MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition
03: Computational Sciences, Statistics, Artificial Intelligence, Complex Networks, Machine Learning, and Big Data
This section covers the different applications of computer sciences, data analysis, statistics, modelling techniques, etc. in multidisciplinary sciences. The topics include, but are not limited to, connectivity analysis in biology, environment, epidemiological, and social networks; including the computational analysis of metabolic pathways in Metabolomics, Protein interaction networks in proteomics, food webs, and other biological-ecological networks like host-parasite, prey-hunter, etc. Geographical Information Systems (GIS), land covering networks, atmospheric reactions networks. Study of social collaboration, electronic social networks (Facebook, Twitter, etc.), disease spreading networks and epidemiology, vaccination models in epidemic networks, legal and law citing networks, networks in sociology and criminology, etc.
This section covers also: technological, industrial, and economic connectivity, including the analysis of computer connectivity, Internet, wireless networks, satellite networks, electrical networks, airport and other transport networks, financial networks, trade networks, etc. In addition, we cover pure theoretical aspects in network science and data analysis theory, including but not limited to theoretical studies in network sciences, topological indices, node centrality, network robustness, multiplex networks, network attack, and new spatial statistical analysis, time series analysis, biostatistics, machine learning and big data analysis methods.
This section is also aimed at presenting the most commonly used software tools in Multidisciplinary Science. Include, but is not limited to, new scientific software, web servers, databases, etc. with applications in Chemistry (all branches), Bioinformatics, Proteomics, Biotechnology, Medical Informatics and Biomedical Engineering, Computer Science, etc.
The short communications should present computational tools that may be desktop/web/mobile applications/scripts, open code or private software. The tool may be original or a pipe of other tools. It should contain a software description, case uses in order to understand how to employ it, links to the open repositories (GitHub, GitLab, Personal Webs, etc.) or official Webs of the private products, and references of the publications where the tools have been applied. The authors may include in the communication a link to their personal webs, web servers, repositories, databases, etc.
Special attention will be paid to the links to tutorials (blogs, videos, etc.), print screens with the tools in action, pseudocodes, examples of input and outputs, script examples while using the tools, and links to the social network posts for the tools. The emphasis of this section is on the software per se. Communications that make use of a software to solve a practical problem but do not put emphasis on describing it could be suitable for other sections.
We also welcome submissions related to: Client–server model — Client–server computing, Grid computing with a cluster of networked, loosely coupled computers to perform very large tasks, Distributed Fog computing paradigm, Dew computing, Mainframe computer for big data processing in large research organizations, Utility computing, and Peer-to-peer computing alternatives in science and medical informatics.
Enjoy programming for science!
See examples in similar sections of the last editions:
MOL2NET 2016, Section 03 (Computational Sciences)
MOL2NET 2015, Section E (Statistics, Artificial Intelligence)
Thank you for your support!!!
Prof. González-Díaz H., IKERBASQUE Professor, Email: email@example.com
(1) Department of Organic Chemistry II, University of the Basque Country UPV/EHU , 48940, Leioa, Biscay, Spain.
(2) IKERBASQUE, Basque Foundation for Science , 48011, Bilbao, Biscay, Spain.