RYNKL, a Tool for Evaluating the Effectiveness of Anti-Aging Skin Treatments, Receives Funding through Kickstarter, Launches on Android

Youth Laboratories, a company dedicated to human aging research, is launching RYNKL, a mobile application aimed to detect and track wrinkles in order to evaluate various anti-wrinkle treatments and anti-aging interventions for Android OS on Google Play Store. The app was fully funded via a crowdfunding campaign on Kickstarter and received broad critical acclaim.

To help develop and popularize the app and also to create a platform to test multiple approaches to evaluating human attractiveness, the team launched Beauty.AI, the first online beauty pageant with an all-robot jury. Some of the algorithms are developed by Konstantin Kiselev, the team’s deep learning lead, but some are provided by global modelling agencies and academic artificial intelligence teams.

Beauty.AI was covered by Cosmopolitan, Yahoo! Beauty, GQ and many other major news outlets significantly increasing the number of participants and expanding the training sets for RYNKL. 

To receive feedback, develop a team of beta-testers, fund testing and marketing efforts, the team had decided to crowdfund the campaign via Kickstarter. In just 25 days into the campaign, the app was fully-funded and received positive critical acclaim with some of the most advanced healthcare and beauty clinic networks enthusiastically supporting the initiative. 

“We are witnessing the most exciting time in the history of humanity, when some technologies previously deemed as far fetched, are quickly becoming reality and rapidly propagate into consumers hands. The beauty and the pharmaceutical industries are clearly lagging when it comes to delivering truly innovative and disruptive products. RYNKL is one of the first tools we developed to help the beauty industry empower and engage the customer in product development and personalization. My goal is to develop a broad ecosystem for studying aging from multiple perspectives and developing effective interventions that can slow down or repair the age-associated processes. RYNKL will play an important role in this ecosystem and will allow us to validate some of the predictions made using human gene expression data”, said Alex Zhavoronkov, PhD, Chief Science Officer of Youth Laboratories and CEO of Baltimore-based Insilico Medicine, Inc.

One of the leading European health and beauty clinic networks, Klinikk Oslo in Norway, dedicated to extending productive longevity, has supported the development of the app and will be launching series of applications based on RYNKL and Beauty.AI platforms.

"Beauty and healthcare should be personalized and effective. Customers have the right to demand more effective anti-aging treatments and we want to lead the revolution in personalized care. Many of our customers don't have the time to wait and we monitor and support cutting-edge research in aging and longevity starting from simple mobile apps to research-intensive interventions like preventative anti-aging approach, SENS", said Olav Espeland, CEO of Klinikk Oslo.

The beta version of RYNKL is available in Google Play, and it will also appear in AppStore in February, 2016. At the moment Youth Laboratories is also developing a set of user-friendly mobile applications to track pimples, skin pigmentations, allergy reactions and other facial imperfections.

“My personal life’s goal is to combat aging and develop effective interventions to extend healthy productive longevity and prevent age-related diseases. But most people consider aging as a natural process and believe that it can not be suspended. But they do care about their looks and whether the money they spend on expensive cosmetics produce noticeable results. RYNKL will help with these tasks and in time turn into a very useful assistant dedicated to preserving your face and your health”, said Polina Mamoshina, advisor to Youth Laboratories and a skin aging and photo aging researcher at Insilico Medicine, Inc. 

“Machine learning, image and face recognition technologies are changing many aspects of our daily lives. We decided to utilize these technologies for personalized healthcare and anti-aging treatments. Many of our colleagues inspired by the book “Ageless Generation” which presents aging as the most pressing economic challenge and the most altruistic cause, are relentlessly developing new approaches for using mobile devices and wearable technology to advance research in aging. Our first app, RYNKL, helps track wrinkles and tests efficiency of various interventions including cosmetic products, skin care procedures, diets and invasive interventions. This app is already being tested by the the most innovative beauty practitioners, cosmetics companies and influential beauty bloggers”, said Alexey Shevtsov, CEO of Youth Laboratories.

About Youth Laboratories 

Youth Laboratories is a team of computer and data scientists, biologists, biogerontologists and business people on a quest to develop novel biomarkers and anti-aging interventions. Earlier Youth Laboratories helped to launch the first beauty contest to be judged by artificial intelligence ‒ http://www.Beauty.AI .

About Klinikk Oslo 

Klinikk Oslo is a famous Norwegian network of cosmetic clinics with customers from all over Europe, who are happy to travel quite far to be treated by its most competent experts. Klinikk Oslo offers a wide range of cosmetic procedures performed by the most skilled and experienced medical practitioners in the industry: dermatologists, surgeons, laser specialists and cardiologists. The clinic is a proven leader in implementing innovative technologies into daily healthcare practice.

 

InSilico Medicine presents advances in deep learning for drug discovery and aging research

InSilico Medicine will present an update on recent advances in applying signaling pathway activation analysis and deep learning to drug discovery and drug repurposing for age-related diseases at the Biotech Showcase in San Francisco.

Aging research is a very controversial area with many failed claims and promises and in order to establish the proof of concept of its approach, InSilico Medicine decided to build a sustainable business model providing pathway activation analysis and machine learning services to pharmaceutical companies, academic and clinical institutions helping drive innovation in many areas of research.

"Our mission is to extend healthy human longevity and we spent several years developing a sustainable model to enable the company to effectively use massive heterogeneous biological data sets for drug discovery, biomarker development and personalized medicine. With over 20 academic publications published in 2015 and over 150 collaborators worldwide, we are very excited to present our results that will lead to faster and more effective diagnostics and cures in many age-related diseases", said Alex Zhavoronkov, PhD, Chief Executive Officer of InSilico Medicine, Inc and Chief Science Officer of the Biogerontology Research Foundation.

Deep learning technologies resulted in major breakthroughs in image, voice and text recognition, autonomous driving, networking and mobile applications surpassing human accuracy and demonstrating unprecedented results. However, in genomic and transcriptomic analysis, drug discovery and repurposing and many other areas of biomedicine, progress has been much slower. Since November 2014 InSilico Medicine decided to shift its focus on and commit to deep learning technologies despite many early failures showing superiority of other machine learning techniques.

"When it comes to training deep neural networks with transcriptomic and other types of biological data many groups notice the superiority of advanced implementations of SVM, GBM and other machine learning methods. However, there is much to be learned from the inaccuracies of the DNNs that will lead to both new methods to working with biological data and provide clues on alternative uses of drugs particularly when it comes to age-related diseases. Several of our collaborators in the pharmaceutical and cosmetics industries are already testing these methods and I am happy to be part of the team that will establish more of these collaborations during the J.P. Morgan conference", said Polina Mamoshina, research scientist, InSilico Medicine.

About InSilico Medicine, Inc
InSilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery and provides services to pharmaceutical companies. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8

Alexey Shevtsov

Alexey Shevtsov, creator of RYNKL, the app, which helps you track the effectiveness of any facial cream or other anti-aging intervention. The biogerontology community is growing fast and we are likely at the point of no return with so many life-extending interventions in the pipeline.
If you would like to put the claims of cosmetics companies to the test, please support our team on Kickstarter.

RYNKL, wrinkle analysis app

My dear friends, I need your help.This team launched a crowdfunding campaign for a new anti-aging application. Can you donate a buck or ten to make this project happen? Every dollar counts!
I have friends here, if every one donates one dollar and we get one research collaborator, the project will fly.
Here is the link:

GPU-Accelerated Deep Neural Nets Look for Cures that Already Exist

Discovering cures for cancer, for Alzheimer’s, for multiple sclerosis, for Parkinson’s, for the halting and reversing of aging itself, may not require the development of new drugs. It may mean discovering properties and therapies in drugs already developed and used for other diseases.
That’s the principle driving bioinformatics start-up Insilico Medicine, a Baltimore-based company utilizing GPU-accelerated NVIDIA advanced scale computing to power deep learning neural nets using massive datasets for drug repurposing research that targets aging and age-related diseases.
Drug re-targeting is not new. One of the best known cases is rapamycin, a drug originally thought to be an antifungal agent before it became widely used in in organ transplantation and then as a cancer fighter. Other companies have pursued drug re-purposing as a development strategy, but Dr. Alex Zhavoronkov, Insilico CEO, said his company using big data analytics to scale the strategy to a level never previously attempted.

Insilico researchers not only generate their own data, they ”scavenge” existing datasets that pharmaceutical companies and research institutions have retired because they were too small, in themselves, to provide much research value. Aggregated and analyzed, the data is providing Insilico, its pharmaceutical partners and physicians with insights into how medications designed and approved for one ailment can be redirected to attack another.

Dr. Alex Zhavoronkov of Insilico

“We’ve found a way to suture together our data with many other databases,” said Zhavoronkov, “and then it starts making sense.” Altogether, Insilico has 3 million gene expression samples amounting to hundreds of terabytes of data. “The breakthrough is combining so many pieces of the puzzle in one particular place,” he said, explaining that Hadoop has been instrumental to harmonizing large amounts of unstructured, weakly related data, and then running Insilico’s drug scoring algorithms against it.
Of course, drug discovery is an endeavor prone to high hopes and false starts. Many new drugs are found early in the development process to have toxicities that cause unacceptable side effects. But Insilico’s approach has the advantage of focusing on 20,000 medications worldwide already in use, drugs that have been approved (either in the U.S. or in other countries) and whose side effects are known. Although Insilico’s anti-aging agenda may trigger skepticism, its basic methodology has drawn positive attention from investors (Deep Knowledge Ventures, Hong Kong), industry analysts and pharmaceutical companies.



In February 2015 at the Personalized Medicine World Conference in Mountain View, CA, Insilico was recognized as the “Most Promising Company” in the fields of human genetics and personalized medicine. In March, Insilico was one of 12 finalists selected to present at the Early Stage Challenge at NVIDIA’s 2015 GPU Technology Conference. In partnership with Novartis last September, Insilico organized an international aging forum at Basel Life Science Week in Switzerland. The company also launched bioinformatics research partnerships with ATLAS Generation (stem cell research), Vision Genomics (ocular diseases); Pathway (cancer research); and Canada Cancer and Aging (personalized medicine and aging research). And the company said Insilico research papers have been published in 50 peer-reviewed journals over the past two years.

Insilico has configured four NVIDIA DevBox desktop supercomputer, using TESLA K80 GPU accelerators and four Titan X graphics cards, for a total of 28TF of processing power.
NVIDIA GPUs are the foundational technology driving deep learning techniques used by Insilico to compare healthy and diseased tissues, as well as aged and young tissues, and then to test – in digital formats – the impacts of drugs on those tissues to restore them to health and youth.
Zhavoronkov said Insilico is experimenting with many flavors of deep neural nets as well as deep learning combined with more traditional research and testing methods. This includes deep feed forward neural nets using different data types as inputs, stacked autoencoders for cross-platform data harmonization, deep belief nets for drug scoring and, ultimately, drug repurposing.

While deep neural net concepts have been around for decades, a revolution in their use started around 2010 when deep learning systems were trained on large image datasets, initially achieving – and then surpassing – the image recognition capabilities of humans. Application of deep learning to genomics and drug discovery has been slow because training systems to enable algorithms to work with “multi-omics” and patient data requires the use of databases on such a massive scale. Insilico developed methods to augment its proprietary gene expression and proteomic data using Hadoop and other methods to harmonize and compare data from different sources and turn it into usable pathway activation profiles that can be used by deep learning algorithms. In so doing, the company has created biomarkers for cancer, Alzheimer’s and other diseases.

The results include:

• DeepPharma, a GPU-based visual computing platform for creating virtual cells, tissues, bodies, and even virtual populations. These virtual laboratories are used is to simulate and test tissue-specific pathway activation – also called “net signaling drift” – measuring the effects of millions of compounds on the molecules within diseased or aged cells.

• OncoFinder, a personalized medicine decision-support tool that has been used by physicians, mostly in Europe and Asia, to help identify drug treatments for more than 800 patients

“When you’re using deep learning in bioinformatics your only option today is GPU computing,” Zhavoronkov said. “Deep neural networks are evolving and revolutionizing many aspects of our daily lives – in pictures in videos in voice. GPU computing is becoming much more available and more databases, with millions of samples, also are becoming available. So success in deep learning is primarily centered around two factors: being able to utilize the full power of GPU computing, and access to huge databases.”

Development of the analytical algorithm used in OncoFinder took about six months, Zhavoronkov said, while another 18 months of virtual clinical trials was needed to test its predictive capabilities on retrospective data to validate its use in clinical settings.

Insilico is not required to undergo FDA or other regulatory approvals because OncoFinder is not used for diagnostics, Zhavoronkov said. Rather, it is a research service and decision support tool that helps doctors select medications that may be most effective in treating patients’ diseases.
Zhavoronkov said one of his greatest challenges has been assembling a staff combining expertise in machine learning, human genetics and pharmacology – particularly since deep learning is new to genomics research. “Finding talent that is qualified to experiment with deep learning applied to gene expression data is very difficult,” he said, “because you need people who are good with math and programming but also understand the biology. There are few people with this range of skills, so it’s a very precious resource.”

One of Insilico’s first aging-related projects is researching the process of skin aging. This involves “digitizing” both the net signaling drift between young and old skin tissue and then virtually testing digitized forms of old skin for drugs that correct this difference. The project includes investigation of the impact of ultraviolet irradiation (sunlight) of skin.

Zhavoronkov said Insilico has predicted the first compounds that may ameliorate the skin aging process and will announce its findings next year. Insilico also plans to partner with a company that “measures” facial wrinkles, which would then be used to assess the effectiveness of anti-aging medications identified by Insilico tools.

“Our first frontier is human skin,” Zhavoronkov said, “if you can successfully treat skin aging you can basically apply the principle to other tissues.”

The first M&A in aging bioinformatics

Insilico Medicine and InSilicoScreen merge to take human aging research to the next level

"The most valuable assets in bioinformatics companies are its people and at Insilico Medicine we constantly hire top talent through hackathons, challenges and competitions. Quentin Vanhaelen is an army of one who has developed a vertically-integrated system which synthesises data, performs complex simulations using manually-curated and automatically-annotated databases of molecular interactions primarily in Fortran, and produces impressive LaTeX reports. We are happy to have him join our team", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

Since 2013, under Dr. Vanhaelen's leadership, scientists at InSilicoScreen have assembled large databases of kinetic protein interactions and built sophisticated signal transmission, regulation of cell metabolism, autophagy, cell cycle and other models. The sole aim was to build models of human aging and regenerative processes to find new interventions and test the current ones on multiple levels from single cells to the entire organs. The company developed a service-oriented business model and engaged with multiple academic and commercial groups worldwide.

"I have known Insilico Medicine for over one and a half years and they were always under the spotlights of innovations, but I realized that they are very focused and with my models they can use their real human data and biologically-relevant pathway activation algorithms with my time-series simulations for many applications, including deep learning. I really like working with the team and will spend my time between Baltimore, Brussels and Moscow", said Quentin Vanhaelen, PhD, founder and ex-CEO of InSilicoScreen.

Aging research is a very altruistic endeavor, but building a sustainable business model is often challenging. As part of its business model, Insilico Medicine provides contract research, data quality analysis, cross-platform harmonization, pathway activation analysis, biomarker development, clinical trials pipeline review and scoring, portfolio review, and drug scoring and drug repurposing services to large pharmaceutical and cosmetics companies, hedge funds, academic institutions, and other organizations with large biological or pharmaceutical databases.

"We are very happy that our flagship portfolio company has expanded its scientific leadership with another eccentric, but amazingly brilliant theoretical physicist-turned systems biologist, who managed to build surprisingly sophisticated models of human biological networks that are fit for dynamic simulations" , said Dmitry Kaminskiy, senior partner of Deep Knowledge Ventures.

About Insilico Medicine, Inc
Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery and provides services to pharmaceutical companies. Brief company video:
https://www.youtube.com/watch?v=l62jlwgL3v8

 

 About InSilicoScreen
 
InSilicoScreen is a Belgian aging bioinformatics company based in Brussels. It provides clients in the pharmaceutical industry and academia with a reliable and specific information about the global dynamic of the cellular networks by combining the efficient numerical and mathematical methods together with an in-depth understanding of the underlying molecular processes.
http://www.InSilicoScreen.eu

The Future of Biotech Enterprise: Exponential Opportunities and Existential Risks

The future of biotechnology has many opportunities to create abundance, but also many risks for destruction. Dmitry Kaminskiy will be speaking with Professors Chris Lowe and Derek Smith about exponential opportunities and existential risks. This is certain to be a valuable and enlightening discussion!



Bioscience technologies have the power to build or destroy a world of health and abundance. Leveraging entrepreneurial opportunities whilst avoiding catastrophic risk is a balancing act with potentially fatal consequences.

Guest Speakers include:

Professor Chris Lowe OBE, FREng, FInstP, FRSC
- Emeritus Professor, Chemical Engineering and Biotechnology, University of Cambridge
- Awarded ‘most entrepreneurial scientist in the UK’, with research recognised by over 20 major national and international awards

Professor Derek Smith
- Professor of Infectious Disease Informatics, University of Cambridge
- Director of WHO Collaborating Centre for Modelling, Evolution and Control of Emerging Infectious Diseases

Dmitry Kaminskiy
- Founder and investor of Deep Knowledge Ventures, Deep Knowledge Life Sciences, OncoFinder, Insilico Medicine, Exponential Technologies Institute, and iBank.
- Developer of artificial intelligence systems VITAL, and SPOCK. Director of the Biogerontology Research Foundation

Refreshments will be provided after the talk.

Sponsored by: Deep Knowledge Ventures - Life Sciences, and BGRF

In collaboration with: Cambridge University Entrepreneurs, Centre for the Study of Existential Risk, Centre for the Advancement of Sustainable Medical Innovation, and Cambridge University Department of Chemical Engineering and Biotechnology

The event will take place on  december 2 in The Queen's Lecture Theatre, Emmanuel College, Cambridge.