Combinatorial chemistry has produced a huge amount of chemical libraries and data banks which include prospective drugs. Despite all of this progress, the fundamental problem still remains: how do we take advantage of this data to identify the prospective nature of a compound as a vital drug? Traditional methodologies fail to provide a solution to this.
Knowledge graphs, however, provide the framework which can make drug discovery much more efficient, effective and approachable. This radical advancement in technology can model biological knowledge complexity as it is found at its core. …
Text is the medium used to store the tremendous wealth of scientific knowledge regarding the world we live in. However, with its ever increasing magnitude and throughput; analysing this unstructured data has become an impossibly tedious task. This has led to the rise of Text Mining and Natural Language Processing (NLP) techniques and tools as the go-to for examining and processing large amounts of natural text data.
Text-Mining is the automatic extraction of structured semantic information from unstructured machine-readable text. …
One of the biggest challenges in our current state of medicine is to provide relevant, personalised and precise diagnoses and treatments. Rather than treating all patients the same, the goal is to fully take into account a person’s demographics and genetic profile while treating or diagnosing them.
In a nutshell, the current problem is that a large number of drugs and treatments prescribed to patients do not treat the individual patient but the generic disease. This is something doctors are well aware of — not all treatments affect every patient in the same way. Yet for decades, the strategy of…
Welcome to the multi-omics age of biological big data. Where data about a myriad of biological systems, all interacting with one another, is being collected. The data may represent genomes, proteomes, epigenomes, metabolomes, transcriptoms, biological pathways, diseases, drugs, published articles, imaging and even medical and clinical data. There is no doubt in the immense value of this tremendous amount of growing data.
Biological data is being accumulated all over the world from various organisations. Some examples include; NCBI, NIH, 1000 Genomes, Ensemble Genomes, Gene Expression Omnibuss, Gene Ontology, Global Biotic Interactions, Human Genome Diversity Project, KEGG, Reactome, MIT Cancer Genomics…
