Nanoinformatics: developing new computing applications for nanomedicine

Maojo, Víctor; Fritts, M. J.; Martín-Sánchez, Fernando; Iglesia, Diana de la; Cachau, Raúl E.; García-Remesal, Miguel; Crespo, José; Mitchell, Joyce A.; Anguita, Alberto; Baker, Nathan A.; Barreiro, José M.; Benítez, Sonia; Calle, Guillermo de la; Facelli, Julio C.; Ghazal, Peter; Geissbühler, Antoine; González‐Nilo, Fernando; Graf, Norbert; Grangeat, Pierre; Hermosilla, I.; Hussein, Rada; Kern, Josipa; Koch, Sabine; Legré, Yannick; López-Alonso, Victoria; López–Campos, Guillermo; Milanesi, Luciano; Moustakis, Vassilis; Munteanu, Cristian R.; Otero, Paula; Pérez-Rey, David; Potamias, George; Sanz, Ferrán; Kulikowski, Casimir A.

doi:10.1007/s00607-012-0191-2

Cited by 19 publications

(17 citation statements)

References 23 publications

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“…This study points out that valuable data on nanomedical CTs are already available implicitly within the ClincalTrials.gov repository, and that machine learning methods can be used to combine the values of individual word-features from the CT summaries into a predictor for detecting nano -related CTs. These kinds of approaches are needed to help gather, organize and integrate the huge volume of existing data which is potentially relevant for nanomedicine—including pre-clinical and clinical data—and make them accessible to researchers [93] .…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov

et al. 2014

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BackgroundClinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs—even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results.MethodsWe propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset.Results and ConclusionsThe performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov

et al. 2014

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“…The data obtained with these high throughput approaches with a consistent fold change are, hence, classified through bioinformatics tools. Powerful bioinformatics tools are needed to merge the data coming from global gene expression analysis to other toxicity endpoints [63]. Finally, to employ gene expression data outputs in regulatory decisions, it is necessary to demonstrate that To infer the molecular pathways modified by the NPs treatment, sets of differentially expressed genes obtained from global expression analysis are compared by IPA software to a set of genes involved in known molecular networks.…”

Section: Hallmarks Of Gene Expression In Response To Npsmentioning

confidence: 99%

Molecular Bases of Nanotoxicology

Tino

Ambrosone

Marchesano

et al. 2014

Bio‐ and Bioinspired Nanomaterials

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The recent design and development of nanosized particulate materials, generally termed as nanomaterials, and their growing employment in several fields spanning from electronics to medicine, created an immediate need for approaches to assess nanomaterial safety. Owing to their distinctive physicochemical and kinetic characteristics, their behavior toward biological systems is influenced by many factors including shape, coatings, solubility, and aggregation of the particles, but is also dictated by the living entity exposed to the nanoparticle, making the prediction of toxicity a great challenge. Owing to the variety of the nanoparticles and systems employed to assess their toxicity, many diverse mechanisms have been identified that contribute to their adverse effects on cells, tissues, and organs of living organisms, and the relevant literature is exponentially growing. Many studies have reviewed in this fast changing research area, addressing several aspects spanning from nanoparticle physicochemical properties that influence toxicity to the impact on environment and human health. Here we focus on the possible molecular events elicited in distinct subcellular compartments by nanomaterials, whose potential hazardous nature is here considered by calling them "nanostressors." We provide an overview of various NP evoked molecular processes generally assembled under the common term nanotoxicology, pointing out the most advanced technological tools (microarray, RNAseq) and underexplored effect (epigenetic) recently employed in the nanotoxicology field. © 2015 Wiley-VCH Verlag GmbH & Co. KGaA. All rights reserved

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“…The University of Zagreb, School of Medicine was a partner institution with Josipa Kern as the project leader from the Croatian side. The aim was to enhance cooperation between research centres, universities, hospitals, small and medium-sized enterprises, public entities and others, and expand the impact of European achievements in grid and biomedical informatics to researchers, educators, and health practitioners world-wide ( 44 , 45 , 46 , 47 , 48 ).…”

Section: Development In the 21 St Centurymentioning

confidence: 99%

Medical Informatics in Croatia - a Historical Survey

Deželić¹,

Kern²,

Petrovečki³

et al. 2014

Acta Inform Med

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A historical survey of medical informatics (MI) in Croatia is presented from the beginnings in the late sixties of the 20th century to the present time. Described are MI projects, applications in clinical medicine and public health, start and development of MI research and education, beginnings of international cooperation, establishment of the Croatian Society for MI and its membership to EFMI and IMIA. The current status of computerization of the Croatian healthcare system is sketched as well as the present graduate and postgraduate study MI curricula. The information contained in the paper shows that MI in Croatia developed and still develops along with its advancement elsewhere.

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Nanoinformatics: developing new computing applications for nanomedicine

Cited by 19 publications

References 23 publications

A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov

A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov

Molecular Bases of Nanotoxicology

Medical Informatics in Croatia - a Historical Survey

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