Special Issue "Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models"
Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 37180
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
Special Issue in International Journal of Molecular Sciences: Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences
Special Issue in Data: Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences
Special Issue in Nanomaterials: Experimental Nanosciences, Computational Chemistry, and Data Analysis
Special Issue in Applied Sciences: Complex Networks and Machine Learning: From Molecular to Social Sciences
Special Issue in Molecules: Cheminformatics, Past, Present, and Future: From Chemistry to Nanotechnology and Complex Systems
Modern experimental techniques used in biomolecular research produce a large amount of data. These techniques include like next-generation sequencing, molecular NMR, iNMR imaging, 2DE and MS in proteomics, and EEG in neurosciences. The data produced, sometimes coined big data, have been collected in public databases online (e.g., ChEMBL, GeneBank, PDB, PubChem, KEGG, NLM, and AIDSvu). The big data sets may give important clues for knowledge discovery, translational research, and personalized medicine if we can analyze them properly. This in turn may result in the development of new applications for omics, drug discovery, vaccine design, biomarkers discovery, neurosciences, and biomedical engineering, etc.
However, most of these big data sets present certain features that difficult the analysis. We can summarize may of these problems, shortly, as big data = 5V + C data features. The 5Vs include problems with data volume, veracity, variability, velocity, and value. The C refers to the complexity of data due to in part of a high number of interconnections among variables in the complex systems studied. This is due to the existence of big data sets forming complex networks in Systems Biology. Examples of these complex networks are due to multiple drugs interacting with different target proteins (drug-target networks), protein–protein interactions networks (PINS), gene–gene regulatory networks (GRN), etc.
In this context, we may need complex network analysis tools to capture the complexity of the data and lulti-label machine learning (ML) algorithms to find predictive models for these data about systems with multiple biological properties (IC50, Ki, Km, LD50, etc.) and multiple labels (drugs, proteins, cell lines, tissues, brain regions, organisms, populations, etc.).
Last but not the least, the use of all these computational techniques to process biomolecular data becomes even more important if we develop computational biomedical engineering systems for translational and personalized medicine. In consequence, ML algorithms have to merge data from preclinical assays (as in ChEMBL databases) with data from clinical assays with personal data information. In this sense, the use of the previous data analysis tools in biomolecular sciences has to consider the legal aspects relevant to personal data protection, software copyright, etc., as well; see, e.g., GDPR in Europe, REACH, and OECD regulations.
Consequently, in this new Issue we propose to open a forum for the publication of technical aspects and new applications or results (software, databases, cheminformatic models, machine learning algorithms, and complex network tools) and the discussion of the ethical and legal implications of these tools as well.
The present Special Issue is also associated with MOL2NET-05, the International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2019. The conference has its HQs in University of Basque Country (UPV/EHU) and is supported by Professors of Ikerbasque: Basque Foundation for Sciences, Harvard Medicine School, UNC Chapel Hill, EMBL-EBI United Kingdom, CNAM Paris, Miami Dade College (MDC), University of Coruña (UDC), etc. The MOL2NET series is hosting more than 10 workshops with in-person and/or online participation every year in universities in the USA, Europe, Brazil, China, India, etc. In addition, the conference hosts the USEDAT: USA-Europe Data Analysis Training School, focused on training students worldwide in data analysis, with an emphasis in cheminformatics. The members of the committee have also guest edited other Special Issues in multiple MDPI journals. Please see the link of the conference at https://mol2net-05.sciforum.net/
We especially encourage submissions of papers from colleagues worldwide to the conference (short communications) and complete versions (full papers) to the present Special Issue. Prof. Dr. Humbert González-Díaz
Prof. Dr. Humbert González-Díaz
Manuscript Submission Information
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- big data
- complex networks
- systems biology
- machine learning