The Reason Founding Team
Dmitry Chebanov, PhD
Cancer & Aging Scientist | Researcher in Computational & Systems Biology | Biohacking Enthusiast | Serial Biotech Entrepreneur
Dmitry started and quickly developed his career in finance. By 2014, he was the CFO of Airi Ventures HK, gaining insight into biotech companies and successful startups. Inspired, he launched a startup with a clinical genetics expert and pursued a bioinformatics course at Peking University. His first peer-reviewed article in "Oncogynecology" earned him recognition among oncologists.
Dmitry joined the N.N. Blokhin Russian Cancer Research Center, co-authoring several groundbreaking articles. In 2015, he founded the Bioinformatics Department in the Laboratory of Genetics and Epigenetics in PFU. From 2015 to 2019, he actively engaged in research and teaching, joining the American Association of Clinical Oncology in 2017.
In 2017, Dmitry began a postgraduate program at the Computational Center of the Russian Academy of Sciences, focusing on predicting remission or recurrence in oncology patients. He earned his Ph.D. in December 2021.
In 2016, Dmitry founded OncoUnite, developing a diagnostic platform for cancer treatment selection. The company used AI and machine learning to process molecular-genetic data. In 2018, he developed the award-winning OncoUnite tumor genomic profiling test. By 2019, OncoUnite was a top European project and won the Founder Institute accelerator in Boston.
OncoUnite became a market leader, conducting over 4,000 studies and surpassing international competitors. In 2020, it received a Microsoft grant and partnership.
In 2023, Dmitry received a U.S. visa for extraordinary abilities and moved to New York. He joined Memorial Sloan Kettering Cancer Center, earning a Doctor of Science degree and starting a post-doctoral fellowship. There, he uses deep learning and molecular-genetic data to predict patient outcomes and develop new treatments for cancer. He currently researches anti-aging interventions with proven scientific effect and works on the human enhancement protocols that help people live longer, more fulfilled years.
Dmitry’s scientific work:
An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666046/
Predictive Modeling of Clinical Trial Outcomes for Novel Drugs using Digital Twin Patient Cohorts and GenerativeAI: https://www.medrxiv.org/content/10.1101/2023.09.11.23295380v1
Application of large language models to nucleotide sequences for profiling signaling pathway disruptions in ovarian cancer patients: https://aacrjournals.org/cancerres/article/84/6_Supplement/3522/741127
Deep learning to predict the effect of drugs in patients with lymphoma based on cell line data: https://onlinelibrary.wiley.com/doi/full/10.1002/hon.82_2881
Modeling of new drugs clinical trials outcome with patients’ digital twins cohorts: https://aacrjournals.org/cancerres/article/84/5_Supplement_2/B023/734555
Machine learning for predicting overall survival using whole exome DNA and gene expression data and analyzing the significance of features: https://aacrjournals.org/clincancerres/article/27/5_Supplement/PO-045/32829/Abstract-PO-045-Machine-learning-for-predicting
Identifying actionable pathway malfunction scores with ML algorithm for omics data: https://aacrjournals.org/cancerres/article/80/8_Supplement/A32/647641/Abstract-A32-Identifying-actionable-pathway
Machine learning for predicting overall survival using whole exome DNA and gene expression data and analyzing the significance of features: https://aacrjournals.org/clincancerres/article/27/5_Supplement/PO-045/32829/Abstract-PO-045-Machine-learning-for-predicting
Method for predicting the effectiveness of the developed immune dendritic cell vaccine in melanoma patients based on cell surface antigens and machine learning with non-classical logic:https://aacrjournals.org/cancerimmunolres/article/9/2_Supplement/PO086/470130/Abstract-PO086-Method-for-predicting-the
Intellectual Mining of Patient Data with Melanoma for Identification of Disease Markers and Critical Genes: https://link.springer.com/article/10.3103/S0005105519050066
Probabilistic statistical-based model for determining individual cancer risk for making personal recommendations to prevent cancer: https://aacrjournals.org/cancerres/article/82/23_Supplement_1/A032/710777/Abstract-A032-Probabilistic-statistical-based
Deep learning-driven drug discovery: A breakthrough algorithm and its implication in lung cancer therapy development: https://aacrjournals.org/mct/article/22/12_Supplement/A014/730382
Katya Skaya
Serial Consumer founder | Media Influencer | Founders Network Director
Katya Skaya (aka Ekaterina Romanovskaya) is a three-time founder and media influencer. She began her career in finance and by 2008 became a strategy consultant for private equity, IPOs, and M&A deals in emerging markets. In 2010, she co-authored a social media project that amassed 2 million followers, leading to her roles as an observer and interviewer for Forbes and GQ Magazine.
In 2016, Katya co-founded Nimb, an international tech startup that enables users to send panic signals to friends, family, and first responders. Nimb has been featured in TechCrunch, Business Insider, Financial Times, Reuters, Associated Press, Daily Mail, and The Guardian. In 2017, the Washington Post named her one of five women changing the world.
In 2021, she co-founded A.ID, a compliance platform for high-risk customers. By 2023, Katya co-founded House of Pitch, a platform connecting early-stage founders with investors.
Additionally, in 2023, she was elected LA Chapter Director for the Founders Network, an organization where first-time founders receive mentorship and guidance from their peers.