相关论文: Analysis Of Cancer Omics Data In A Semantic Web Fr…

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try…

There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which…

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new…

Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on…

The Semantic Web (or Web of Data) represents the successful efforts towards linking and sharing data over the Web. The cornerstones of the Web of Data are RDF as data format and SPARQL as de-facto standard query language. Recent trends show…

Solutions to the classic problems of dealing with heterogeneous data and making entire collections interoperable while ensuring that any annotation, which includes the recognition-and-reward system of scientific publishing, need to fit into…

Risk estimation of developing breast cancer poses as the first prevention method for early diagnosis. Furthermore, data integration from different departments involved in the process plays a key role. In order to guarantee patient safety,…

An important challenge in cancer systems biology is to uncover the complex network of interactions between genes (tumor suppressor genes and oncogenes) implicated in cancer. Next generation sequencing provides unparalleled ability to probe…

We propose a novel computational framework leveraging hypergraph theory to analyse cancer stem cell markers (CSCMs) across multiple organs. Hypergraphs provide a robust representation of CSCM co-expression patterns, capturing their complex…

The benefit of using ontologies, defined by the respective data standards, is shown. It is presented how ontologies can be used for the semantic enrichment of data and how this can contribute to the vision of the semantic web to become…

In computational biology, biological entities such as genes or proteins are usually annotated with terms extracted from Gene Ontology (GO). The functional similarity among terms of an ontology is evaluated by using Semantic Similarity…

The World Wide Web currently evolves into a Web of Linked Data where content providers publish and link data as they have done with hypertext for the last 20 years. While the declarative query language SPARQL is the de facto for querying…

Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios. A…

A key component of deep learning (DL) for natural language processing (NLP) is word embeddings. Word embeddings that effectively capture the meaning and context of the word that they represent can significantly improve the performance of…

Semantic Web is actually an extension of the current one in that it represents information more meaningfully for humans and computers alike. It enables the description of contents and services in machine-readable form, and enables…

As the amount of data on the World Wide Web continues to grow exponentially, access to semantically structured information remains limited. The Semantic Web has emerged as a solution to enhance the machine-readability of data, making it…

Current research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common…

Semantic web is the next generation web, which concerns the meaning of web documents It has the immense power to pull out the most relevant information from the web pages, which is also meaningful to any user, using software agents. In…

We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of annotated data is a…

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment challenging. In recent years, high-throughput omic technologies have facilitated the discovery…