{"id":951,"date":"2018-06-28T15:20:43","date_gmt":"2018-06-28T13:20:43","guid":{"rendered":"http:\/\/iict-space.heig-vd.ch\/cpn\/?p=951"},"modified":"2018-06-28T15:21:52","modified_gmt":"2018-06-28T13:21:52","slug":"sib-days-2018","status":"publish","type":"post","link":"http:\/\/iict-space.heig-vd.ch\/cpn\/sib-days-2018\/","title":{"rendered":"SIB days 2018"},"content":{"rendered":"

Diogo Leite, Ruan Fernando L\u00f2pez, Diana Victoria Ramirez and Jibril Mammeri presented posters at the SIB days 2018<\/h1>\n

Exploration of a large spectrum of machine-learning techniques to predict phage-bacterium interactions <\/span><\/b><\/span><\/span><\/span><\/h3>\n

\u00a0Diogo Leite1<\/sup>, Gr\u00e9gory Resch3<\/sup>, Yok-Ai Que2<\/sup>, Xavier Brochet1<\/sup>, Miguel Barreto1<\/sup> and Carlos Pe\u00f1a1<\/sup>
\n1<\/sup>School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Switzerland & Swiss Institute of Bioinformatics (SIB)
\n2<\/sup>Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
\n3<\/sup>Department of Intensive Care Medicine, Bern University Hospital (Inselspital), Bern, Switzerland<\/span><\/p>\n

Abstract<\/strong><\/p>\n

Antibiotic resistance threatens the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy use viruses (phages) to specifically infect and kill bacteria during their life cycle. Currently, there is no method to predict phage-bacterium interactions, and these pairs must be empirically tested in laboratory, a costly process in terms of time and money. To overcome such situation, we are currently exploring several computational approaches intended at predicting if a given phage-bacterium pair may interact reducing, thus, the number of required in vivo experiments.<\/div>\n
<\/div>\n
Exploration of a large spectrum of machine-learning techniques to predict phage-bacterium interactions<\/a><\/div>\n
<\/div>\n

Applying One-Class learning algorithms for predicting phage-bacteria interactions<\/span><\/b><\/span><\/span><\/span><\/h3>\n

Juan Fernando L\u00f3pez4<\/sup>, Diogo Leite1<\/sup>, Gr\u00e9gory Resch3<\/sup>, Yok-Ai Que2<\/sup>, Xavier Brochet1<\/sup> and Carlos Pe\u00f1a1<\/sup>
\n1<\/sup>School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Switzerland & Swiss Institute of Bioinformatics (SIB)
\n2<\/sup>Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
\n3<\/sup>Department of Intensive Care Medicine, Bern University Hospital (Inselspital), Bern, Switzerland
\n4<\/sup>Autonomous University of the West (Universidad Aut\u00f3noma de Occidente), Cali, Colombia
\n<\/span><\/p>\n

\n

Abstract<\/strong><\/p>\n

The misuse of antibiotic drugs contributes to the emergence and rapid dissemination of antibiotic resistance worldwide, threatening medical progress. A re-emerging therapy, dubbed phage-therapy, might represent an alternative for this. Phage-therapy is based on bacteriophages that specifically infect and kill bacteria during their life cycle. The success of phage therapy mainly relies on the exact matching between the bacteria and the phage. However, this is a time-consuming process achieved only in laboratories.<\/p>\n

Hence, the fast identification of potential phage candidates capable of dealing with a given bacteria is essential for using phage-therapy in routine. Machine learning algorithms constitute a promising approach to achieve this goal. Unfortunately, public databases contain highly imbalanced interaction data (i.e., only positive phage-bacterium interactions); making it harder to use classic machine learning algorithms that needs relatively-balanced classes to work. To address this problem, we are exploring the use of One-Class learning methods, which are robust tools to deal with imbalanced datasets.<\/p>\n

We have tested an odd number of One-Class learning techniques merged with the ensemble-learning paradigm on real phage-bacteria interactions and obtained accurate results in different types of metrics (e.g. accuracy and f1-score up to 80%). Further work could include developing new methods for One-Class classification and applying them to other types of real data.<\/p>\n

Applying One-Class learning algorithms for predicting phage-bacteria interactions<\/a><\/p>\n<\/div>\n

Agent-based modelling of the behavior of stem cells in a 3D-printed bio-device to be used in regenerative medicine<\/span><\/b><\/span><\/span><\/span><\/h3>\n

Victoria L\u00f3pez1<\/sup>, Carlos Pe\u00f1a2<\/sup> and \u00c1lvaro J. Rojas1<\/sup>
\n1<\/sup>Autonomous University of the West (Universidad Aut\u00f3noma de Occidente), Cali, Colombia
\n2<\/sup>School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Switzerland & Swiss Institute of Bioinformatics (SIB)
\n<\/span><\/p>\n

\n

Abstract<\/strong><\/p>\n

A novel treatment proposed for patients who have suffered a myocardial infarction is based on the design and construction of a 3D-printed bio-device where mesenchymal stem cells (MSC), along with other cell types, are cultivated for being later implanted in the patient\u2019s myocardium.<\/p>\n

It is very important to understand the behavior of the bio-device, which can be affectedby different variables. In line with this goal, we present, herein, our contribution to the development of a computational model of the behavior of the MSC and other cell types used in the 3D-printed scaffold.<\/p>\n

First, we explored the use of a model based on ordinary differential equations and concluded that it was limited regarding the possibilities to take into account additional characteristics of the cellular microenvironment, we opted for an Agent-Based modeling approach. The model considers some basic MSC\u2019s characteristics and their interactions with the microenvironment. Until now, our results have shown that the emergent behavior of the cells in the model agrees with other cellular modeling results and observations of preliminary experiments. Further simulations will be performed to analyze the effect of parameters in the results and will be validated with in-vitro observations.<\/p>\n

Agent-based modelling of the behavior of stem cells in a 3D-printed bio-device to be used in regenerative medicine<\/a><\/p>\n<\/div>\n

MaLDIveS : Machine Learning Diagnostic Soil<\/span><\/b><\/span><\/span><\/span><\/h3>\n

Jibril Mammeri1,2<\/sup>, Xavier Brochet1,2<\/sup>, Thierry Heger2,3<\/sup>, Sven Bacher4<\/sup>, Magdalena Steiner4<\/sup>, Carlos Pe\u00f1a1,2<\/sup>
\n1<\/sup>School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Switzerland & Swiss Institute of Bioinformatics (SIB)
\n2<\/sup>Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
\n3<\/sup>School of Viticulture and Enology (Changins), University of Applied Sciences Western Switzerland (HES-SO), Switzerland
\n4<\/sup>Department of Biology, Unit of Ecology and Evolution, University of Fribourg, Switzerland
\n<\/span><\/p>\n

\n

Abstract<\/strong><\/p>\n

MaLDiveS is an ongoing multidisciplinary project, which aims to develop a new method of biomonitoring based on next-generation sequencing data and their treatment by machine learning methods. It will allow to assess the impact of treatment on the health and quality of the vines soil. This work will improve the understanding of pesticides and other environmental factors impact on protists communities. Our main objective is to identify bioindicators associated with environmental stress and to characterize the behavior of their relative abundance, which will lead to the construction of diagnostic models.<\/p>\n

MaLDIveS : Machine Learning Diagnostic Soil<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Diogo Leite, Ruan Fernando L\u00f2pez, Diana Victoria Ramirez and Jibril Mammeri presented posters at the SIB days 2018 Exploration of a large spectrum of machine-learning techniques to predict phage-bacterium interactions \u00a0Diogo Leite1, Gr\u00e9gory Resch3, Yok-Ai Que2, Xavier Brochet1, Miguel Barreto1 Continue reading SIB days 2018<\/span>→<\/span><\/a><\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[15],"tags":[],"_links":{"self":[{"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/posts\/951"}],"collection":[{"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/comments?post=951"}],"version-history":[{"count":3,"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/posts\/951\/revisions"}],"predecessor-version":[{"id":959,"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/posts\/951\/revisions\/959"}],"wp:attachment":[{"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/media?parent=951"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/categories?post=951"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/iict-space.heig-vd.ch\/cpn\/wp-json\/wp\/v2\/tags?post=951"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}