Related papers: Primer on the Gene Ontology
Gene Ontology (GO) is the most important resource for gene function annotation. It provides a way to unify biological knowledge across different species via a dynamic and controlled vocabulary. GO is now widely represented in the Semantic…
The Gene Ontology (GO) is a formidable resource but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding…
Contemporary techniques in biology produce readouts for large numbers of genes simultaneously, the typical example being differential gene expression measurements. Moreover, those genes are often richly annotated using GO terms that…
The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to each other. These term-term relationships encode how scientists conceive…
The Gene Ontology (GO) is a major bioinformatics ontology that provides structured controlled vocabularies to classify gene and proteins function and role. The GO and its annotations to gene products are now an integral part of functional…
The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The information accumulated time-by-time and included in the…
The increasing availability of high throughput data arising from gene expression studies leads to the necessity of methods for summarizing the available information. As annotation quality improves it is becoming common to rely on the Gene…
Gene Ontology (GO) is the primary gene function knowledge base that enables computational tasks in biomedicine. The basic element of GO is a term, which includes a set of genes with the same function. Existing research efforts of GO mainly…
The Gene Ontology aims to define the universe of functions known for gene products, at the molecular, cellular and organism levels. While the ontology is designed to cover all aspects of biology in a "species independent manner", the fact…
Public repositories for genome and proteome annotations, such as the Gene Ontology (GO), rarely stores negative annotations, i.e. proteins not possessing a given function. This leaves undefined or ill defined the set of negative examples,…
Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data. In this chapter, we present the Computer Science Ontology (CSO), which is the largest ontology of research…
The study of human genes and diseases is very rewarding and can lead to improvements in healthcare, disease diagnostics and drug discovery. In this paper, we further our previous study on gene disease relationship specifically with the…
Gene studies are crucial for fields such as protein structure prediction, drug discovery, and cancer genomics, yet they face challenges in fully utilizing the vast and diverse information available. Gene studies require clean, factual…
Increasingly used high throughput experimental techniques, like DNA or protein microarrays give as a result groups of interesting, e.g. differentially regulated genes which require further biological interpretation. With the systematic…
Recent developments in next generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical…
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies.…
Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can…
Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure the GO similarity between two proteins in isolation, but pairs of proteins in a network alignment…
Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases…
Trees -- i.e., the type of data structure known under this name -- are central to many aspects of knowledge organization. We investigate some central design choices concerning the ontological modeling of such trees. In particular, we…