Change - Deciphering Aging

Created on June 17, 2013, 12:39 p.m. by Hevok & updated on June 17, 2013, 10:21 p.m. by antonkulaga

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Roadmap ¶
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.. Anton: probably we should think about two documents, one the will explain roadmap in details, and one general (this one) with all possible directions ¶
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.. Daniel: All right, here is the Roadmap http://denigma.info/data/Roadmap/description &para]
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International Longevity Alliance vision and proposal ¶
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Aging is very complex phenomena, and currying it is not an easy task. It is not a task for one researcher or a small group of researchers, in fact it is a matter of whole anti-aging community and even the whole society (as it is also a social issue). It is even much more important: Everyone ages, the anti-aging community alone is not sufficient to solve it. What we need is a revolution. ¶
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Understanding vs. repairing, ILA's approach (what is different from Aubrey's approach) ¶
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To cure aging we need to decipher it first and then indentify and create treaments (interventions). Understanding all the mechanisms of aging and its causes is a hard task and thus it is always tepmting not to do it and limit ourselves to thinking of it as only damage-repair problem. ¶
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.. Should also provide hope at the same time ¶
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But the reality shows us that it does not work that way. There are numerous experiments starting from heterochronic parabiosis and organ replacement in old animals... [provide links and complete the phrase] that show us that the overall damaging processes in old organisms are very fast and just repairing does not work well, it is like patching up the holes in the dam. ¶
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.. He will probaly not know heterochronic parabiosis ¶
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Let us provide a simple analogy: not thinking about the causes of damages is good for cars because after you completely reparied an old rusty car you know that the pace of rusting will not be many times faster than for the new ones. So if it will break soon it should be something else that you simply forgot to repair. But experiments and studies show us that in old organisms it is different. In fact the regulation that is broken in aging organisms and we cannot fix it withough good understanding of aging process. ¶
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So solving aging is impossible withough understanding it. It would be nice but hard to understand it entirely. Meanwhile it will be already sufficient to understand it at the level that will let us slow down/reverse its processes in a way that will let us repair it faster than organism ages. ¶
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Solving aging ¶
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Solving aging is not a simple task so we have some preferences but we can not say with absolute 100% certainty which of aging theories (or some entirely new one) is right. In fact the favourability of aging theories changes with new data and new assumptions. But what can do and what we are already doing is coordinating activities of anti-aging organisations and building an infrastructure that will tell them all (together with our own researchers) to work and collaborate more productively and solve aging much faster. ¶
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In order to decipher and then cure aging we need to: ¶
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* develop and improve our worlwide organization to attract people and resources and coordinate activities of all groups and individulas to solve aging. This is what is ILA all about ¶
* create an IT infrastructure for aging research because we need to summarize, analyse and collaborate on tones of data gathered and produced worldwide. It is what is Denigma all about ¶
* conduct experiments on various animals to test what we learned and to obtain new knowledge. That is a subject of "Testing Center" that is not yet established but is in our plans. ¶
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"I have a plan" (Roadmap) ¶
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TODO: integrate with Ilia's document: ¶
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Everything starts with planning, and each plan starts with some preliminary phase that involves styding of what exists, who are possible partners and what has been done so far. ¶
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Some information is already in our heads and in our portfolio but other needs some actions to be conducted and there are two approaches for this that can be combined: ¶
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The first approach concludes in collecting needed info by making dedicated studies: ¶
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* conduct a survey and understand what is current state of affairs in anti-aging domain and related fields ¶
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* look at current state of affairs, choose the most effective technologies and ways to work and create our own strategy and action plan ¶
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* create a foresight of how it will be. The foresight includes not only our actions byt also our view of how others will act and how technologies will develop. And not only in aging field because aging researchers depend and are limited by worldwide state of technologies that will grow in future and there are also social issues that will influence funding ¶
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The main problem of the first approach is that it takes time and effort and thus money. The world is changing rapidly, so does the state of the art in aging research. So keeping it up to date needs conducting new studies that will need extra resources. ¶
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But of course it is not the only solution. ¶
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There is another, different way. ¶
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It relies on the fact that planing and information gathering is an iterative process: we need to learn in order to act and we can also learn by doing. So having a good IT infrastructure and beeing a hub of aging research and social activism let us obtain information constantly and minimizes a need for coducting determined studies because you will need just to take info from Denigma databases and do some small amount of extra work to produce them. So for the long term perspective second approach is better. ¶
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Planning and learning of what has been done in the field is not only a way to conduct our own planning, it also provides some opportunities: ¶
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* it easies advocacy for aging research and for seeking for investment opportunities ¶
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* with information and experties we will be able to provide the consultancy about the field help investors who are interested in projects in this area ¶
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* [TODO: add points] ¶
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Deciphering Aging (Denigma) ¶
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Denigma is information technology (IT) ecosystem that speeds up anti-aging research and activism, summarizies collected knowledge and infers new knowledge from collected one. [todo: improve text and add links+pics] ¶
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We already gathered a tremendous amount of facts in our repositories and applications. At the center is the lifespan application which shows all the lifespan interventions and lifespan factors, so it is possible to see what has already been achieved in anti-aging experiments and which factors influence aging. Gene expressions, gene interactions and aging genes apps [picture of genes network] show aging from genetic points of view. ¶
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There are also other apps valuable for aging research. Now we are working hard on network analysis and semantic features that will allow us to automaticaly infer new knowledge out of existing. We also develop collaborative features to ease collaboration for researchers and activists. ¶
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In fact IT is a field where a group of several developers working fulltime can change a lot as features that we develop increase productivity of many of people involved in the field. And as for any IT platform all features of Denigma can also be used in many other fields (both research and not) that provides some extra opportunities ¶
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* "Denigma for other fields": a lot of things that we do for aging are applicable and can be reused for other fields ¶
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* knowledge managememnt platform. Current knowledge managemt platforms (like semantic wiki) that are used by corparations and academia were developed too much time ago and do not meet the challenges of today so the features that we develop for aging researchers and activists will work there also ¶
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* community possibilities. [todo: tell something how user communities are important and valiable] ¶
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* educational opportunities. We develop graph and other visualizations and quests that in combination with other features can be easily used for education purposes anywhere. ¶
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* crowdsourcing for the research. We are working on features that let researchers outsource parts of their work to volunteers. Projects like Aging genes project do well with this approach. ¶
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* etc. When you create an ecosystem you often do not know beforehands what opportunities will it give you. But what is important here is that technologies/features together with user community that uses them. It always provide some new opprotunities that are not known beforehand. Denigma is opensource that makes it easier to grow and attract new people. In the same time it is possible to provide some payed services and consultancy on top of opensource Denigma platform ¶
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[todo: add new points] ¶
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Experimentations (Testing center) ¶
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Having summarized and infered new knoledge we need to conduct experiments to test it and discover new facts. ¶
It is essential to have our own an aging research testing center for several reasons. ¶
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* We need to conduct a lot of lifespan experiments with many animals. Most of them share some common features that can be automized to make experiments cheaper and more reliable. Inside ILA there is AgeVivo project that managed to develop some new techniques for lifespan tests. ¶
* Different animal organizations (like PETA and others) are very successfull in lobbying prohibition of various animal experiments so researchers in such countries will be able to outsource those experiments that are prohibited to us. ¶
* We need to have a lot of old animals. Currently they are much more expensive in labs then young ones. There are also some species (like short-living apes) that is especially interesting for ageing interventions and we need to grow them cheaper ¶
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The testing center should not be one building in one city or country. It is possible to "go cloud". There are also a lot of small labs and volunteers that can help with experiments in many countries. With appropriate infrastructure it is possible to split experiments into different countries and collect/combine the results. It also makes it more reliable because if there are some errors in some labs (with treatment, statistics or puriness of mice lines) the results in others will show it up. ¶
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There are also some facilities and equipment that belong to ILA's activists that can be used: ¶
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* in Kyiv Immunology lab can freely use Kyiv Gerontology vivarium and owes some useful equipment (like two sequencers) ¶
* there are several DIY bio groups in Israel and France that are eager to collaborate ¶
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In the same time many challenges that aging researchers face (like legal regime) are same for many other fields that provides us some additional opportunities. Researchers and medical companies from other fields can be interested in ordering animal experiments. ¶
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.. Other stuff (not sure if we should mention it): consultancy on ageing related issues, ageing related medical diagnostics, DIY bio services for activists (to learn biology better), e ¶
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Track record ¶
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should we tell about it?
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Deciphering Aging ¶
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:Abstract: Denigma is a Digital Decipher Machine that can encode and decode data in the web. It is specifically devoted to decipher the aging process by utilizing and systematizing the rapidly increasing amount of available biological data and any other information with relevance to aging research. Denigma structures the information in the form of facts and enables modular access to diverse data and logical reasoning on the constructed knowledge base. This enables decoding algorithms to perform global inference and planning on web-scale and therefore a the identification of effective interventions by considering all the previous knowledge and making high confident and testable predictions. ¶
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.. The Problem of Aging and Approaches to its Amelioration ¶
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.. The combination of machine and human intelligence is crucial for reverse-engineering the aging process and for developing effective and promising therapies to ameliorate aging. ¶
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Introduction ¶
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Aging is an enormous problem that needs a Manhatten-scale Project to be addressed. Such a project is not possible without establishing an extensive information technological platform that will be used for data unification, provide collaboration tools for researchers and longevity activists and automate the discovery of novel therapeutics to be tested. ¶
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That is why the International Longevity Alliance (ILA) initiated the construction of a digital decipher machine, Denigma [http://denigma.de], to reverse-engineer the aging process, make aging research more collaborative as well as efficient and facilitate the discovery of powerful therapeutics to ameliorate the degenerative aging process. ¶
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Denigma is a project of the ILA, it also functions as its main information technological platform with the long term aim to automate the discovery of novel therapeutics to be tested. Such discoveries will be a basement for a huge number of potential interventions that in their turn will create the need to establish a lifeextension testing center [Figure: The Vision]. ¶
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.. figure:: http://dgallery.s3.amazonaws.com/roadmap_concept.svg &para]
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Figure: The Vision. ¶
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The construction of a digital decipher machine will allow the creation of a web intelligence capable of reverse-engineering the aging process. Specifically Denigma uses advanced information technologies such as semantic web and machine learning bundled with information theory and combined with crowdsourcing for longevity research. Its development is lead by a group of enthusiasts consisting of aging research scientists and software development engineers, as well as social networkers which all together form the General Computing Initiative (GCI). ¶
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Data Unification for Deciphering Aging ¶
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Currently biomedical knowledge is scattered in heterogeneous formats. It is present in the form of unstructured academic articles, semi-structured datasets, structured databases and non-accessible in the minds of experts. This renders such heterogeneous knowledge inaccessible for global computing [Attwood et al., 2009]. ¶
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In order to provide a holistic view for researchers and run various machine learning algorithmes to infer new knowledge, the data should be represented in a well-structured format. This format should be both machine and human readable at the same time. For this we need a knowledge graph and the semantic web is the most suitable technology as it is widely accepted standard and was initialy developed in order to provide a new generation of internet that is knoweldge based. ¶
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A key technology to enable such a multi-layered data integration is ontology engineering. An ontology is an explicit formal specification of a shared conceptualization [Gruber T, 1995]. It is a basis for so-called Semantic Web Stack, a set of technologies that let researchers describe heterogeneous data and run reasoning computer algorithms over it. ¶
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.. figure:: http://dgallery.s3.amazonaws.com/semantic_web_technology_stack.png &para]
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Figure: Semantic Web Stack. ¶
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Open Distributed Science for Aging Research ¶
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In order to create a semantic graph the knowledge from research papers in the form of free text needs to be converted into a structured form, while individual articles, datasets and databases need to be mapped for modular access and converted into the same structure. Ontologies specific for aging research are necessary and being developed on Denigma. A collective effort is needed to establish the initial structure of the data. ¶
Data annotation and ontology creation processes can be automated, but only partially and therefore require human reasoning. ¶
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Linked data allows to combine machine learning with croudsourcing approach where a lot of tasks that need "human computations" are effectively outsourced to the crowd. Our crowdsourcing infrastructure is made to attract longevity activists to help scientists, complement and improve machine learning where humans do what machine cannot do well. ¶
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Denigma itself the result of crowdsourcing effort of researchers in aging and longevity as well as scientists from other fields. ¶
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http://denigma.de/data/entry/deciphering-aging &para]
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Ageing Data Repositories for Constructing a Knowledge Base ¶
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For deciphering aging we need to have a comprehensive and consistent knowledge. ¶
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So far Denigma already has gathered a tremendous amount of facts about aging research. At the center of it is the Lifespan application which systematizes data from lifespan studies, ranging from lifespan experiments, over extending interventions to individual factors that limit the lifespan. ¶
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With Denigma it is easily possible to see what has already been achieved in anti-aging experiments and which factors influence aging. Further functional genomics information are utilized such as gene expression activity changes and interactions between biological entities to infer new possible factors capable of extending the lifespan. ¶
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We work in strong collaboration with leading reasearchers of aging such as João Pedro de Magalhães, the pionier of the genomics of aging and of the application of systems biology on aging research, and Aubrey de Grey, the pionier of tackling aging as an enginering problem and the utilization of regenerative medicine to reverse aging, among many other scientists. ¶
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We recently started the construction of the world's most comprehensive database on the genetic variants associated with longevity in humans, which will help computer-aided targeted drug discovery to be used to extend the human healthspan. ¶
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Further we are developing novel ways of making the aging process visually more attractive, so as to engage citizen scientists and utilize the power of the crowd to complement automated algorithms to knowledge base creation as well as educate the public about the problem of aging [Figure: Lifespan Charts]. ¶
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.. figure:: http://dgallery.s3.amazonaws.com/lifespan_charts.png &para]
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Figure: Lifespan Charts. ¶
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There are numerous other applications developed on Denigma that are valuable for aging research, such as a semantic menagement system that harbours information about experts researching on aging, transhumanists and life extension activists, as well as functionalities to enable real-time collaborations. Specifically we develop collaborative features to ease collaboration for researchers and activists. ¶
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Finding Effective Anti-Aging Interventions ¶
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Computational methods developed on Denigma enable to identify effective therapeutics. Novel interventions can be found be utilizing top-down approaches via the use of high-throughput omics data or bottom-up targeted approaches, including: ¶
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* Gene expression pattern matching ¶
* Network-based disssection ¶
* Computer-aided drug discovery ¶
* Fact-based reasoning with ontologies ¶
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Gene Expression Pattern Matching ¶
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Gene expression activity patterns from transcriptomics data of already known lifespan extending interventions are used to derive common molecular signatures associated with longevity. Those signatures are then matched with gene expression profiles of drugs that trigger similar gene expression patterns. Drugs that induce similar gene expression changes are with high accuracy lifespan-extending drugs. Similiar meta-analysis of aging-related gene expression changes is used to derive a common molecular signaure associated with aging. The comparison of this signature with the gene expression profiles of drugs allows the researchers to identify those components that reverse the age-related gene expression changes. Those drugs that reverse aging gene expression changes can be powerful anti-aging drugs capable of reversing aging. ¶
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Network-Base Dissection ¶
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Having a comprehensive inventory of all the factors that are associated with lifespan determination, enables to apply network-approaches by utilizing interactomics data. For instance, given a list of the genes associated with the process of aging, the researcher can apply the guilt-by-association concept which basically states that genes with more interactions than expected by chance with genes associated with the given process are likely to also play a role in that process, i.e. are potential drug targets. ¶
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Computer-Aided Drug Discovery ¶
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By leveraging distributed computing algorithms, we can identify drugs that fit exactly into the three-dimensional structure of the gene products of, for example, aging genes. This approach allows to develop drugs with specific ¶
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Mutual Information ¶
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As mutual information provides the exact estimate of similarity between various model systems, it will be possible to predict the efficacy of a yet untested drug or treatment using the estimates of its similarity (mutual information) with other tested drugs and treatments along with the similarity of model systems to which they are applied. ¶
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Fact-Based Reasoning with Ontologies ¶
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Logical reasoning of a consistence knowledge base with apropriately designed ontologies allows to infer the causality of the chain of age-related changes and can suggest, via revealing implicit hidden knowledge, which therapies are most effective to reverse the drivers of age-related changes. ¶
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Therefore, by utilizing such computational appraches, the Denigma will be able to suggest experiments to provide insights into the mechanims of aging and their potential reversal, taking into account the current exsiting knowldge and data, and to provide methods to estimate the efficiency of potential life-extending interventions. ¶
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An Ecosystem of Reusable Applications ¶
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In fact information technology is a field where a group of several developers working full-time can change a lot, as features that we develop increase productivity of many people involved in the field. And as for any IT platform, all features of Denigma can also be used in many other fields (both research-related and unrelated) that provides some extra opportunities. ¶
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We are developing tools and applications which are used primarly for research on longevity. We are also training the crowd in programming to fascilitate development. Some of software we are producing are of general usability while others are domain specific. Those software that we develop can have the potential to be commercialed. ¶
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Some of the Denigma applications include: ¶
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* Data App ¶
- Data Entry ¶
- Ontologies ¶
- annotate, categorize, systemize, data ¶
- Links to external resources ¶
* Experts ¶
* Aspects ¶
- Science ¶
- Programming ¶
- Design ¶
+ Gallery ¶
* Integrator ¶
- Lifespan ¶
- Datasets ¶
- Expresssions ¶
- Interactions ¶
* Polls ¶
* Questionaries ¶
* Task Management ¶
- Tasks ¶
- Todos ¶
- Quests ¶
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Applicability to other Domains ¶
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* "Denigma for other fields". A lot of applications that we develop for aging can be reused for other fields ¶
* Knowledge management platform. The current knowledge management platforms (like semantic wiki) that are used by corporations and academia were developed long time ago and do not meet the challenges of today. So the features that we develop for aging researchers and activists will be also applicable in those areas. ¶
* Community building possibilities. Lifespan-extensionist communities can be strengthened and recruited using the Denigma comunication services. ¶
* Educational opportunities. We develop graphs and other methods of visualization as well as quests that, in combination with other features including gamification, can be easily used for education purposes anywhere. ¶
* Crowdsourcing for research. We are working on features that let researchers outsource parts of their work to volunteers. Projects like "Aging Genes" progress well with this approach. ¶
* Further benefits. When creating an ecosystem, it is often not known beforehand what opportunities can emerge. Yet, what is important in the Denigma platform is that here technologies/features exist together with the user community that utilizes them. Such a combination always provides new opportunities that were not known beforehand. Denigma is open-source which makes it easier to expand and attract new people. In the same time, it is possible to provide some payed services and consultancy on top of the open-source Denigma platform. ¶
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Conclusion ¶
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Denigma lies on the intersection of science, collaboration and machine learning [Figure: Denigma Concept]. It enhances scientific discoveries by combining human and machine intelligence, via supporting collaborations and utilizing crowdsourcing with artifical intelligence and machine learning. ¶
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.. figure:: http://dgallery.s3.amazonaws.com/denigma.png &para]
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Figure: Denigma Concept. ¶
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.. We have already ensured that the data can be accessed. ¶
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1. Conceptual picture with three networks ¶
2. Graph visualization ¶
3. Lifespan charts (or curve) ¶
4. Age-related disease ¶
5. Ontology graph of age-related deceases ¶
6. Age-related changes graph ¶
7. Screenshot with aging factors ¶
8. Prezi presentation with circles ¶
9. Semantic Web Stack ¶

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