Search results for "RNR"
showing 10 items of 302 documents
New Tools for Streamlined In Vivo Homing Peptide Identification
2021
In vivo peptide-phage display is an unbiased technique for mapping of the vascular diversity and identification of homing peptides. This chapter is intended to serve as a structured practical guide to execute in vivo T7 phage biopanning and data analysis experiments. We discuss experimental designs and protocols with emphasis on application of high-throughput sequencing-based technologies for streamlined in vivo biopanning and validation of homing peptides.
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistic…
2008
Abstract Background Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. Results We consider five such measures: Clest, Consensus (Consensus Clustering), FOM (Figure of Merit), Gap (Gap Statistics) and ME (Model Explorer), in addition to the classic WCSS (Within Cluster…
Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space
2019
In this paper, a clustering based surrogate is proposed to be used in offline data-driven multiobjective optimization to reduce the size of the optimization problem in the decision space. The surrogate is combined with an interactive multiobjective optimization approach and it is applied to forest management planning with promising results. peerReviewed
Principal Component and Neural Network Analyses of Face Images: What Can Be Generalized in Gender Classification?
1998
We present an overview of the major findings of the principal component analysis (pca) approach to facial analysis. In a neural network or connectionist framework, this approach is known as the linear autoassociator approach. Faces are represented as a weighted sum of macrofeatures (eigenvectors or eigenfaces) extracted from a cross-product matrix of face images. Using gender categorization as an illustration, we analyze the robustness of this type of facial representation. We show that eigenvectors representing general categorical information can be estimated using a very small set of faces and that the information they convey is generalizable to new faces of the same population and to a l…
Methodological advances in the functional profiling of genomic studies
2013
In this thesis we present bioinformatic tools and algorithms for the analysis of genomic data such as those generated by microarray devices or next generation sequencing techniques. Particularly, we develop new approaches to gene set analysis. The described procedures should be useful in practice to tackle complex biological experiments, but hopefully will also be methodologically relevant, as they introduce new ways of conceptualizing genomic functional profiling. Our very flexible approach allows for the inclusion of not just one kind of genomic measurement but many. It makes possible, for instance, to analyze expression measurement and genomic variation data at a time. This multidimensio…
Large-scale nonlinear dimensionality reduction for network intrusion detection
2017
International audience; Network intrusion detection (NID) is a complex classification problem. In this paper, we combine classification with recent and scalable nonlinear dimensionality reduction (NLDR) methods. Classification and DR are not necessarily adversarial, provided adequate cluster magnification occurring in NLDR methods like $t$-SNE: DR mitigates the curse of dimensionality, while cluster magnification can maintain class separability. We demonstrate experimentally the effectiveness of the approach by analyzing and comparing results on the big KDD99 dataset, using both NLDR quality assessment and classification rate for SVMs and random forests. Since data involves features of mixe…
Hybrid vibration signal monitoring approach for rolling element bearings
2019
New approach to identify different lifetime stages of rolling element bearings, to improve early bearing fault detection, is presented. We extract characteristic features from vibration signals generated by rolling element bearings. This data is first pre-labelled with an unsupervised clustering method. Then, supervised methods are used to improve the labelling. Moreover, we assess feature importance with each classifier. From the practical point of view, the classifiers are compared on how early emergence of a bearing fault is being suggested. The results show that all of the classifiers are usable for bearing fault detection and the importance of the features was consistent. peerReviewed
Analysis of the endometrial microbiome and its impact on human reproduction
2021
La implantación es un proceso complejo que requiere la sincronización entre un endometrio receptivo y un blastocisto en desarrollo. En los últimos años, gracias a los avances en las técnicas de secuenciación, se han identificado microorganismos en el útero y se ha visto que pueden tener un efecto sobre los resultados reproductivos. Por ello, el estudio del microbioma endometrial está ganando cada vez más interés en las clínicas de reproducción asistida. En este estudio, planteamos la hipótesis de que la presencia de patógenos bacterianos en el útero tiene consecuencias negativas en la salud reproductiva. El objetivo principal fue caracterizar en profundidad el microbioma endometrial y su im…
A web-based collection of genotype-phenotype associations in hereditary recurrent fevers from the Eurofever registry
2017
PubMed ID: 29047407
Multitask deep learning for native language identification
2020
Identifying the native language of a person by their text written in English (L1 identification) plays an important role in such tasks as authorship profiling and identification. With the current proliferation of misinformation in social media, these methods are especially topical. Most studies in this field have focused on the development of supervised classification algorithms, that are trained on a single L1 dataset. Although multiple labeled datasets are available for L1 identification, they contain texts authored by speakers of different languages and do not completely overlap. Current approaches achieve high accuracy on available datasets, but this is attained by training an individua…