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CHICAGO--(BUSINESS WIRE)--Tempus, a leader in artificial intelligence and precision medicine, today announced the launch of its cloud-based data and analytics platform, Lens. The all-in-one platform will provide scientists and researchers across biotechnology and pharmaceutical companies with short term, on demand access to more than 35 petabytes of de-identified clinical and molecular datasets, along with the latest artificial intelligence analytical tools to accelerate drug discovery and development.

The Lens platform enables users to access, analyze, and build upon Tempus library of data in an optimized environment equipped with the tools needed to extract insight in minutes. Lens provides multiple ways for both researchers and physicians to leverage one of the worlds largest libraries of clinical and molecular data - including Tempus tumor/normal matched DNA, RNA, and H&E data - from creating targeted patient cohorts and simulating clinical trials in silico, to renting or licensing the underlying data to test hypotheses. Through Lens, users can also collaborate with Tempus computational biologists and data scientists to analyze datasets to uncover unique insights. Users can analyze data within the Lens platform through a combination of tried-and-true open source and purpose-built Tempus visual analysis tools or a cloud-based data science environment. They can also apply their own proprietary tools to the data.

Never before has real-world data played such a major role in guiding care and accelerating drug discovery and development. Our data library has reached such a significant scale that it was critical we develop Lens to help our partners navigate and utilize our vast dataset in real-time, helping them mine unique insights that can only be found in a library like ours, said Eric Lefkofsky, Founder and CEO of Tempus. The option for scientists and analysts to only pay for data when they need it, on a short term usage basis, is revolutionary and we expect it will not only save the healthcare system money, but will also unblock research and accelerate the very pace of innovation itself.

The platform serves partners drug discovery and development needs by validating targets with one of the markets largest multi-modal datasets, characterizing diseases in more granular ways, analyzing RNA expression signatures, designing trials for precise patient populations, and working with Tempus AI team to launch a multi-scale modeling approach.

"Visual data exploration in Lens is key to exploring ideas in minutes on large datasets. It helps lower the bar to access the data, and having this capability up front makes it easy to then move to deeper analysis, said Markus Bauer, Principal Scientist at Boehringer Ingelheim.

Lens also provides additional utility to administrators and clinical researchers within academic cancer centers by enabling quick, seamless assessment of patient prevalence across key clinical and molecular characteristics. Whether to evaluate and analyze a candidate population for research and publication, identify a subset of patients at the institution who may be considered for a newly approved therapy, or to understand mutation trends within a set of biomarkers relative to the general population, Lens empowers users with the tools to rapidly answer their important questions.

Lens has advanced our research and allowed us to easily leverage Tempus resources in the platform to characterize the genomic landscape of advanced prostate cancer in African American men with the goal to understand the mechanisms behind the disparities seen in clinical outcomes between patients of different ancestries, the findings of which we recently presented at two national meetings, said Dr. Nicholas Mitsiades, Associate Professor of Medicine at Baylor College of Medicine and Oncologist at the Dan L Duncan Comprehensive Cancer Center. The ability to filter patients and mutations in Lens offers a more precise look at patient populations and their genomic landscapes. By adding Lens to an arsenal of other products and services, Tempus has made it easy to interact with large datasets and quickly extract meaningful insights.

To learn more about Lens and how multi-modal data can uncover new personalized insights, please visit https://tempus.co/3uSWHhw.

About Tempus

Tempus is a technology company advancing precision medicine through the practical application of artificial intelligence in healthcare. With one of the worlds largest libraries of clinical and molecular data, and an operating system to make that data accessible and useful, Tempus enables physicians to make real-time, data-driven decisions to deliver personalized patient care and in parallel facilitates discovery, development and delivery of optimal therapeutics. The goal is for each patient to benefit from the treatment of others who came before by providing physicians with tools that learn as the company gathers more data. For more information, visit tempus.com.

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Tempus Unveils Its Lens Platform, Offering Unparalleled Access to One of the World's Largest De-Identified Clinical and Molecular Datasets - Business...

Businesses today are collecting more data than at any other time in history but finding the time of day to keep data quality high remains an issue, says Kitman Cheung, chief technology officer at IBM Data, AI, and Automation in the Asia Pacific (APAC). Cheung is referring to some of the roadblocks faced by the banking industry in the APAC region, and how data and AI can play a crucial role to help banks overcome them and crest the innovation wave.

The speed and pace of data pipelines is growing more rapidly than ever, too, with a greater variety of data and disparate database platforms both structured and unstructured data, making their way into data repositories. This burgeoning volume of data must be managed, and doing it well is not a trivial challenge.

Data challenges

Onboarding new data sets and data sources into a traditional data warehouse continues to be a challenge. [The result is that] data sets either remain outside or data get onboarded at a much slower pace, making it difficult for stakeholders to look at the entire spectrum of data in a cohesive way, said Cheung.

In the meantime, the demand for new data continues to increase in both volume and importance. As banks start demanding greater access to data and at a faster pace, the capability gap between business needs and the ability of IT to deliver will only grow.

A dearth of skillsets is another challenge that banks in the region face, says Cheung. It is hard to hire data scientists who understand the banking sector on one hand, even as there are many business users in banks with the smarts and business knowhow but lacking in data science knowledge to collaborate meaningfully. The result is that innovation slows down at a time where banks need it most to stay ahead.

We see quite a few projects which were delayed due to the unavailability of data. To address that, a lot of clients are now looking at enabling self-service access to data to eliminate some of the bottlenecks in data delivery and IT transformation.

Agility without being bogged down

A fixation on new technologies or solutions for its own sake can often exacerbate the situation. Cheung pointed to how organizations around the world once gravitated towards Hadoop as the Holy Grail to address all their data-centric challenges. Often, they do not solve the [data] problems that we are facing. Instead, it can create a different set of problems, he said.

This can include popular platforms such as the public cloud: You outsource a bunch of capabilities to a third-party vendor. There are economies of scale for sure, but if you are using the cloud for consistent workloads and for a very long time, it starts to get more expensive. You start to think about everything in terms of OPEX (Operating Expenditure), which sounds good in the first place, but it translates into a different set of problems.

Instead of a complete rip and replace strategy with the favored technology of the day, Cheung recommends that banks adopt a more holistic approach and focus on fundamentals such as open-source software, open standards, and interoperability. Pick the right tools for the job. Continue with tools that are working and think deeply about data governance from the start not as an afterthought.

With a holistic IT strategy, banks can create an innovative platform that allows them to stay focused on the business objectives of every project. Leverage technologies such as containerization, microservices, and APIs to plug in new capabilities and evolve the platform, suggests Cheung. When it comes to actual investments in technology, organizations might want to think in small pieces, investing in a variety of interoperable technologies rather than an inflexible, monolithic system.

A fully integrated platform

A holistic approach makes it possible to build a fully integrated platform to support data science and AI. Cheung notes that such a data and AI platform can play a key part in supporting the culture, process, and people within the organization and a meaningful conversation around the same view on data. This is the reason why enterprises are moving to data virtualization, establishing a central place where all datasets can be accessed.

Crucially, such an approach allows a compliance policy to be seamlessly and cohesively applied across the organization instead of a piecemeal approach. Changes or updates to the data policy can hence be easily enforced without having to delve into individual databases or tables, ensuring personally identifiable information (PII) and data assets stay secure.

In closing, Cheung cautioned that data and AI tools are merely enablers to innovation, and not the solution: Successful transformation and becoming more agile and innovative is a cultural and process change. The company needs to say: I want to become more agile in business, and I willleverage data and AI technology to achieve that.

Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at paulmah@cdotrends.com.

Image credit: iStockphoto/Jeff_Dotson

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Harnessing Data and AI to Crest the Innovation Wave - CDOTrends