Search results for "Biological classification"

showing 7 items of 7 documents

Chemodiversity of the Essential Oil from Leaves of Abies nebrodensis (Lojac.) Mattei

2016

Abies nebrodensis (Lojac.) Mattei (Pinaceae) is a species occurring in a very small population only in a restricted area of Sicily. Its taxonomic classification as different species has been object of discussion. In this work the chemical composition of the essential oil from the leaves is presented for the first time and compared to the essential oils from other euroasiatic species reported in literature. Peculiar characteristics of the essential oil of A. nebrodensis are highlighted.

0106 biological sciencesAbies nebrodensiPlant compositionPopulationBioengineering01 natural sciencesPlant OilBiochemistryEssential oilGas Chromatography-Mass Spectrometrylaw.inventionSettore BIO/01 - Botanica Generalechemistry.chemical_compoundlawBotanyAbieOils VolatilePlant OilsSettore BIO/15 - Biologia FarmaceuticaAbies nebrodensiseducationMolecular BiologyEssential oilbeta-Pineneeducation.field_of_studybiologySettore BIO/02 - Botanica SistematicaChemistry (all)General ChemistryGeneral MedicineBiological classificationβ-PineneSettore CHIM/06 - Chimica Organicabiology.organism_classification0104 chemical sciencesPlant LeavesChemotaxonomy010404 medicinal & biomolecular chemistrychemistryChemotaxonomyPinaceaeMolecular MedicinePlant LeaveAbies010606 plant biology & botany
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Deep learning models for bacteria taxonomic classification of metagenomic data.

2018

Background An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions.…

0301 basic medicineTime FactorsDBNComputer scienceBiochemistryStructural BiologyRNA Ribosomal 16SDatabases Geneticlcsh:QH301-705.5Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazionibiologySettore INF/01 - InformaticaShotgun sequencingApplied MathematicsAmpliconClassificationComputer Science Applicationslcsh:R858-859.7DNA microarrayShotgunAlgorithmsCNN030106 microbiologyk-mer representationlcsh:Computer applications to medicine. Medical informaticsDNA sequencing03 medical and health sciencesMetagenomicDeep LearningMolecular BiologyBacteriaModels GeneticPhylumbusiness.industryDeep learningResearchReproducibility of ResultsPattern recognitionBiological classification16S ribosomal RNAbiology.organism_classificationAmpliconHypervariable region030104 developmental biologyTaxonlcsh:Biology (General)MetagenomicsMetagenomeArtificial intelligenceMetagenomicsNeural Networks ComputerbusinessClassifier (UML)BacteriaBMC bioinformatics
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Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

2017

Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential f…

0301 basic medicinelcsh:QH426-470Taxonomic classificationADNCodificació Teoria de laBiologyBioinformaticsMachine learningcomputer.software_genreDNA; genes; taxonomic classification; convolutional neural networks; encodingConvolutional neural networkArticle03 medical and health sciences0302 clinical medicineBiologia -- ClassificacióEncoding (memory)convolutional neural networksGeneticstaxonomic classificationSensitivity (control systems)genesGenetics (clinical)ta113Biology -- Classificationbusiness.industryBiological classificationCoding theoryDNAencodinglcsh:Genetics030104 developmental biologyGenes030220 oncology & carcinogenesisEncodingConvolutional neural networksArtificial intelligenceCoding theorybusinesscomputerGens
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The Subject Matter and Methods of Comparative Biochemistry

1994

The diversity of living organisms, the abundance of forms, colours and phenotypes, is one of the most impressive aspects of our world. Since ancient times, science has concerned itself with the ordering and classification of this diversity. At first, only those structures and processes which were recognizable without any special apparatus could be compared; the invention of the light microscope and the electron microscope introduced new dimensions and new opportunities for comparison. Finally, progress to the molecular level became possible with the development of methods of biochemical analysis. Different comparative biological disciplines arose, each with particular goals and methods. The…

Comparative BiochemistryGeographyMolecular levelTaxonomy (general)media_common.quotation_subjectBiological classificationEpistemologyDiversity (politics)media_commonSubject matter
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Not just BLAST nt: WGS database joins the party

2019

AbstractSince its introduction in 1990 and with over 50k citations, the NCBI BLAST family has been an essential tool of in silico molecular biology. The BLAST nt database, based on the traditional divisions of GenBank, has been the default and most comprehensive database for nucleotide BLAST searches and for taxonomic classification software in metagenomics. Here we argue that this is no longer the case. Currently, the NCBI WGS database contains one billion reads (almost five times more than GenBank), and with 4.4 trillion nucleotides, WGS has about 14 times more nucleotides than GenBank. This ratio is growing with time. We advocate a change in the database paradigm in taxonomic classificat…

DatabaseMetagenomicsGenBankBasic Local Alignment Search ToolJoinsBiological classificationConfidence scorecomputer.software_genrecomputerDe facto standard
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Towards a functional classification of the freshwater phytoplankton

2002

This paper considers the structure of freshwater phytoplankton assemblages and promotes a scheme of ‘vegetation recognition’, based upon the functional associations of species represented in the plankton. These groups are often polyphyletic, recognizing commonly shared adaptive features, rather than common phylogeny, to be the key ecological driver. Thirty-one such associations are outlined and the basic pattern of their distinctive ecologies is outlined. An invitation to other plankton scientists to assist in the development of this scheme is issued.

EcologyEcologyPolyphylyfungiPhytoplanktonSettore BIO/03 - Botanica Ambientale E ApplicataAquatic SciencePlanktonBiologyPhytoplankton Functional Groups Biological ClassificationEcology Evolution Behavior and Systematics
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Goal Oriented Soil Mapping

2017

Abstract Soil mapping is very important for the correct implementation of sustainable land use management. In recent decades, soil mapping methods and data availability have increased exponentially, improving the quality of the maps produced. Despite these advances, local knowledge is a great source of information, refined for centuries and useful for soil mapping and the implementation of a sustainable land management. Local wisdom and experience should be an important aspect of soil mapping because farmers will be one of the major end-users of the maps produced and they should account for the farmers’ reality. However, several problems have been identified in the spatial correlation betwe…

Soil mapSustainable land managementGoal orientationLand useManagement sciencebusiness.industrymedia_common.quotation_subjectEnvironmental resource managementFolk classificationSoil classification04 agricultural and veterinary sciencesBiological classification010501 environmental sciences01 natural sciencesGeneralLiterature_MISCELLANEOUSGeography040103 agronomy & agriculture0401 agriculture forestry and fisheriesQuality (business)business0105 earth and related environmental sciencesmedia_common
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