1,721,608 research outputs found
Preface to SPECIAL ISSUE: UNSUPERVISED CLUSTERING FOR EXPLORATIVE DATA ANALYSIS: CURRENT RESEARCH TOPICS AND APPLICATIONS
No full textRecent Advances and Challenges in the IFToMM PC for History of MMS
No full textThe paper presents a brief account of the evolution of the IFToMM Permanent Commission (PC) for History of MMS in terms of activities and people involved in order to illustrate the evolution of the commission with its peculiarities, problems, results, and the possibility of satisfying the constitution purposes of the commission in terms of keeping memory of the history of the IFToMM and of the thematic areas of the MMS (Mechanism and Machine Science) with evaluations and interpretations of historical-technical nature. The examination of these activities is centred on the developments of the last twenty years referring to an activity of the commission with an increasing impact not only within the IFToMM community. The conference, editorial, and social activities are reported with chronicle outlines, including the interaction among the members of the commission, with particular reference to the HMM Symposium on history of mechanisms and machines started in 2000 and the book series of History of MMS started in 2007
Editorial: Mechanics of Legged Robots: From Bio-Inspiration to Novel Legged Machines
No full textEditorial on the Research Topic Mechanics of Legged Robots: From Bio-Inspiration to Novel Legged Machine
Cesare Rossi (1955–2017)
No full textCesare Rossi was very active in several aspects of his life. At his University of Naples “Federico II” he was a committed and popular professor, holding his lectures with great commitment toward the students, and was involved in several committees. At the national level he had been serving for many years as a member of the Executive Council of the “GMA”, the council of the Italian professors and researchers of Mechanics Applied to Machines. Since 2014 he was the Chair of IFToMM Italy, the Italian section of IFToMM. Under his leadership the activity of IFToMM Italy has been reinvigorated with a clear society structure and through several initiatives, among which the start of the biennial national IFToMM conference IFIT. At the international level, he was very active in several communities, as well as a prominent member of IFToMM, promoting many activities and events, within the Technical Committee on Robotics and Mechatronics and especially in the Permanent Commission for the History of Mechanism and Machine Science
Plans for a Course on the History of Mechanisms and Machine Science
No full textMany universities offer optional courses within the curricula of studies that students can choose based on their interests. Usually, they are short courses of 3 ECTS (credits) but in some cases they can be proposed up to 6 ECTS. Moreover, they are often common to several degree courses, so the topics covered should be more general and transversal with respect to the specific engineering curriculum. In this paper, the background significance of the History of Mechanisms and Machine Science (MMS) is discussed by re-proposing a short course in technical formation curricula for engineers, preferably at Bachelor levels. After reviewing some previous preliminary experiences, the course proposal is outlined as based on the expectations in learning outcomes and with a general structure referring to basic literature. The target is to provide historical education backgrounds within the formation curriculum of a modern engineer
Machine learning prediction of oncology drug targets based on protein and network properties
No full textBackground: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results: We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions: We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions
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