Introduction to Shivom
Healthcare data, when trapped in siloes, does not yield maximum value. Compartmentalizing information limits healthcare providers from making large strides in research and development, which ultimately puts a limit on the benefits that can be made available to the public. This is especially true for genomics data. Breaking up genomics data silos is of utmost importance because the value of genomics data silos is very limited in their current state, but when deployed in a way to aggregate genomes globally, a gain of value will be observed according to the law of accelerating returns rather than a law of diminishing returns.
For example, if individuals and medical centers around the world shared their genomic data fully, then as a whole, these data would be profoundly more valuable and useful. The opportunities to find patients somewhere in the world with unusual mutations and phenotypes, or individuals with disease-resistant mutations, would increase significantly, shifting precision medicine into the next gear. By combining genomic sequencing data with health records, researchers and clinicians will have a vast resource that can be interpreted to improve patient outcomes and also be used to investigate the causes and treatments of a disease.
For big data approaches to thrive, however, historical barriers dividing research groups and institutions need to be broken down and a new era of open, collaborative and data-driven science ushered in. There are several obstacles that made it difficult in the past to break down data siloes. Probably the biggest obstacle to using advanced data analysis on genomics data isn’t skill base or technology; it’s simply data access.
There is also a cost to using and producing data. If this cost is shared with as many stakeholders as possible, new data sources, which outperform older databases significantly can be built.
Another obstacle is the data generation itself. The genomes have to come from somewhere.
However, people often have limited access to their own data and even when they do, they have
only limited ways to use this data for improving their health or for sharing with third parties, e.g.
for scientific studies. So, what is needed is a platform to store data in a secure way, anonymized, impenetrable to malicious attacks or unauthorized access, but at the same time publicly accessible and searchable.
The data in the database must be usable, which means it must be easy to use for population health studies, pharmaceutical R&D, and personal genomics. Such a platform must be accessible on a global scale, offer data provenance and auditing features and should offer the possibility to be monetized by the data owners (patients and healthy donors).
Adding new genomic data to the platform must be trivial and accessible to everyone. Finally, participants must be incentivized to ensure traction. We will build such a platform using convergent technologies including Genomics, Blockchain and artificial intelligence, exactly those areas that were named in a statement by the European Commission as today's most promising breakthrough innovations.
Shivom Founder and CEO
Shivom Founder and CEO Dr. Axel Schumacher talks about the importance of personal data privacy, security and access, providing examples of cases where individuals may want to limit data access. This is made possible with state-of-the-art blockchain technology, cryptography, and encryption, and enables to link the platform to other apps and services which will be provided on top of the blockchain in an app marketplace
According to several market analyst companies, the global genomics market is estimated to reach over USD $22 billion by 2020, growing at an expected compound annual growth rate (CAGR) of 10-11%. The global genomics market has undergone an increase in its market potential due to technological developments. Factors including growing prevalence of fetal disorders such as diabetes and cancer, an increase in partial or full compensation by the government of certain countries add to the market’s growth across the globe. These market dynamics show that our genomics ecosystem comes at the right moment in history.
The growth of the overall genomics market can be attributed to increasing investments, grants, and funds by governments; increasing research in the field of genomics; increasing number of start-up companies; and increasing application of genomic sequencing in diagnostics. Interaction with the genomics ecosystem can come from many stakeholders in the genomics end user market, categorized into academic institutes, research centers, government institutes, nutraceutical companies, hospitals & clinics, pharmaceutical & biotechnology companies, cosmetic industry, agrigenomic companies, forensic agencies, and private individuals. Moreover, the genomics-based personalized medicine segment is projected to have the highest growth rate, at an anticipated CAGR of over 12-15% to 2020.
The high growth rate of the segment is reflecting the growing demand for population-based
therapeutic solutions and consequent increase in R&D initiatives. The Shivom ecosystem will not
only target the established ‘genomic’ economies (US and Central Europe), but will also serve the
whole global community, with a special focus on China and India. Indeed, Asia Pacific is likely to be the highest growing region, at a projected CAGR of 12.7% over the next six years. The presence of large unmet medical needs and the increasing demand for economical clinical outsourcing in the emerging nations of China and India is anticipated to drive the high growth rate.
Once people have their genome sequenced and uploaded on the Shivom platform, they will have access to various customized, health-related apps developed by Shivom and third parties.
Each individual will have the choice to only learn what they want about themselves. Sometimes, people want to know only aspects of their potential future, in particular information that has actionable consequences rather than information related to certain incurable and unpreventable diseases (e.g. Huntington’s disease). It is this type of information that some healthy, high-risk individuals prefer not to know. Using smart contracts in the Shivom platform, people can be guided in their learning process and they can easily decide if they want to learn about their risk of developing certain diseases. There is currently no consistent view on how inadvertent predictive testing (or incidental findings, which may or may not have health implications at some time in the distant future) should be managed, particularly when testing would be performed at birth or during pregnancy. Smart contracts on the blockchain will serve to better manage these choices and consequences.
A steadily growing collection of health- and lifestyle applications, ancestry information and other genome-based material will either be directly accessible in exchange for tokens from the users’ dashboard or will be made accessible via partner programs. The Shivom platform will be open for other service providers to add their genomics apps and services to the platform. Typical apps will relate to nutritional advice, taste perception, drug metabolism, caffeine or alcohol tolerance, behavior, physical appearance, ethnicity, ancestry and many more.
AI driven research
Shivom’s ultimate goal is to provide the best in healthcare for the individual, with blockchain technology increasing the speed in which precision medicine is implemented at a lower cost. One central part of it will be artificial intelligence-based diagnostic and drug development.
The human genomic machinery with all its facets is extremely complex, and we can safely assume that no human being will ever be able to comprehend even a small part of such a complex system. Fortunately, technology advanced to a point where scientists can tackle this problem. The solution is machine learning, where in contrast to lab scientists or physicians, computers look at very large anonymized data sets and use algorithms to classify and recognize features. Already, these algorithms can capture discriminative patterns from genomic data, and using these patterns for prediction hold promise to extract actionable information via data integration.
Machine learning is by far most effective in situations where one would like to make sense of huge, complex data sets. Accordingly, machine learning methods are likely to become ever more important to genomics as more large data-sets via the Shivom platform become available. Until recently, machine learning systems had to be trained to find useful patterns in omics data.
However, we no longer need to teach computers how to perform complex tasks like image recognition or omics data integration; instead, we have artificial intelligence (A.I.) systems that learn how to do it themselves. The most powerful form of machine learning approaches being used today is called “deep-learning”, designed to be analogous to how a human brain works.
Deep learning builds a complex mathematical structure called a neural network based on vast quantities of data. These deep-learning methods use multiple processing layers to discover patterns, where each layer learns a concept from the data that subsequent layers build on. The higher the level, the more abstract the concepts that are learned.
Source : https://shivom.io