harja

et al. 2020

Sci Rep

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Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the package . The main features of are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our package is freely available from CRAN.

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Section: Structural Exploration Of Go Inmentioning

confidence: 99%

mentioning

confidence: 99%

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Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Manjang

Tripathi

Yli‐Harja

et al. 2020

Sci Rep

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“…The gene clustering [15] is used to identify and separate each patient's disease-causing genes. From these genes, the gene having gene ID (i.e., 8614) is searched in GODB [16]. These GODB is stored in separate sheets having GO term ID, functional description & association features.…”

Section: Figure 3 Gene Association With the Go Termsmentioning

confidence: 99%

Gene Expression Analysis to Mine Highly Relevant Gene Data in Chronic Diseases and Annotating its GO Terms

Bell¹,

Vigila²

2018

EAI Endorsed Transactions on Energy Web

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Gene Expression Analysis seeks to find the highly expressive genes from a highly dimensional Microarray disease gene Database by using some statistical gene selection approaches based on supervised or unsupervised learning. Gene Ontology (GO) introduces a series of method for annotating gene function that combines semantic similarity measures by taking account on the underlying topology of gene interaction networks for structuring the graphs of the gene ontology. Initially, the genes are identified by clustering microarray disease dataset giving gene id of most expressive genes and further the genes are associated based on their biological functionalities using the gene ontology annotations taken from bioinformatics database. Also, t-test is used for finding the up-regulated genes so it can be annotated to find the most significant gene terms in hierarchical graph structure. The proposed method uses term Similarity measures to compare two or more gene ontology terms. Finally, gene functional classification and gene term association is done by forming a graph structure to be readily analysed by medical practitioner intending the nature of disease-causing genes at deeper level of understanding in chronic disorder based health care environments.

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“…Smart personal assistants (PAs), for instance, often need to infer the location or social context of their user from sensor data (e.g., GPS coordinates, nearby Bluetooth devices), but the hierarchy of relevant places and people changes as the user's life changes [9]. Another example is protein function prediction, in which annotations are structured according to the Gene Ontology, a resource that is regularly updated to incorporate the latest discoveries [11]. We refer to this as knowledge drift (KD).…”

Section: Introductionmentioning

confidence: 99%

Human-in-the-loop Handling of Knowledge Drift

Bontempelli¹,

Giunchiglia²,

Passerini³

et al. 2021

Preprint

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We introduce and study knowledge drift (KD), a complex form of drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the groundtruth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, introducing a new is-a relation between two concepts might be confused with individual changes to those concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in many human-in-the-loop applications (like smart personal assistants) the user knows whether and what kind of drift occurred recently. Motivated by this, we introduce TR-CKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD. In addition, TRCKD implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that often a handful of queries to the user are enough to substantially improve prediction performance on both synthetic and realistic data.

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Monitoring changes in the Gene Ontology and their impact on genomic data analysis

Cited by 11 publications

References 27 publications

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Gene Expression Analysis to Mine Highly Relevant Gene Data in Chronic Diseases and Annotating its GO Terms

Human-in-the-loop Handling of Knowledge Drift

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