AUSTIN, TX Katana Graph, the AI-powered Graph Intelligence Platform providing faster, deeper, and more accurate insights on massive and complex data, demonstrated the power of its analytics library at Intel Investor Meeting 2022, a gathering of Intel executives to announce the company's future growth strategy, product roadmaps, and key milestones. The demo used Katana Graph’s technology to execute a 1.3 million cell genomic analysis on a Next-Gen Intel Xeon Scalable processor in 370 seconds, twice as fast as its closest competitor.
We are building the best graph intelligence platform with the goal of making it easier for data scientists to perform predictive analytics on massive datasets.
Single-cell genomic analysis, which provides genetic insights at cellular levels by decoding gene expression to determine response to disease and drugs, is highly compute-intensive. Intel’s Single Cell Genomics Competitive Demo used Louvain clustering, a community detection algorithm implemented in Katana’s analytics library and optimized for Intel’s Xeon platform.
"We are building the best graph intelligence platform with the goal of making it easier for data scientists to perform predictive analytics on massive datasets," said Keshav Pingali, Katana Graph CEO. "Combined with the power of Intel's hardware, our platform reduces the time to discovery while lowering the cost of making these discoveries. I am proud of Katana Graph's partnership with Intel as we tackle the toughest pain points of data scientists, enabling critical discoveries in the field of genomics."
The demonstration of the power of Katana Graph’s Analytics Library at Intel Investor Meeting 2022 comes on the heels of Katana’s success in the Therapeutics Data Commons’ (TDC) molecular property predictions competition in which Katana’s innovative graph machine-learning algorithm solved twenty-two problems, ranking first in eight problems and second in two other problems, to take the first place in the leaderboard for the TDC competition.
Katana Graph is also partnering with Intel to create and launch a high-performance graph analytics Python library for the benefit of data scientists and the growth of the open core community.
