A manual approach to literature review, is no longer practical. However, despite significant advances in the technology, many computational approaches struggle to accurately tag and disambiguate scientific terms, let alone deal with the complexity and variability of unstructured scientific language.
We at SciBite understand the complexities of science. Our solutions transform scientific text into machine-readable data. This semantic enrichment opens up new possibilities for you to mine data more effectively, derive valuable insights and ensure you never miss something relevant.
Coupled with SciBite’s comprehensive hand-curated ontologies, our semantic analytics software can recognise and extract relevant terms or patterns found in scientific text and harmonise synonymous terms, such as ‘heart attack’ and ‘myocardial infarction’, so that they identified as the same entity.
By accurately tagging all relevant concepts within a document, SciBite enables you to rapidly identify the most relevant terms and concepts and cut through the background ‘noise’ to get to the real essence of the article.
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Artificial Intelligence (AI) has been touted as a way to revolutionise the entire pharmaceutical value chain. Despite such promises, tangible evidence of how AI is actually helping research has been elusive.
One of the more promising applications of AI is Machine Learning: ‘training’ a computational model to make decisions or predictions with the inclusion of a feedback loop to refine the model based on the accuracy of a given decision.
In this paper we provide a range of real-world examples that illustrate how SciBite is pioneering the use of Machine Learning and Semantic Analytics to transform common scientific and business processes, deliver robust and repeatable results and conserve the valuable time of experts.
Real-world evidence reported by patients themselves is an under-utilised resource for pharmaceutical companies striving to remain competitive and maintain awareness of the effects of their drugs.
Health forums, such as PatientsLikeMe, provide a wealth of valuable information, but many current computational approaches struggle to deal with the inherent ambiguity and informal language used within them.
SciBite uses semantic analytics to transform the free text within patient forums into unambiguous, machine-readable data. This enables pharmaceutical companies to unlock the value of patient-reported data and make faster, more informed decisions.
Phenotypic similarity between diseases is an important factor in biomedical research since similar diseases often share similar molecular origins. This forms the basis of an inference-led approach to disease characterisation known as Phenotype Triangulation.
However, evidence of disease similarity is often hidden within unstructured biomedical literature and often not presented as direct evidence, necessitating a time consuming and costly review process to identify relevant linkages. Such linkages are particularly challenging to find for rare diseases for which the amount of existing research to draw from is still at a relatively low volume.
SciBite has developed a method that combines Semantic Analytics and Machine Learning to unlock the potential of biomedical literature and successfully predict disease relationships without any prior knowledge of the diseases, based on the strength of indirect evidence.
Data-driven drug development promises to enable pharmaceutical companies to derive deeper insights and make faster, more informed decisions. A fundamental step to achieving this nirvana is important to be able to make sense of the information available and to make connections between disparate, heterogeneous data sources.
The primary role of Resource Description Framework (RDF) is to store meaning with data and represent it in a structured way that is meaningful to computers.
Through semantic enrichment, SciBite enables unstructured documents to be converted to RDF, providing the high quality, contextualised data needed for subsequent discovery and analytics to be effective.
With the rise in machine learning and artificial intelligence approaches to big data, systems that can integrate into the complex ecosystem typically found within large enterprises are increasingly important.
Hadoop systems can hold billions of data objects but suffer from the common problem that such objects can be hard or organise due to a lack of descriptive meta-data. SciBite can improve the discoverability of this vast resource by unlocking the knowledge held in unstructured text to power next-generation analytics and insight.
Here we describe how the combination of Hadoop and SciBite brings significant value to large-scale processing projects.
Most pharmaceutical companies will have, at some point, deployed an Electronic Laboratory Notebook (ELN) with the goal of centralising R&D data. ELNs have become an important source of both key experimental results and the development history of new methods and processes.
However, most pharmaceutical companies are unable to realise the true value of the data stored in their ELN. Much of the information stored within it is captured as qualitative free text or as attachments, with the ability to mine it limited to rudimentary text and keyword searches.
SciBite’s unique combination of retrospective and prospective semantic enrichment immediately brings scientifically intelligent search to any ELN platform, opening up new possibilities to mine the data more effectively and derive valuable scientific and business insights.
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