Advanced analytics made easy
Perform complex sequential analyses involving millions of data points, within the user-friendly interface, and display the output as clear, interactive visualisations of your data. Derive new insights by exploiting a framework that has the structured flexibility to perform the analyses you need, with the power that comes from correctly structured data and advanced machine learning.
Jump straight to extracting insights
Run unsupervised, supervised and statistical modelling on any subset of data stored within the Aigenpulse Platform. A suite of pre-built analytical workflows and visualisations can run straight out-of-the-box, so you can start analysing your data as soon as it reaches the system. And because labs all have different analytics requirements, the Platform gives you the freedom to define and refine your own tailored workflows, when you need them.
Are you spending more time looking for data than analysing it?
Speak to one of our scientists to find how you focus on advanced data analytics with the Aigenpulse Platform.
Focus on analysis, not formatting
The Aigenpulse Platform takes care of standardisation across internal and external datasets, normalisation, and linking appropriate data entity identifiers across data providers and between genomics, transcriptomics and proteomics. Seamless, relational data management across entities and experimental disciplines means that you can focus on what matters most, deriving meaningful conclusions from your data. And because all of the metadata that adds additional context to your datasets is automatically matched and managed, you only have to think about analysing and interpreting your scientific output, and not on spending time mapping, curating and arguing about scientific terms.
Large scale machine learning at your fingertips
Specialised advanced machine learning algorithms, such as natural language processing (NLP), are available as Aigenpulse Platform modules. The Aigenpulse Text Mining module, heavily utilising NLP, can scan huge amounts of unstructured data held in resources including published research and other literature, patents and websites, to identify and capture information that is relevant to your research, experiments, samples and other entities. Raw information is parsed, and connections – for example, gene-disease relationships – with Aigenpulse Platform entities and ontology concepts are identified, extracted and classified, and integrated into the resource. The Aigenpulse Text Mining module has demonstrated its ability to identify promising new druggable target genes, for example with customers in the biopharmaceutical space looking to connect their development pipeline to novel application areas.
The Aigenpulse Platform architecture accommodates fast, automated data processing pipelines. Any data type-specific data processing pipeline can be configured, including proteomics, genomics, genetics, metabolomics and FACS. Raw files are automatically detected, processing parameters can be set directly by the system or configured by users, and processing jobs triggered and distributed across available computational resources (available with automatic scaling and management to cloud computing and distributed environments). Processed datasets are then subjected to quality assurance validation before integration into the Aigenpulse Platform. The Aigenpulse Data Processing Pipeline modules reduce the need for time-consuming manual movement and management of files, and automate the QA of all datasets, so that only validated datasets are integrated into the resource.
Aigenpulse Platform Workflow management combines Entities, Experiments and Analytics for organisations to automate and scale scientific processes within a single, unified and audited system. Offering both flexibility and structure for optimum control, the system is not limited by the scope of tools, and allows users to set up custom workflows, with seamless iteration over time as processes are optimised and evolved. Each executed workflow is logged on the Aigenpulse Platform with the entitles, data, processing parameters and output all persisted providing you the expectation of reproducible analytics and reduced data obsolescence.