Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences
A special issue of Data (ISSN 2306-5729).
Deadline for manuscript submissions: closed (30 November 2016) | Viewed by 579
Special Issue Editors
2. IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
Interests: cheminformatics; bioinformatics; machine learning; complex networks; computational nanoscience
Special Issues, Collections and Topics in MDPI journals
Interests: chemometric; QSAR/QSPR; multi-criteria decision making; molecular descriptors; software development
Special Issues, Collections and Topics in MDPI journals
Interests: big data analysis; machine learning, artificial intelligence; bioinformatics; cheminformatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
There is a steady increasing necessity of multidisciplinary collaborations in molecular science between experimentalists and theoretical scientists, as well as among theoretical scientists from different fields. One of the more important forces driving this necessity is the accumulation of large amounts of data as results of important advances in Chemometrics and Molecular Sciences Experimental Techniques of data acquisition in general.
In this context, we decided to create [MD1] a new scientific conference to promote the scientific synergies expressed earlier. MOL2NET (the conference's running title) was held from 15–30 December, 2015, on the SciForum platform. The official website of the conference is: http://sciforum.net/conference/mol2net-1. Represented disciplines will encompass the molecular and biomedical sciences, social networks analysis, and beyond. More specifically, this conference aims to promote scientific synergies between groups of experimental molecular and bio-medical scientists. Relevant fields include chemistry, pharmacology, cancer research, proteomics, the neurosciences, the nanosciences, and epidemiology. Moreover, the conference welcomes computational and social sciences experts from different areas, such as computational chemistry, bioinformatics, social networks analysis, big data predictive analytics, biostatistics, etc. The full title of this conference is the 1st International Conference on Synergies of Experimental Groups of Molecular and Biomedical Sciences with Data, Networks, and Social Sciences Experts. The conference per se is the result of the synergy between the Department of Organic Chemistry, University of Basque Country (UPV/EHU), and IKERBASQUE, Basque Foundation for Sciences, with the Faculty of Informatics, University of Coruña (UDC).
In order to strengthen and spread the outputs of MOL2NET, we decided to be the Guest Editors for this affiliated Special Issue with the one published in the journal IJMS (https://www.mdpi.com/journal/ijms/special_issues/QSAR_QSPR_Chemistry). In consonance with the conference, the topic of the issue is: Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences. The Special Issue is focused on the development and application of different theoretical algorithms combining Chemoinformatics, Computational Chemistry, Bioinformatics, Data Analysis, and Network Science methods. Submissions of other authors that do not attend the conference are also welcome. Data descriptors will be published in the journal Data, which is an open access publication journal of MDPI, in the field of data in sciences (https://www.mdpi.com/journal/data).
Prof. Dr. Humberto González-Díaz (IKERBASQUE Senior Professor)
Prof. Dr. Alejandro Pazos Sierra
Prof. Dr. Roberto Todeschini
Dr. Sonia Arrasate Gil
Guest Editors
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Keywords
- Big Data Analysis in Chemometrics
- QSPR Chemoinformatics models of Chemical Reactivity
- Computer-Aided Drug Discovery (CADD) with QSAR models
- DNA/Protein Quantitative Sequence-Activity Models (QSAM) in Bioinformatics
- Structure-Property Relationships Analysis of Bio-Molecular Networks
- Prediction of Drug-Target Interaction Networks
- Computational Proteomics and Metabolomics
- Protein Interaction Networks
- Machine Learning in Cheminformatics
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