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008			170622s2017 sz a ob 101 0 eng d
020			_a3319608169 _q(electronic bk.)
020			_a9783319608167 _q(electronic bk.)
020			_z3319608150
020			_z9783319608150
040			_aN$T _cN$T _dEBLCP _dN$T _dYDX _dGW5XE _dOCLCF _dUAB _dAZU _dUPM _dSTF _dMERER _dESU _dOCLCQ _dCOO _dOCLCQ _dIOG _dOCLCO _dES-MaUEC _bspa
050		4	_aQH324.2 _b2017 EB
111	2		_aInternational Conference on Practical Applications of Computational Biology & Bioinformatics _n(11th : _d2017 : _cPorto, Portugal)
245	1	0	_a11th International Conference on Practical Applications of Computational Biology & Bioinformatics _cFlorentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel P. Rocha, Juan F. De Paz, Tiago Pinto, editors
264		1	_aCham, Switzerland _bSpringer _c[2017]
300			_a1 recurso en línea _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aAdvances in intelligent systems and computing _vvolume 616
500			_aSpringerLink
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface; Organization; General Co-chairs; Program Committee; Organising Committee; PACBB 2016 Sponsors; Contents; S2P: A Desktop Application for Fast and Easy Processing of 2D-Gel and MALDI-Based Mass Spectrometry ... ; Abstract; 1 Introduction; 2 Materials and Methods; 2.1 Case Study; 2.2 Implementation; 3 Results and Discussion; 4 Conclusions; Acknowledgements; References; Multi-Enzyme Pathway Optimisation Through Star-Shaped Reachable Sets; 1 Introduction; 1.1 Multi-Enzyme Pathways; 1.2 Mathematical Setup; 1.3 Optimal Control; 2 Star-Shaped Reachable Sets.
505	8		_a2 Material and Methods2.1 Dataset and Tools; 2.2 Clustering; 2.3 Clustering Validation; 2.4 Feature Selection; 2.5 Classification; 3 Results and Discussion; 4 Conclusion; Acknowledgements; References; Development of Text Mining Tools for Information Retrieval from Patents; 1 Introduction; 2 Patent Pipeline Development; 3 Results; 4 Conclusions; References; How Can Photo Sharing Inspire Sharing Genomes?; 1 Introduction; 2 An Analogy Between Sharing Photos and Sharing Genomes; 2.1 Some Portions of Data Are More Privacy-Sensitive Than Others; 2.2 One's Data May Affect the Privacy of Others.
505	8		_a2.1 Reachable Sets and Optimization2.2 Star-Shaped Sets Generated by Multi-Enzyme Pathway; 2.3 Examples; 3 Conclusions; References; Automated Collection and Sharing of Adaptive Amino Acid Changes Data; Abstract; 1 Introduction; 2 ADOPS Batch Mode; 3 B+ Database Implementation; 4 Conclusion; Acknowledgements; References; ROC632: An Overview; 1 Introduction; 1.1 ROC Curves; 1.2 Area Under the ROC Curve; 1.3 The Bootstrap Method; 2 Materials and Methods; 3 Results and Discussion; 3.1 Evaluation of the boot. ROC Function; 3.2 Assessment of the boot. ROCt Function; 4 Conclusions; References.
505	8		_aK-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression DataAbstract; 1 Introduction; 2 Materials and Methods; 2.1 Datasets and Tools; 2.2 Centroid Clustering Analysis (CCA-I); 2.3 Clustering Validation (CV-II); 2.4 Feature Selection (FS-III); 2.5 Classification (C-IV); 3 Results and Discussion; 3.1 Accuracy and Number of the Selected Genes in the Subset; 3.2 List of the Selected Genes; 4 Conclusion; Acknowledgements; References; Classification of Colorectal Cancer Using Clustering and Feature Selection Approaches; Abstract; 1 Introduction.
505	8		_aProcessing 2D Gel Electrophoresis Images for Efficient Gaussian Mixture ModelingAbstract; 1 Introduction; 2 Materials and Methods; 2.1 Data; 2.2 Processing Methods; 2.3 Gaussian Mixture Modeling; 3 Results and Discussion; 3.1 Spot Detection Performance; 3.2 Goodness of Model Fit; 4 Conclusions; Acknowledgments; References; Improving Document Prioritization for Protein-Protein Interaction Extraction Using Shallow Linguistics and Word Embeddings; 1 Introduction; 2 Methods; 2.1 Data; 2.2 Feature Extraction; 2.3 Document Classification; 3 Results; 4 Conclusions; References.
520	3		_aBiological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next-generation sequencing technologies, together with novel and constantly evolving, distinct types of omics data technologies, have created an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information and requires tools from the computational sciences. In the last few years, we have seen the rise of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance in boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. The PACBB'17 conference was intended to contribute to this effort and promote this fruitful interaction, with a technical program that included 39 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Further, the conference promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists).
588	0		_aOnline resource; title from PDF title page (EBSCO, viewed June 27, 2017).
988			_aEBOOK, EBSPRINGER_2017D
650		7	_aBioinformática _2embne _9160489
700	0		_aMohd Saberi Mohamad, _eeditor literario
700	1		_aFdez-Riverola, Florentino _qFernández-Riverola _eeditor literario _999689
700	1		_aPaz Santana, Juan F. de, _eeditor literario
700	1		_aPinto, Tiago, _eeditor literario
700	1		_aRocha, Miguel P., _eeditor literario _999688
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-60816-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI