Innoplexus AG
| File:Innoplexus.png | |
| ISIN | 🆔 |
|---|---|
| Industry | |
| Founded 📆 | 2011 |
| Founder 👔 | |
Area served 🗺️ | |
Key people | |
| Products 📟 | |
| Members | |
Number of employees | 250 |
| Divisions | Life Sciences, Financial Services, Legal |
| Subsidiaries | Innoplexus Holdings, Inc., Innoplexus Consulting Services Pvt. Ltd. |
| 🌐 Website | innoplexus |
| 📇 Address | |
| 📞 telephone | |
Innoplexus AG is a German artificial intelligence and blockchain company that specializes in life science-related products designed primarily for the pharmaceutical and biotechnology industries, which include a life science search engine, ontology-based network analytics, sentiment analytics and business intelligence, and clinical trial and regulatory tools. Innoplexus is considered one of the most innovative artificial intelligence companies in healthcare.[1] Innoplexus is also a member of NVIDIA's Inception Program,[2] an accelerator program that supports exceptional artificial intelligence startups, providing them with expertise and GPU hardware necessary to improve their technologies.[3] Innoplexus has also designed tools for the legal and financial services industries. Innoplexus's mission is to democratize artificial intelligence, providing solutions to companies that would not otherwise be able to build such systems themselves or to companies that want to augment their existing in-house infrastructure in order to accelerate pharmaceutical innovation.
History
Innoplexus was founded in 2011 by Gunjan Bhardwaj and Gaurav Tripathi in Frankfurt, Germany with funding from German venture capital firm HCS Beteiligungsgesellschaft.[4] Innoplexus also established an office in Pune, India. In 2017, Innoplexus raised a Series A funding round from Apeiron Investment Group.[5] In January 2018, Innoplexus opened its North American flagship office in Hoboken, New Jersey. Former Merck KGaA executive, Lawrence Ganti, was appointed as CEO of the Americas.[6]
Technology
Innoplexus has routinely been cited as an industry leader in the application of artificial intelligence in the life sciences by Gartner in 2017 and 2018.[7] Innoplexus's technology stack includes MongoDB, Elasticsearch, and ArangoDB. Furthermore, Innoplexus is partnered with ArangoDB,[8] Hortonworks, NVIDIA, Google Cloud,[9] and Amazon Web Services. Innoplexus currently has 10 patent applications pending in the United States.[10]
Data Crawling & Aggregation
Innoplexus uses machine learning to actively crawl public web domains for life science data. Additionally, Innoplexus aggregates diverse data types from thousands of structured, semi-structured, and unstructured life science databases such as PubMed, EMBL-EBI, and DrugBank. Together, this produces a real-time data ocean containing over 35 million scientific publications, 500,000 clinical trials, 833,000 scientific congress proceedings, 26 million patents, 5 million grants, 1 million theses and dissertations, 73,000 drug profiles, 40,000 gene profiles, 10,000 news organizations, 35,000 treatment guidelines, and 20 million authors. This data ocean encompasses approximately 97% of all public life science data available.[11]
Data Extraction & Computer Vision
Crawled and aggregated structured, semi-structured and unstructured data are then extracted from their respective sources using optical character recognition (OCR) and layout analysis which uses machine learning to understand document layout and structure data into a machine-readable format.
Biomedical Ontology
Innoplexus's technology infrastructure is built upon a biomedical ontology created by combining 200 public life science domain ontologies such as Reactome, MeSH, and UMLS. Machine learning algorithms have been built into the ontology such that when introduced to novel biomedical concepts, the ontology automatically assimilates the new term into the existing ontology and connects the relevant data assets to existing data effectively creating a self-learning biomedical ontology. This biomedical ontology consists of over 20 million concepts related to adverse events, cell lines, approved and experimental drugs, diseases, laboratory and medical devices, genes, proteins, pathways, mechanisms-of-action, reagents, compound toxicities, medical procedures, and others. This biomedical ontology also assigns causal relationships between these entities such that users can perform network analysis to determine cross-talk between biomedical pathways.
Natural Language Processing & Named Entity Disambiguation
Because life science information contains a massive lexicon of abbreviations, nuanced distinctions, and duplicated terms, natural language processing, which is simply a tool for interpreting standard language, is alone insufficient to be able to correctly understand the distinction between Epidermal growth factor receptor and Estimated glomerular filtration rate, both abbreviated EGFR, in science literature. Additionally, natural language processing is unable to discern whether EGFR is referring to a gene or a protein. In order to disambiguate and tag biological entities appropriately, the biomedical ontology is used to determine in which instance is Epidermal Growth Factor relevant and not Estimated Glomerular Filtration Rate. This process also enables sentiment analysis of life science domain information such as patient forums. Whereas industry standard text sentiment tools such as Google Sentiment API are adept at assigning sentiment to everyday text, these tools fail when applied to life science related language.
Products and services
Ontosight
The foundation of Innoplexus's technology infrastructure is Ontosight, which is a life science intelligence engine that allows users to query the biomedical ontology and retrieve data assets closely associated with the user's search term. This is a concept-based search platform, which is distinct from a simple keyword search engine that powers existing search platforms and returns results that are the most relevant to the user's search even if the given search term is not present in the resulting data. This concept-based approach is made possible through the mapping of causal relationships between biomedical terms and concepts, allowing the platform to make logical jumps to other closely related concepts in the ontology. In addition to search functionality, users can create real-time custom dashboards for a variety of user-defined use cases relevant across drug development process. Ontosight was rebranded in 2018 from iPlexus. As of 2019, Innoplexus has made a version of Ontosight available for free to academic researchers with a .org or .edu email address.[12][13]
OntoXplore
Whereas Ontosight allows users to query the biomedical ontology for specific terms, OntoXplore provides users with the ability to visualize and navigate the ontology itself, assaying specific relationships between biological entities. This is particularly useful when applied to biomarker identification or novel drug target identification.
References
- ↑ Flores, Mona. "8 Startups Ahead Of The Pulse In Healthcare". Forbes. Retrieved 2019-03-07.
- ↑ "Innoplexus has been accepted to NVIDIA Inception Program". Innoplexus German. Retrieved 2019-03-07.
- ↑ "NVIDIA Inception Program: A Deep Learning and AI Startup Accelerator". NVIDIA. Retrieved 2019-03-07.
- ↑ "German VC fund HCS backs Innoplexus". VCCircle. 2016-12-22. Retrieved 2019-03-07.
- ↑ "Innoplexus". Crunchbase.
- ↑ Innoplexus. "Innoplexus Announces New North American Affiliate Helmed by Former Merck KGaA Executive". www.prnewswire.com. Retrieved 2019-03-07.
- ↑ "INNOPLEXUS INCLUDED AGAIN IN GARTNER HYPE CYCLE FOR LIFE SCIENCES". Innoplexus. Retrieved 2019-03-07.
- ↑ "ArangoDB - One database to rule them all for Innoplexus". ArangoDB. Retrieved 2019-03-07.
- ↑ "Innoplexus Case Study". Google Cloud. Retrieved 2019-03-07.
- ↑ "PreGrant Publication Database Search Results: Innoplexus in PGPUB Production Database March 15th - September 30th 2001". appft.uspto.gov. Retrieved 2019-03-07.
- ↑ "Life Science AI Products & Solutions". Innoplexus. Retrieved 2019-03-07.
- ↑ "Ontosight". ontosight.com. Retrieved 2019-03-07.
- ↑ "Ontosight Academics". Innoplexus. Retrieved 2019-03-07.
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