Insilico to present the advances in deep-learned multimodal biomarkers of aging at NCI
Tuesday, February 6th, 2018, Baltimore, MD – Insilico Medicine, a Baltimore-based company specializing in artificial intelligence for drug discovery, biomarker development and aging research will present a lecture on deep-learned multimodal biomarkers of aging at the Cancer Biomarkers Data Commons Meeting (CBDC) Think Tank Meeting, February 8th 2018, at the National Cancer Institute. The event is open to the public with prior registration.
Dr. Zhavoronkov's lecture "Deep-learned multimodal biomarkers of aging" will cover the latest advances in artificial intelligence for development of aging biomarkers. The session will focus on the machine learning approaches used for aging biomarker development and their potential application to the cancer biomarker discovery.
"Assessing the biological age of the patient using multiple data types may significantly contribute to personalization in the many areas of medicine primarily in immuno oncology. People, their organs and systems age at different rates and adjusting the therapy to the biological age of the patient may help improve outcomes in clinical trials and in the real world. The deep neural networks trained to predict the biological age of the patient may be used to discover novel targets and pathways in aging and age-related diseases", said Alex Zhavoronkov, PhD, the founder and CEO of Insilico Medicine, Inc.
The Think Tank Meeting on the Development of a Cancer Biomarkers Data Commons will bring together thought leaders from academia, industry, and government to discuss approaches to the development of cancer biomarkers. The event sets up the objectives to examine clinical and research needs in cancer biomarker discovery, discuss machine learning and statistical approaches to biomarker discovery and explore bioinformatics strategies to transform Big Data into FIT (fit-for-purpose) Data.
"The early diagnosis and prognosis of a cancer have become a necessity in cancer research, as it can facilitate the more effective and accurate decision making in clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of integrative high-throughput computational analyzed and machine learning methods. Although it is evident that the use of these methods can improve our understanding of cancer progression, there are still significant challenges and limitations associated with computational modeling human cancer. However, with the dismal success rate seen in clinical trials, and as the current technical limitations deep-learning are overcome, the value of these tools in addressing the continuing challenges in clinical oncology will grow", said Eugene Izumchenko, PhD, Head and Neck Cancer Research, Department of Otolaryngology, Johns Hopkins School of Medicine.
Insilico Medicine is responsible for the many "firsts" and proofs of concept in the application of deep learning to drug discovery and biomarker development. It was the first to apply the deep generative adversarial networks (GANs) to the generation of new molecular structures with specified parameters and published seminal papers in Oncotarget and Molecular Pharmaceutics. Another paper published in Molecular Pharmaceutics in 2016 and demonstrated the proof of concept of the application of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data, received the American Chemical Society Editors' Choice Award. One of the recent papers published in November 2017 described the application of the next-generation AI and blockchain technologies to return the control over personal data back to the individual. The latest paper published in the Journals of Gerontology demonstrated the application of the deep neural networks to assessing the biological age of the patients.
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Contact: Qingsong Zhu, PhD
About Insilico Medicine, Inc
Insilico Medicine, Inc. is an artificial intelligence company headquartered at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore, with R&D and management resources in Belgium, Russia, UK, Taiwan and Korea sourced through hackathons and competitions.
The company utilizes advances in genomics, big data analysis, and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. Insilico pioneered the applications of the generative adversarial networks (GANs) and reinforcement learning for generation of novel molecular structures for the diseases with a known target and with no known targets. In addition to working collaborations with the large pharmaceutical companies, the company is pursuing internal drug discovery programs in cancer, dermatological diseases, fibrosis, Parkinson's Disease, Alzheimer's Disease, ALS, diabetes, sarcopenia, and aging. Through a partnership with LifeExtension.com the company launched a range of nutraceutical products compounded using the advanced bioinformatics techniques and deep learning approaches. It also provides a range of consumer-facing applications including Young.AI and Aging.AI and operates Chemistry.AI intended to capture the tacit knowledge of medicinal chemists.
Through a partnership with the BitFury Group, the company is working on a range of AI solutions for blockchain to help return the power over life data back to the individual. The company raised venture capital and partnered with Juvenescence Limited, a holding company focused on longevity biotechnology. The company aspires to become the "Bell Labs" for artificial intelligence and associated technologies for healthcare and longevity biotechnology and commercialize its research by forming subsidiaries around the specific technologies and licensing the intellectual property, molecules and data to the biotechnology and pharmaceutical companies. In 2017, NVIDIA selected Insilico Medicine as one of the Top 5 AI companies in its potential for social impact. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8