Elucidata’s MLOps platform boosts data quality for drug discovery

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From younger startups to main conglomerates, virtually each group within the life sciences business is AI to drive R&D and provide you with novel medication and therapies. The effort revolves round coaching predictive fashions on large datasets pertaining to the issue at hand. But, for many organizations, gathering high-quality data continues to be a significant drawback.

Essentially, most scientists must work with multi-omics, bio-assays, scientific, EHR and different types of biomedical data which might be normally saved in a number of programs inside their organizations or sourced externally. This type of data is just not solely siloed but in addition so various that it turns into tough to base an correct predictive mannequin upon it.

“Deciphering insights from biomedical data is at the heart of addressing the world’s most important breakthroughs in biopharmaceuticals,” Ashish Venkataramani, associate at Eight Roads Ventures, notes. “There is an explosion in the generation of these complex, heterogeneous datasets, driven by innovations in sequencing technologies and the proliferation of connected devices.”

Elucidata solves data quality problem

To assist life sciences organizations take advantage of this chance, with out worrying in regards to the heterogenous or siloed nature of data, Massachusetts-based Elucidata gives an MLOps platform known as Polly. The resolution offers R&D groups entry to wash, curated biomolecular data that may be queried and analyzed over a graphical person interface (GUI) or programmatically. Elucidata in the present day raised $16 million in a Series A spherical of funding.


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According to the corporate, Polly gathers, transforms and harmonizes data into standardized machine-readable codecs, enabling enterprises to make use of it for their machine studying purposes. The resolution has entry to 70TB of ML-ready biomedical data, or greater than 1.5 million datasets and 4.1 million samples, from greater than 30 public and proprietary sources. This covers greater than 26 organic data sorts.

The platform makes use of connectors to tug data from sources akin to TCGA and Gene Expression Omnibus (GEO). Then, it converts the data into flat recordsdata and provides ontology-backed metadata and labels to fine-tune it into curated biomedical data, prepared for machine studying. Finally, the cleaned data is made accessible within the buyer’s data warehouse.

“Organizations often underestimate the importance of data quality, and as a result, a lot of AI/ML initiatives are compromised. We’re on a mission to derisk such initiatives in life sciences R&D by empowering them with high-quality biomedical data at every stage of the R&D process,” Abhishek Jha, CEO and co-founder of Elucidata, stated. 


Elucidata claims to supply the flexibility to serve firms in varied levels of the drug discovery course of. In truth, the corporate says Polly has already seen adoption from over 30 life science business gamers, together with Genentech, Pfizer and Janssen in addition to analysis institutes like Stanford and the Bill & Melinda Gates Foundation. The platform can’t solely speed up the tempo of implementing AI initiatives but in addition cut back the time to downstream evaluation by as much as 70%, the corporate provides.

With this spherical of funding, which was led by Eight Roads Ventures, Elucidata will give attention to deepening its product capabilities in translational drug analysis and allied markets, scaling go-to-market initiatives and accelerating the worldwide enlargement of operations. 

“When it comes to AI projects, the quality of labeled data can play a vital role in the difference between success and failure,” Nihal Sinha, MD and associate at F-Prime Capital, which participated within the spherical, stated in an announcement. 

“Elucidata is providing life science companies access to high-quality datasets and, as a result, effectively accelerating their R&D efforts towards developing innovative solutions that can improve human health,” Singha added.

According to RBC Capital Markets, by 2025, the compound annual development price of data for healthcare will contact 36% — 6% sooner than manufacturing, 10% sooner than monetary providers and 11% sooner than media and leisure.

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