ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • 2020-2022  (6)
Collection
Publisher
Years
Year
  • 1
    facet.materialart.
    Unknown
    Predicting Candidate Genes From Phenotypes, Functions, And Anatomical Site Of Expression (2020)
    Oxford University Press
    Details
    Publication Date: 2020-10-14
    Description: Motivation Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models. Results We developed a novel graph-based machine learning method for biomedical ontologies which is able to exploit axioms in ontologies and other graph-structured data. Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression. We then develop a machine learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state of the art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes which are associated with phenotypes, functions, or site of expression. Availability Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    Semantic similarity and machine learning with ontologies (2020)
    Oxford University Press
    Details
    Publication Date: 2020-10-13
    Description: Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier (2020)
    Public Library of Science
    Details
    Publication Date: 2020-11-18
    Description: Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases.
    Print ISSN: 1553-734X
    Electronic ISSN: 1553-7358
    Topics: Biology , Computer Science
    Published by Public Library of Science
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes (2021)
    Oxford University Press
    Details
    Publication Date: 2021-03-02
    Description: Motivation Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. Results We developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Availability Code and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 5
    facet.materialart.
    Unknown
    Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies (2020)
    BioMed Central
    Details
    Publication Date: 2020-12-01
    Description: Background Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may reveal previously hidden contradictions. Methods We developed a method that tests for hidden unsatisfiabilities in an ontology that arise when combined with other ontologies. For this purpose, we combined sets of ontologies and use automated reasoning to determine whether unsatisfiable classes are present. In addition, we designed and implemented a novel algorithm that can determine justifications for contradictions across extremely large and complicated ontologies, and use these justifications to semi-automatically repair ontologies by identifying a small set of axioms that, when removed, result in a consistent and coherent set of ontologies. Results We tested the mutual consistency of the OBO Foundry and the OBO ontologies and find that the combined OBO Foundry gives rise to at least 636 unsatisfiable classes, while the OBO ontologies give rise to more than 300,000 unsatisfiable classes. We also applied our semi-automatic repair algorithm to each combination of OBO ontologies that resulted in unsatisfiable classes, finding that only 117 axioms could be removed to account for all cases of unsatisfiability across all OBO ontologies. Conclusions We identified a large set of hidden unsatisfiability across a broad range of biomedical ontologies, and we find that this large set of unsatisfiable classes is the result of a relatively small amount of axiomatic disagreements. Our results show that hidden unsatisfiability is a serious problem in ontology interoperability; however, our results also provide a way towards more consistent ontologies by addressing the issues we identified.
    Electronic ISSN: 1472-6947
    Topics: Computer Science , Medicine
    Published by BioMed Central
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 6
    facet.materialart.
    Unknown
    DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions (2021)
    Oxford University Press
    Details
    Publication Date: 2021-07-28
    Description: Motivation In silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks. Results We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods. Availability and implementation DTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...