Our engagement starts by defining the specific issues of an identified problem.
At every stage, we engage our partners with regular check-ins and transparency in our processes. We offer continued support to ensure a successful adaptation of our delivery in your workflow.
Our engagements broadly fall under one of the three classes described below.
The journey from data acquisition to a human interpretable format can be long, tedious, and error-prone. In addition, there is an opportunity cost of the time lost doing repetitive tasks.
Our solutions enable users to automate data processing, thus saving time for more involved data analysis and interpretation. We implement highly customized solutions to streamline processing for different kinds of data, such as, Metabolomics, Genomics, CRISPR, Drug Metabolism and Pharmacokinetics (DMPK). Our solutions speed up existing processing workflows through re-design and automation.
Validating hypotheses and extracting actionable insights from biological datasets is highly iterative and time consuming. It requires cross-domain expertise in mathematics, biology, data science, and visualization.
We have partnered with many academic and industrial labs to develop and apply mathematical models and algorithms to study biological processes spanning multiple temporal and spatial scales. We have expertise in integrating multi omics data to generate biological hypotheses. We also analyze proprietary datasets and public databases across diseases such as cancer and diabetes.
Existing visualization tools are often limited in their scope and are not designed for biological analyses. It is hard to generate graphical visual summaries of experiments and share them with colleagues and partners.
We build visualization tools designed to empower scientists to navigate the complexity of large biological datasets seamlessly. Our tools are pathway centric, don’t require tedious data manipulation to start visualizing, support large files and can be integrated with existing databases. They also allow users to make connections by deep linking to other relevant data sources.