N1005 IBM Research: healthcare

Microfluidic Probe for Personalized Healthcare

The sequencing of the entire human genome back in the early 2000s is considered one of the most important scientific breakthrough enabling data-driven medicine. It flooded researchers in biology with a tremendous amount of Big Data that needed to be processed and tested. It was around then that DNA microarrays, small glass chips where thousands of DNA fragments are deposited, became popular because they enable high-throughput and multiplexed testing for gene expression and mutations, for example in cancer.   <table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpA2zI9pGJWzWVKfDujm_IbFOqFKOiv6xiGohNsSOrHq8jgH7uXGJM6dPvXJWqHU9Rms7sPYKO8MbEWm1Yp-7Pf7fZoafrSaLVoMGQbNkhXoPiweC18jeF6K4ZJgQXxZz7VjurvzYKi8g/s1600/group+photo+--+Analytical+Chemistry+%25281%2529.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="248" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpA2zI9pGJWzWVKfDujm_IbFOqFKOiv6xiGohNsSOrHq8jgH7uXGJM6dPvXJWqHU9Rms7sPYKO8MbEWm1Yp-7Pf7fZoafrSaLVoMGQbNkhXoPiweC18jeF6K4ZJgQXxZz7VjurvzYKi8g/s400/group+photo+--+Analytical+Chemistry+%25281%2529.jpg" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">From left to right, Julien Cors, Govind Kaigala and David Taylor</td></tr></tbody></table><div class="MsoNormal" style="line-height: normal;"> Interestingly, DNA and protein microarrays were one of the convergence points of microtechnology and biology that resulted in the fabrication of high-density arrays of biomolecules.  While protein microarrays have significant value for drug discovery, and molecular profiling, the same success in the use of DNA arrays was not transferable to protein arrays due to the limited quality of protein arrays. This inspired a team of IBM scientists to develop a new biopatterning method for efficient, accurate and high-quality patterning of proteins on surfaces by addressing fundamental bottlenecks inherent to the fabrication of protein microarrays. Such high-quality microarrays will benefit quantitative biological assays for personalized diagnostics and screening applications.  A new paper appearing this month on the cover of <a href="http://pubs.acs.org/doi/abs/10.1021/acs.analchem.5b04649">Analytical Chemistry</a> details their latest research breakthrough. I spoke with two of the authors <a href="http://researcher.ibm.com/researcher/view.php?person=zurich-GOV">Govind Kaigala</a> and <a href="http://researcher.watson.ibm.com/researcher/view.php?person=zurich-JCO">Julien Cors</a>. <table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbGDL8AloPQcHClEw7dE3ANzFAShlEyZAFH2-GaZ8CwIZpMzD4CsUU6r6xMsduYbQEL1ty5-lTbeeB3BVUWQkleiGZFlZnkHO5B-2he5t7m_FFXkHAAtcGUJVsuilUyZeMDg6vHVywA4g/s1600/JCO+--+Biopatterning+--+cover+page.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbGDL8AloPQcHClEw7dE3ANzFAShlEyZAFH2-GaZ8CwIZpMzD4CsUU6r6xMsduYbQEL1ty5-lTbeeB3BVUWQkleiGZFlZnkHO5B-2he5t7m_FFXkHAAtcGUJVsuilUyZeMDg6vHVywA4g/s320/JCO+--+Biopatterning+--+cover+page.jpg" width="241" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Cover of Analytical Chemistry Credit: ACS Publications</td></tr></tbody></table></div><div class="MsoNormal" style="line-height: normal;"> </div> One of the benefits of your technique is depositing reagents on surfaces in a wet environment. Why is this important and how does it compare to current approaches? <a href="http://researcher.ibm.com/researcher/view.php?person=zurich-GOV" style="font-family: Arial, Helvetica, sans-serif;">Govind Kaigala</a> (GK): Current techniques for protein patterning use inkjet and pin spotters, and they do not produce homogeneous protein patterns -- largely because they operate in a dry environment. So we asked ourselves, biology is wet, so how can we accurately test samples when we are changing it’s make up by drying it out? In contrast, our method relies on a microfluidic probe (MFP) that confines nanoliter volumes of reagents in a wet environment. By fully immersing the substrate in a liquid, the “coffee stain effect” (<a href="https://en.wikipedia.org/wiki/Marangoni_effect" style="color: black;">Marangoni effect</a>), is minimized with the reduction of evaporation. In addition, we use continuous flows on the surface, which ensure improved reaction kinetics compared to deposition relying on diffusion in existing techniques. Also via a technique of re-circulating liquid we improve reagent utilisation, which is important because its very expensive. In our article we studied the transport of biomolecules in flows and their surface interactions and to address this we designed specific MFP heads to create homogeneous protein patterns with volume consumption comparable to inkjet spotters. The paper also describes the convection-enhanced transport and recirculation of sub-microliter volumes using analytical models. A major factor of your research is working at the micrometer level. Can you explain what the trick is when working with such small sample sizes? <a href="http://researcher.watson.ibm.com/researcher/view.php?person=zurich-JCO" style="font-family: Arial, Helvetica, sans-serif;">Julien Cors</a> (JC): IBM is considered the birthplace for scanning probe techniques with the original scanning probes developed for atomic imaging. Our scanning probe isn’t imaging atoms, the MFP technology that we are developing is for biological applications, in this case protein patterning. But, the technique is similar in that the probe head never comes into contact with the sample and is used in liquid environments and with a resolution of a few micrometers. By continuously injecting and aspirating liquids, nanoliter volumes of liquids are confined at the tip of the head. To put this into perspective, we are working with volumes of liquids thousands of times smaller than a tear drop. At this micrometer-length scale both reaction kinetics and transport of biomolecules onto the surface are highly favourable  By exposing one or multiple such confined liquid using the probe on a surface diverse patterns of biomolecules can be created. Can you talk about some specific applications for the innovation? GK: Protein-based assays (immunoassays) are very broadly applicable, and our method can be used to create high-quality substrates to implement such tests. Current protein microarrays provide qualitative information i.e. presence or absence of a specific biomarker. Because of the inhomogeneity of the deposited patterns, extracting quantitative information remains challenging. Quantitative data have great importance both in diagnostics and biomarker discovery.  <div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-layout-grid-align: none; text-autospace: none;"> </div><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody><tr><td style="text-align: center;"><a href="https://c1.staticflickr.com/3/2854/12953095294_191b7d42e0.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="211" src="https://c1.staticflickr.com/3/2854/12953095294_191b7d42e0.jpg" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The MFP comes with different microfluidic patterns based on the type of application.</td></tr></tbody></table> <div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">For example, in the Analytical Chemistry paper we demonstrate an assay for the detection immunoglobulins (IgGs). IgGs are used as a diagnostics marker for several autoimmune diseases and as a measure of the immune response. </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"> </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">This research is partially funded by your European Research Council (ERC) grant. What do you hope to achieve by the end of the grant?</div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"> </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">GK: Within the ERC-<a href="http://www.bioprobe.eu/">BioProbe</a> project, we are developing new concepts, tools and methods for improved molecular profiling of tissues for tumor diagnosis for personalized medicine. </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"> </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">Techniques for liquid recirculation and analytical models for improved protein microarrays was in fact an off-shoot of this work – while in BioProbe, we will focus on the main theme, we keep an eye for such projects and pursue them.</div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"> </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">What's next for your research and when can we expect it to be used outside of IBM by partners?</div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">GK: We are very excited about the progress and evolution of the microfluidic probe, particularly as it is now starting to be applied in various areas including pathology and personalized medicine. </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"> </div><div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-outline-level: 1;">Convection-Enhanced Biopatterning with Recirculation of Hydrodynamically Confined Nanoliter Volumes of Reagents, <a href="http://pubs.acs.org/author/Autebert%2C+Julien">Julien Autebert</a>, <a href="http://pubs.acs.org/author/Cors%2C+Julien+F">Julien F. Cors</a>, <a href="http://pubs.acs.org/author/Taylor%2C+David+P">David P. Taylor</a>, and <a href="http://pubs.acs.org/author/Kaigala%2C+Govind+V">Govind V. Kaigal</a>a, Anal. Chem., 2016, 88 (6), pp 3235–3242, <a href="http://pubs.acs.org/doi/abs/10.1021/acs.analchem.5b04649">DOI: 10.1021/acs.analchem.5b04649</a></div><div class="MsoNormal"> </div>

The sequencing of the entire human genome back in the early 2000s is considered one of the most important scientific breakthrough enabling d...

From Knowledge Graphs to Cognitive Computing

6:47 AM N1000 0

Editor’s note: This article was written by <a href="http://researcher.ibm.com/researcher/view.php?person=ie-marco.sbodio">Marco Luca Sbodio</a>, Research Staff Member at IBM Research - Ireland, and originally appeared on 01net’s website as <a href="http://www.01net.it/dati-di-pubblica-utilita-dai-knowledge-graph-al-cognitive-computing/">Dati di pubblica utilità: dai knowledge graph al cognitive computing</a>; re-printed here with permission.  We live in the era of Big Data, and we use data for everything: from predicting which goods a customer may buy next, to forecasting the weather, and analyzing traffic in cities, or the spreading of diseases in the population of a country. Indeed data is an extremely powerful resource, an enabler for a large portion of our current technology. However, we should not forget that though data is the foundation underpinning information processing, the real value is in the information that we are able to surface by processing the data, in the patterns that we are able to identify and recognize, and, ultimately, in the knowledge that we extract. So, we can say that from Big Data we usually distill Small Knowledge, which has Big Value. <a name="more"></a> The problem of representing knowledge in a machine processable format is a well known field of Artificial Intelligence, which has been extensively studied since the 1970s. An approach to knowledge representation based on graph (mathematical structure represented as sets of nodes, or vertices, which may be connected by edges) has been discussed for a long time, starting from the introduction of conceptual graphs [1] to the more recent Linked Data initiative [2] - a method to publish data and knowledge over the Web by explicitly representing their relationships, thus enabling computers to directly access and semantically query such distributed knowledge graphs (example below). <div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxQQOsxmStnbJaiub_ISSmc51MGve5x0_Sn-6gd-wgSurCynWOXQcyiqMkVcuzMNN8chtxuXT_-McHIV4Dp0yhW55JBm8RSQrAyb7LZ7cPVgW2fEo-nqyd_oV-hKhaR6fxH5Y9rsYNTm8/s1600/Graph_View_DBPedia_Ireland.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="276" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxQQOsxmStnbJaiub_ISSmc51MGve5x0_Sn-6gd-wgSurCynWOXQcyiqMkVcuzMNN8chtxuXT_-McHIV4Dp0yhW55JBm8RSQrAyb7LZ7cPVgW2fEo-nqyd_oV-hKhaR6fxH5Y9rsYNTm8/s400/Graph_View_DBPedia_Ireland.jpg" width="400" /></a></div> Knowledge graphs are a possible approach to building large knowledge bases, structured collections of facts about the world that computer systems can use to reason, and to interact with humans more naturally. These are some of the key characteristics of cognitive computing. The impact of this new computing approach are potentially very high, and, combined with other technologies current under development, it promises to open up whole new ways for humans to use computers. Among the various fields of application of cognitive computing, a very interesting one is the integrated care domain, an emerging worldwide trend aiming at delivering more effective and coordinated forms of care provision spanning, among others, the social domain and the health domain. Social care and health care are knowledge intensive domain, where data and information are abundant. Delivering insights into the strengths and vulnerabilities of individuals with respect to the communities they live in and their social environment represents a key challenge for practitioners in the integrated care domain. Link2Outcome [3] [4] is a prototype of a cognitive system that helps care workers to make more informed decision to improve outcome for patients. Among other goals, Link2Outcome aims at facilitating access to knowledge across domains and data sources, and to use such knowledge to summarize the state of an individuals though a dynamic set of vulnerability indexes. An interesting example of how Link2Outcome can help doctors is shown in [3]: the system may import (for example from an open data city portal) linked data describing pollution levels in a city, and correlate such information with the area where a patient lives in and with knowledge of how pollution may affect clinical conditions of the patient. A doctor may see how the vulnerability indexes of the patien change after the system has ingested the new data, and thus better understand how the environment may affect this patient. As an extension to Link2Outcome, the more recent work BlueLENS [5], tackle the problem of collecting and providing the right information, to the right people, at the right time across the care continuum. The health of an individual is increasingly studied from multiple perspective, and analyzing multi-sectoral determinants of health is becoming a mainstream approach in integrated care. To enable this approach, data fusion from multiple heterogeneous sources is of key importance, and cognitive systems are uniquely positioned to help practitioners in surfacing the right information at the right time. By establishing a more natural and seamless interaction between doctors and knowledge systems, cognitive computing is paving the way to revolutionize care, by bringing better and more timely insights that help experts in taking the best possible decisions to help patients. <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtJhed_lT3FMaY6x6dFelLApVt-3vdI5p3ob5rzVNuDvoR9rL4ePFQ2pFUkzuV05hZYg3Dwo1_ImXy6n8eUtbej0Kcqk8nxEUD40Bwvsn2bXPLBfWuNMRCJMIeBV3lm3Lfa-M3Udfs4aw/s1600/GuruBlueLensHackathonAwardGroupShot.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="303" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtJhed_lT3FMaY6x6dFelLApVt-3vdI5p3ob5rzVNuDvoR9rL4ePFQ2pFUkzuV05hZYg3Dwo1_ImXy6n8eUtbej0Kcqk8nxEUD40Bwvsn2bXPLBfWuNMRCJMIeBV3lm3Lfa-M3Udfs4aw/s400/GuruBlueLensHackathonAwardGroupShot.jpg" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">L-R: Martin Stephenson, Vanessa Lopez, Jiewen Wu,  Marco Luca Sbodio, Pierpaolo Tommasi, Spyros Kotoulas, Guruduth Banavar, and Nuno Lopes.</td></tr></tbody></table> [1] John F. Sowa. 1976. Conceptual graphs for a data base interface. IBM Journal of Research and Development 20, 4 (July 1976), 336-357. DOI=[<a href="http://dx.doi.org/10.1147/rd.204.0336">http://dx.doi.org/10.1147/rd.204.0336</a>] [2] Bizer, Christian; Heath, Tom; Berners-Lee, Tim (2009). "Linked Data—The Story So Far". International Journal on Semantic Web and Information Systems 5 (3): 1–22. [doi:10.4018/jswis.2009081901]. ISSN 1552-6283. [3] [<a href="https://www.youtube.com/watch?v=lx3MRzR_fxI">https://www.youtube.com/watch?v=lx3MRzR_fxI</a>] [4] Spyros Kotoulas, Vanessa Lopez, Marco Luca Sbodio, Martin Stephenson, Pierpaolo Tommasi, and Pol Mac Aonghusa. 2014. A linked data approach to care coordination. In Proceedings of the 25th ACM conference on Hypertext and social media (HT '14). ACM, New York, NY, USA, 77-87. DOI=[<a href="http://dx.doi.org/10.1145/2631775.2631807">http://dx.doi.org/10.1145/2631775.2631807</a>] [5] [<a href="http://researcher.watson.ibm.com/researcher/view_group.php?id=6439">http://researcher.watson.ibm.com/researcher/view_group.php?id=6439</a>]

big data cognitive computing healthcare From Knowledge Graphs to Cognitive Computing

Editor’s note: This article was written by Marco Luca Sbodio , R esearch S taff M ember at IBM Research - Ireland , and originally appeared...