pinecone/core-2020-05-10-deduplication huggingface dataset:
Nonlinear mixture-wise expansion approach to underdetermined blind separation of nonnegative dependent sources

Underdetermined blind separation of nonnegative dependent sources consists in decomposing set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixture-wise mapping of observed data in high-dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness constrained nonnegative matrix factorization (NMF) therein. Thus, original problem is converted into new one with increased number of mixtures, increased number of dependent sources and higher-order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, that is the case for mass spectra of analytes, sparseness constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on original problem. The method is exemplified on numerical and experimental examples related respectively to extraction of ten dependent components from five mixtures and to extraction of ten dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples
Probabilistic model-based discriminant analysis and clustering methods in chemometrics

International audienceIn chemometrics, the supervised and unsupervised classification of high-dimensional data has become a recurrent problem. Model-based techniques for discriminant analysis and clustering are popular tools, which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappoint- ing behaviour in high-dimensional spaces, which up to now have been limited in their use within chemometrics. The recent developments in model-based classification overcame these drawbacks and enabled the efficient classifica- tion of high-dimensional data, even in the 'small n / large p' condition. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modelling, subspace classifica- tion methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in chemometrics using R packages
Classification and unscrambling a class-inside-class situation by object target rotation : Hungarian silver coins of the Árpád Dynasty, 997 – 1301 AD

Classification is an important part of chemometrics and mostly based on optimization by\ud
vector rotations. The present study is a continuation of the classification of medieval\ud
Hungarian silver coins including the 16 kings of the Hungarian Árpád Dynasty (997AD –\ud
1301AD) (Rácz et al.: Heritage Science 2013 1:2) The Rácz et al. paper identified three\ud
historical periods of the Árpád Dynasty from chemical data. The aim of the present study is to\ud
test whether the classification could be further refined by marker object projection aided\ud
classification. It offers an example of the efficiency of this method in unscrambling a classinside-\ud
class situation.\ud
The frequency distribution of concentrations of the coins are skewed and to a certain\ud
extent bi- modal, and the arithmetic mean value and standard deviation around the mean\ud
frequently used in parametric methods may be poor descriptors of the information carried by\ud
the data. We test a combination of principal components decomposition and the nonparametric,\ud
non-iterative object target rotation method to overcome some of the theoretical\ud
limitations of parametric methods. This test includes identification of archetypical class\ud
―Ambassadors‖ of each of the three historical periods of the Árpád Dynasty and shows a\ud
class-inside-class situation
Empirical kernel map approach to nonlinear underdetermined blind separation of sparse nonnegative dependent sources: pure component extraction from nonlinear mixture mass spectra

Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing set of observed nonlinearly mixed signals into greater number of original nonnegative and dependent component (source) signals. That hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of nonlinear multicomponent mixtures. This paper presents method for nonlinear underdetermined blind separation of nonnegative dependent sources that comply with sparse probabilistic model, i.e. sources are constrained to be sparse in support and amplitude. That model is validated on experimental pure components mass spectra. Under sparse prior nonlinear problem is converted into equivalent linear one comprised of original sources and their higher-, mostly second, order monomials. Influence of these monomials, that stand for error terms, is reduced by preprocessing matrix of mixtures by means of robust principal component analysis, hard-, soft- and trimmed thresholding. Preprocessed data matrices are mapped in high-dimensional reproducible kernel Hilbert space (RKHS) of functions by means of empirical kernel map. Sparseness constrained nonnegative matrix factorizations (NMF) in RKHS yield sets of separated components. They are assigned to pure components from the library using maximal correlation criterion. The methodology is exemplified on demanding numerical and experimental examples related respectively to extraction of 8 dependent components from 3 nonlinear mixtures and to extraction of 25 dependent analytes from 9 nonlinear mixtures mass spectra recorded in nonlinear chemical reaction of peptide synthesis
Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

The nonlinear, nonnegative single-mixture blind source separation (BSS) problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined BSS problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra ; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi-mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi-mixture into a high-dimensional reproducible kernel Hilbert space (RKHS). Under sparse probabilistic conditions that were previously imposed on sources, the single-mixture nonlinear problem is converted into an equivalent linear, multiple-mixture problem that consists of the original sources and their higher order monomials. These monomials are suppressed by robust principal component analysis, hard-, soft- and trimmed thresholding. Sparseness constrained nonnegative matrix factorizations in RKHS yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of 8 dependent components from 1 nonlinear mixture. The method is further demonstrated on 3 nonlinear chemical reactions of peptide synthesis in which 25, 19 and 28 dependent analytes are extracted from 1 nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry-based non-targeted metabolic profiling, such as biomarker identification studies
Parameter selection guidelines for adaptive PCA-based control charts

Methods based on principal component analysis (PCA) are widely used for statistical process monitoring of highdimensional
processes. Allowing the monitoring model to update as new observations are acquired extends this
class of approaches to non-stationary processes. The updating procedure is governed by a weighting parameter that
defines the rate at which older observations are discarded, and therefore, it greatly affects model quality and monitoring
performance. Additionally, monitoring non-stationary processes can require adjustments to the parameters
defining the control limits of adaptive PCA in order to achieve the intended false detection rate. These two aspects
require careful consideration prior the implementation of adaptive PCA. Towards this end, approaches are given in
this paper for both parameter selection challenges. Results are presented for a simulation and two real-life industrial
process examples.status: publishe
A new penalized nonnegative third order tensor decomposition using a block coordinate proximal gradient approach: application to 3D fluorescence spectroscopy

International audienceIn this article, we address the problem of tensor factorization subject to certain constraints. We focus on the Canonical Polyadic Decomposition (CPD) also known as Parafac. The interest of this multi-linear decomposition coupled with 3D fluorescence spectroscopy is now well established in the fields of environmental data analysis, biochemistry and chemistry. When real experimental data (possibly corrupted by noise) are processed, the actual rank of the " observed " tensor is generally unknown. Moreover, when the amount of data is very large, this inverse problem may become numerically ill-posed and consequently hard to solve. The use of proper constraints reflecting some a priori knowledge about the latent (or hidden) tracked variables and/or additional information through the addition of penalty functions can prove very helpful in estimating more relevant components rather than totally arbitrary ones. The counterpart is that the cost functions that have to be considered can be non convex and sometimes even non differentiable making their optimization more difficult, leading to a higher computing time and a slower convergence speed. Block alternating proximal approaches offer a rigorous and flexible framework to properly address that problem since they are applicable to a large class of cost functions while remaining quite easy to implement. Here, we suggest a new block coordinate variable metric forward-backward method which can be seen as a special case of Majorize-Minimize (MM) approaches to derive a new penalized nonnegative third order CPD algorithm. Its interest, efficiency, robustness and flexibility are illustrated thanks to computer simulations carried out on both simulated and real experimental 3D fluorescence spectroscopy data
Complete determination of plant tissues based only on auto-fluorescence and the advanced image analysis – study of needles and stamens

Finding the procedure for proper tissue determination is a challenging task in various fields of biology and medicine, unfortunately often affected by subjectivity of the histologist. We have introduced the method based on L0-norm constrained nonnegative matrix factorization and compared it with several algorithms on controlled, simulated set of images. The key advantage is the ability to extract much more components from the starting set of images (often small number) in comparison to other methods, providing more versatile set of parameters for tissue classification
USDA stakeholder workshop on animal bioinformatics: Summary and recommendations

Copyright © Hindawi Publishing Corporation.An electronic workshop was conducted on 4 November–13 December 2002 to discuss current issues and needs in animal bioinformatics. The electronic (e-mail listserver) format was chosen to provide a relatively speedy process that is broad in scope, cost-efficient and easily accessible to all participants. Approximately 40 panelists with diverse species and discipline expertise communicated through the panel e-mail listserver. The panel included scientists from academia, industry and government, in the USA, Australia and the UK. A second ‘stakeholder’ e-mail listserver was used to obtain input from a broad audience with general interests in animal genomics. The objectives of the electronic workshop were: (a) to define priorities for animal genome database development; and (b) to recommend ways in which the USDA could provide leadership in the area of animal genome database development. E-mail messages from panelists and stakeholders are archived at http://genome.cvm.umn.edu/bioinfo/. Priorities defined for animal genome database development included: (a) data repository; (b) tools for genome analysis; (c) annotation; (d) practical application of genomic data; and (e) a biological framework for DNA sequence. A stable source of funding, such as the USDA Agricultural Research Service (ARS), was recommended to support maintenance of data repositories and data curation. Continued support for competitive grants programs within the USDA Cooperative State Research, Education and Extension Service (CSREES) was recommended for tool development and hypothesis-driven research projects in genome analysis. Additional stakeholder input will be required to continuously refine priorities and maximize the use of limited resources for animal bioinformatics within the USDA.Debora L. Hamernik and David L. Adelso
Association of polymorphisms in the human IL4 and IL5 genes with atopic bronchial asthma and severity of the disease.

15/07/14 meb. Publisher pdf attached, Ok to add.Two polymorphisms in the IL4 (G/C 3-UTR) and IL5 (C-703T) genes were studied in a sample of families whose probands had atopic bronchial asthma (BA) (66 families, n = 183) and in a group of non-cognate individuals with the severe form of the disease (n = 34). The samples were collected from the Russian population in the city of Tomsk (Russia). Using the transmission/disequilibrium test (TDT), a significant association of allele C-703 IL5 with BA was established (TDT = 4.923, p = 0.007 +/- 0.0007). The analysis of 40 individuals with mild asthma and 49 patients with the severe form of the disease revealed a negative association of genotype GG IL4 (OR = 0.39, 95% CI = 0.15-0.99, p = 0.035), and also a trend towards a positive association of the GC IL4 genotype (OR = 2.52, 95% CI = 0.98-6.57, p = 0.052) with mild BA. There was a concordance of the clinical classification of BA severity with the genotype (McNemars chi(2) test with continuity correction constituted 0.03, d.f. = 1, p = 0.859). These results suggest that polymorphisms in the IL4 and IL5 genes contribute to the susceptibility to atopic BA and could determine the clinical course of the disease
Development of computational tools for the inference of protein interaction specificity rules and functional annotation using structural information

Relatively few protein structures are known, compared to the enormous amount of sequence data produced in the sequencing of different genomes, and relatively few protein complexes are deposited in the PDB with respect to the great amount of interaction data coming from high-throughput experiments (two-hybrid or affinity purification of protein complexes and mass spectrometry). Nevertheless, we can rely on computational techniques for the extraction of high-quality and information-rich data from the known structures and for their spreading in the protein sequence space. We describe here the ongoing research projects in our group: we analyse the protein complexes stored in the PDB and, for each complex involving one domain belonging to a family of interaction domains for which some interaction data are available, we can calculate its probability of interaction with any protein sequence. We analyse the structures of proteins encoding a function specified in a PROSITE pattern, which exhibits relatively low selectivity and specificity, and build extended patterns. To this aim, we consider residues that are well-conserved in the structure, even if their conservation cannot easily be recognized in the sequence alignment of the proteins holding the function. We also analyse protein surface regions and, through the annotation of the solvent-exposed residues, we annotate protein surface patches via a structural comparison performed with stringent parameters and independently of the residue order in the sequence. Local surface comparison may also help in identifying new sequence patterns, which could not be highlighted with other sequence-based methods
Analysis of known bacterial protein vaccine antigens reveals biased physical properties and amino acid composition

Copyright © 2003 John Wiley & Sons, Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Many vaccines have been developed from live attenuated forms of bacterial pathogens or from killed bacterial cells. However, an increased awareness of the potential for transient side-effects following vaccination has prompted an increased emphasis on the use of sub-unit vaccines, rather than those based on whole bacterial cells. The identification of vaccine sub-units is often a lengthy process and bioinformatics approaches have recently been used to identify candidate protein vaccine antigens. Such methods ultimately offer the promise of a more rapid advance towards preclinical studies with vaccines. We have compared the properties of known bacterial vaccine antigens against randomly selected proteins and identified differences in the make-up of these two groups. A computer algorithm that exploits these differences allows the identification of potential vaccine antigen candidates from pathogenic bacteria on the basis of their amino acid composition, a property inherently associated with sub-cellular location
A survey of nucleotide cyclases in actinobacteria: unique domain organization and expansion of the class III cyclase family in Mycobacterium tuberculosis.

09.01.15 KB. Ok to add published version to spiral, OA paperCyclic nucleotides are well-known second messengers involved in the regulation of important metabolic pathways or virulence factors. There are six different classes of nucleotide cyclases that can accomplish the task of generating cAMP, and four of these are restricted to the prokaryotes. The role of cAMP has been implicated in the virulence and regulation of secondary metabolites in the phylum Actinobacteria, which contains important pathogens, such as Mycobacterium tuberculosis, M. leprae, M. bovis and Corynebacterium, and industrial organisms from the genus Streptomyces. We have analysed the actinobacterial genome sequences found in current databases for the presence of different classes of nucleotide cyclases, and find that only class III cyclases are present in these organisms. Importantly, prominent members such as M. tuberculosis and M. leprae have 17 and 4 class III cyclases, respectively, encoded in their genomes, some of which display interesting domain fusions seen for the first time. In addition, a pseudogene corresponding to a cyclase from M. avium has been identified as the only cyclase pseudogene in M. tuberculosis and M. bovis. The Corynebacterium and Streptomyces genomes encode only a single adenylyl cyclase each, both of which have corresponding orthologues in M. tuberculosis. A clustering of the cyclase domains in Actinobacteria reveals the presence of typical eukaryote-like, fungi-like and other bacteria-like class III cyclase sequences within this phylum, suggesting that these proteins may have significant roles to play in this important group of organisms
MicroCore: Mapping Genome Expression to Cell Pathways and Networks

The MicroCore toolkit is a suite of analysis programs for microarray and proteomics
data that is open source and programmed exclusively in Java. MicroCore provides
a flexible and extensible environment for the interpretation of functional genomics
data through visualization. The first version of the application (downloadable from
the MicroCore website: http://www.ucl.ac.uk/oncology/MicroCore/microcore.htm),
implements two programs—PIMs (protein interaction maps) and MicroExpress—and
 is soon to be followed by an extended version which will also feature a fuzzy
k-means clustering application and a Java-based R plug-in for microarray analysis.
PIMs and MicroExpress provide a simple yet powerful way of graphically relating
large quantities of expression data from multiple experiments to cellular pathways
and biological processes in a statistically meaningful way
Interactome data and databases: Different types of protein interaction

6 páginas, 1 figura.-- Conference review.-- This is an open access article distributed under the Creative Commons Attribution License.In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of 'protein-protein interactions', i.e. the 'interactome'. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of 'what a protein-protein interaction is' and 'which types of protein-protein interactions are found in a living cell'. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.We thank the support and research funding provided by “Castilla y León” Local Government (JCyL project ref. SA104/03) and the Spanish Ministry of Health (MSyC project ref. P1030920).Peer Reviewe