The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union

2021 ◽  
Vol 21 ◽  
Author(s):  
Ricardo Santana ◽  
Enrique Onieva ◽  
Robin Zuluaga ◽  
Aliuska Duardo-Sánchez ◽  
Piedad Gañán
Keyword(s):  
Machine Learning ◽  
European Union ◽  
Scientific Knowledge ◽  
Predictive Models ◽  
Physicochemical Characteristics ◽  
Success Rates ◽  
European Regulation ◽  
European Public ◽  
European Public Assessment Report

Background: Machine Learning (ML) has experienced an increasing use given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models, capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need of efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in European Union and the role of ML in the authorization process. Methods: In terms of methodology, a dogmatic study of the European regulation and the guidances of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations. Results: As result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information. Conclusion: It is concluded that Machine Learning has the capacity to help to improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations and European Authority Medicine. To our best knowledge this is the first study focused on nanotechnology medicine products and machine learning use to support technical European public assessment report (EPAR) for complementary information.

Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union

2020 ◽  
Vol 20 (4) ◽  
pp. 324-332 ◽  
Author(s):  
Ricardo Santana ◽  
Enrique Onieva ◽  
Robin Zuluaga ◽  
Aliuska Duardo-Sánchez ◽  
Piedad Gañán
Keyword(s):  
Machine Learning ◽  
European Union ◽  
Biological Activity ◽  
Food Safety ◽  
Scientific Knowledge ◽  
Public Safety ◽  
Prediction Models ◽  
European Food Safety Authority ◽  
Food Regulation ◽  
Food Law

Aim: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Background: Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. Objective: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Results: It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. Conclusion: To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.

scholarly journals Listening without ears: Artificial intelligence in audio mastering

2018 ◽  
Vol 5 (2) ◽  
pp. 205395171880855 ◽  
Author(s):  
Thomas Birtchnell
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Scientific Knowledge ◽  
Music Industry ◽  
Digital Technologies ◽  
The Past ◽  
Listening Environments ◽  
Pragmatic Strategies

Since the inception of recorded music there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labour. With the advent of algorithms, big data and machine learning, loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio mastering processes and radically disrupting mastering careers. The emergence of dedicated products and services in artificial intelligence-driven audio mastering poses profound questions for the future of the music industry, already having faced significant challenges due to the digitalization of music over the past decades. The research reports on qualitative and ethnographic inquiry with audio mastering engineers on the automation of their expertise and the potential for artificial intelligence to augment or replace aspects of their workflows. Investigating audio mastering engineers' awareness of artificial intelligence, the research probes the importance of criticality in their labour. The research identifies intuitive performance and critical listening as areas where human ingenuity and communication pose problems for simulation. Affective labour disrupts speculation of algorithmic domination by highlighting the pragmatic strategies available for humans to adapt and augment digital technologies.

Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25

2021 ◽  
pp. 108705472110154
Author(s):  
Martha L. Cervantes-Henríquez ◽  
Johan E. Acosta-López ◽  
Ariel F. Martinez ◽  
Mauricio Arcos-Burgos ◽  
Pedro J. Puentes-Rozo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Latent Class ◽  
Pleiotropic Effect ◽  
Machine Learning Algorithms ◽  
Nucleotide Polymorphisms ◽  
Specific Symptom ◽  
Caribbean Community ◽  
Family Based

Objective: To investigate whether single nucleotide polymorphisms (SNPs) in the ADGRL3, DRD4, and SNAP25 genes are associated with and predict ADHD severity in families from a Caribbean community. Method: ADHD severity was derived using latent class cluster analysis of DSM-IV symptomatology. Family-based association tests were conducted to detect associations between SNPs and ADHD severity latent phenotypes. Machine learning algorithms were used to build predictive models of ADHD severity based on demographic and genetic data. Results: Individuals with ADHD exhibited two seemingly independent latent class severity configurations. SNPs harbored in DRD4, SNAP25, and ADGRL3 showed evidence of linkage and association to symptoms severity and a potential pleiotropic effect on distinct domains of ADHD severity. Predictive models discriminate severe from non-severe ADHD in specific symptom domains. Conclusion: This study supports the role of DRD4, SNAP25, and ADGRL3 genes in outlining ADHD severity, and a new prediction framework with potential clinical use.

TheOrvosi Hetilap (Hungarian Medical Journal)in the network of medical journals

2014 ◽  
Vol 155 (22) ◽  
pp. 876-879
Author(s):  
András Schubert
Keyword(s):  
Medical Journal ◽  
Scientific Knowledge ◽  
Knowledge Network ◽  
Scientific Journals ◽  
Scientific Enterprise ◽  
Medical Journals ◽  
The Subject

The role of networks is swiftly increasing in the production and communication of scientific knowledge. Network aspects have, therefore, an ever growing importance in the analysis of the scientific enterprise, as well. The present paper demonstrates some techniques of studying the network of scientific journals on the subject of seeking the position of Orvosi Hetilap (Hungarian Medical Journal) in the international journal network. Orv. Hetil., 2014, 155(22), 876–879.

THE ROLE OF LOCAL GOVERNMENT IN STRENGTHENING DEMOCRACY

2016 ◽  
Vol 3 (1) ◽  
pp. 115-131
Author(s):  
Mbuzeni Mathenjwa
Keyword(s):  
European Union ◽  
Local Government ◽  
United Nations ◽  
Public Interest ◽  
Legal Status ◽  
African Union ◽  
African Countries ◽  
The Status ◽  
Administrative Units

The place and role of local government within the structure of government in Africa has attracted much public interest. Prior to and after independence, African countries used local government as the administrative units of central governments without their having any legal status, to the extent that local authorities were under the strict control of central governments. The autonomy of local government is pivotal in the democratisation of a country. The United Nations, European Union and African Union have adopted treaties to promote the recognition and protection of local government in the state parties’ constitutions. Accordingly, this article explains the status of local government in Africa and its impact on strengthening democracy in African states.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

The role of human resources in processes of portuguese firms’ RD&I certification and development

2014 ◽  
Author(s):  
Sérgio Gomes ◽  
Vítor Braga ◽  
Alexandra Braga
Keyword(s):  
Human Resources ◽  
Competitive Advantage ◽  
Scientific Knowledge ◽  
Management Systems ◽  
Research Development ◽  
Innovation Activities

Innovation is seen as a competitive advantage that many companies use to ensure the continuity and success of your business.NP 4457:2007 is the Portuguese norm that supports management, based on a model of innovation backed up by interfaces and interaction between technical/scientific knowledge, its specific mechanisms and the overall society.Our paper aims to analyse innovation activities and the involvement of human resources in Portuguese firms certified by NP4457 and associated to the implementation of Research, Development, and Innovation (RD&I) management systems. We have collected the data through IPAC’s database, using a survey administered to all firms.Our results suggest the existence of a Human Resources (HR) involvement policy, customers and suppliers. The involvement of stakeholders with innovation activities results of its acceptance as a mechanism able to generate wealth, with benefits for both firms and the community.

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