Relevant bibliographies by topics / Bioinformatics classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bioinformatics classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bioinformatics classification"

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Wolstencroft, K., P. Lord, L. Tabernero, A. Brass, and R. Stevens. "Protein classification using ontology classification." Bioinformatics 22, no. 14 (2006): e530-e538. http://dx.doi.org/10.1093/bioinformatics/btl208.

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Gasparovica-Asīte, M., and L. Aleksejeva. "Classification Methodology for Bioinformatics Data Analysis." Automatic Control and Computer Sciences 53, no. 1 (2019): 28–38. http://dx.doi.org/10.3103/s0146411619010073.

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Stevens, R., C. Goble, P. Baker, and A. Brass. "A classification of tasks in bioinformatics." Bioinformatics 17, no. 2 (2001): 180–88. http://dx.doi.org/10.1093/bioinformatics/17.2.180.

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Ma, S., and J. Huang. "Penalized feature selection and classification in bioinformatics." Briefings in Bioinformatics 9, no. 5 (2008): 392–403. http://dx.doi.org/10.1093/bib/bbn027.

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Chen, Xiang, Minghui Wang, and Heping Zhang. "The use of classification trees for bioinformatics." WIREs Data Mining and Knowledge Discovery 1, no. 1 (2011): 55–63. http://dx.doi.org/10.1002/widm.14.

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Bicego, Manuele, and Pietro Lovato. "A bioinformatics approach to 2D shape classification." Computer Vision and Image Understanding 145 (April 2016): 59–69. http://dx.doi.org/10.1016/j.cviu.2015.11.011.

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Polaka, Inese, Igor Tom, and Arkady Borisov. "Decision Tree Classifiers in Bioinformatics." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (2010): 118–23. http://dx.doi.org/10.2478/v10143-010-0052-4.

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Decision Tree Classifiers in BioinformaticsThis paper presents a literature review of articles related to the use of decision tree classifiers in gene microarray data analysis published in the last ten years. The main focus is on researches solving the cancer classification problem using single decision tree classifiers (algorithms C4.5 and CART) and decision tree forests (e.g. random forests) showing strengths and weaknesses of the proposed methodologies when compared to other popular classification methods. The article also touches the use of decision tree classifiers in gene selection.
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Hickinbotham, Simon, Sarah Fiddyment, Timothy L. Stinson, and Matthew J. Collins. "How to get your goat: automated identification of species from MALDI-ToF spectra." Bioinformatics 36, no. 12 (2020): 3719–25. http://dx.doi.org/10.1093/bioinformatics/btaa181.

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Abstract Motivation Classification of archaeological animal samples is commonly achieved via manual examination of matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) spectra. This is a time-consuming process which requires significant training and which does not produce a measure of confidence in the classification. We present a new, automated method for arriving at a classification of a MALDI-ToF sample, provided the collagen sequences for each candidate species are available. The approach derives a set of peptide masses from the sequence data for comparison with the sample data, which is carried out by cross-correlation. A novel way of combining evidence from multiple marker peptides is used to interpret the raw alignments and arrive at a classification with an associated confidence measure. Results To illustrate the efficacy of the approach, we tested the new method with a previously published classification of parchment folia from a copy of the Gospel of Luke, produced around 1120 C.E. by scribes at St Augustine’s Abbey in Canterbury, UK. In total, 80 of the 81 samples were given identical classifications by both methods. In addition, the new method gives a quantifiable level of confidence in each classification. Availability and implementation The software can be found at https://github.com/bioarch-sjh/bacollite, and can be installed in R using devtools. Supplementary information Supplementary data are available at Bioinformatics online.
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Rosen, G. L., E. R. Reichenberger, and A. M. Rosenfeld. "NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads." Bioinformatics 27, no. 1 (2010): 127–29. http://dx.doi.org/10.1093/bioinformatics/btq619.

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Dang, T., and T. Man. "Classification of Sarcomas Using Bioinformatics and Molecular Profiling." Current Pharmaceutical Biotechnology 8, no. 2 (2007): 83–91. http://dx.doi.org/10.2174/138920107780487492.

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Dissertations / Theses on the topic "Bioinformatics classification"

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Liang, Jiarong. "Federated Learning for Bioimage Classification." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420615.

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Huque, Enamul. "Shape Analysis and Measurement for the HeLa cell classification of cultured cells in high throughput screening." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-27.

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<p>Feature extraction by digital image analysis and cell classification is an important task for cell culture automation. In High Throughput Screening (HTS) where thousands of data points are generated and processed at once, features will be extracted and cells will be classified to make a decision whether the cell-culture is going on smoothly or not. The culture is restarted if a problem is detected. In this thesis project HeLa cells, which are human epithelial cancer cells, are selected for the experiment. The purpose is to classify two types of HeLa cells in culture: Cells in cleavage that are round floating cells (stressed or dead cells are also round and floating) and another is, normal growing cells that are attached to the substrate. As the number of cells in cleavage will always be smaller than the number of cells which are growing normally and attached to the substrate, the cell-count of attached cells should be higher than the round cells. There are five different HeLa cell images that are used. For each image, every single cell is obtained by image segmentation and isolation. Different mathematical features are found for each cell. The feature set for this experiment is chosen in such a way that features are robust, discriminative and have good generalisation quality for classification. Almost all the features presented in this thesis are rotation, translation and scale invariant so that they are expected to perform well in discriminating objects or cells by any classification algorithm. There are some new features added which are believed to improve the classification result. The feature set is considerably broad rather than in contrast with the restricted sets which have been used in previous work. These features are used based on a common interface so that the library can be extended and integrated into other applications. These features are fed into a machine learning algorithm called Linear Discriminant Analysis (LDA) for classification. Cells are then classified as ‘Cells attached to the substrate’ or Cell Class A and ‘Cells in cleavage’ or Cell Class B. LDA considers features by leaving and adding shape features for increased performance. On average there is higher than ninety five percent accuracy obtained in the classification result which is validated by visual classification.</p>
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Muhammad, Ashfaq. "Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23.

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<p>Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and</p><p>management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.</p>
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Shikhagaie, Medya. "Subfamily classification of the Defensin gene superfamily." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-885.

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<p>Defensins are small cysteine-rich, cationic peptides that play an essential role in the innate immune system of virtually all life forms, from insects and plants to amphibians and mammals. Defensins are mainly an innate immunity element, exhibiting antibacterial activities by disrupting the cell membrane of a wide range of organisms (Cole et al. 2002). Defensins also affect certain adaptive immune responses, including enhancing phagocytosis, promoting neutrophil recruitment, and enhancing the production of proinflammatory cytokines.</p><p>The aim of this thesis is to make a comprehensive and accurate subfamily classification of the defensin gene family, primarily by using a library of Hidden Markov Models (HMMs). In this project the subfamily classification of the defensin gene family is primarily based on a constructed library of HMMs. Results: Sets of known defensins were organized in placed in 84 clusters using the clustering and alignment tool, FlowerPower. The clusters were further classified as mammalian alpha- or beta-defensins, plant defensin, insect defensin and defensin MGD. This classification was based on significant cluster hits against the Structural Classification of Proteins (SCOP) database and species distribution. Based on the relative positions of disulfide bonds and constructed Multiple Sequence Alignments (MSAs) some sequences were classified as belonging to the sperm– and theta-defensin subfamilies. Compared to PFAM’s classification of defensins, the subfamily classification presented here is more informative. The library of HMMs has been made public via a web server that was used to automatically score and analyze input sequences against the created database of HMMs. This database and web server are expected to be useful to researchers working on various aspects of defensin action.</p>
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Lysholm, Fredrik. "Structural characterization of overrepresented." Thesis, Linköping University, The Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12325.

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<p>Background: Through the last decades vast amount of sequence information have been produced by various protein sequencing projects, which enables studies of sequential patterns. One of the bestknown efforts to chart short peptide sequences is the Prosite pattern data bank. While sequential patterns like those of Prosite have proved very useful for classifying protein families, functions etc. structural analysis may provide more information and possible crucial clues linked to protein folding. Today PDB, which is the main repository for protein structure, contains more than 50’000 entries which enables structural protein studies.</p><p>Result: Strongly folded pentapeptides, defined as pentapeptides which retained a specific conformation in several significantly structurally different proteins, were studied out of PDB. Among these several groups were found. Possibly the most well defined is the “double Cys” pentapeptide group, with two amino acids in between (CXXCX|XCXXC) which were found to form backbone loops where the two Cysteine amino acids formed a possible Cys-Cys bridge. Other structural motifs were found both in helixes and in sheets like "ECSAM" and "TIKIW", respectively.</p><p>Conclusion: There is much information to be extracted by structural analysis of pentapeptides and other oligopeptides. There is no doubt that some pentapeptides are more likely to obtain a specific fold than others and that there are many strongly folded pentapeptides. By combining the usage of such patterns in a protein folding model, such as the Hydrophobic-polar-model improvements in speed and accuracy can be obtained. Comparing structural conformations for important overrepresented pentapeptides can also help identify and refine both structural information data banks such as SCOP and sequential pattern data banks such as Prosite.</p>
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Garma, L. D. (Leonardo D. ). "Structural bioinformatics tools for the comparison and classification of protein interactions." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526216065.

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Abstract Most proteins carry out their functions through interactions with other molecules. Thus, proteins taking part in similar interactions are likely to carry out related functions. One way to determine whether two proteins do take part in similar interactions is by quantifying the likeness of their structures. This work focuses on the development of methods for the comparison of protein-protein and protein-ligand interactions, as well as their application to structure-based classification schemes. A method based on the MultiMer-align (or MM-align) program was developed and used to compare all known dimeric protein complexes. The results of the comparison demonstrates that the method improves over MM-align in a significant number of cases. The data was employed to classify the complexes, resulting in 1,761 different protein-protein interaction types. Through a statistical model, the number of existing protein-protein interaction types in nature was estimated at around 4,000. The model allowed the establishment of a relationship between the number of quaternary families (sequence-based groups of protein-protein complexes) and quaternary folds (structure-based groups). The interactions between proteins and small organic ligands were studied using sequence-independent methodologies. A new method was introduced to test three similarity metrics. The best of these metrics was subsequently employed, together with five other existing methodologies, to conduct an all-to-all comparison of all the known protein-FAD (Flavin-Adenine Dinucleotide) complexes. The results demonstrates that the new methodology captures the best the similarities between complexes in terms of protein-ligand contacts. Based on the all-to-all comparison, the protein-FAD complexes were subsequently separated into 237 groups. In the majority of cases, the classification divided the complexes according to their annotated function. Using a graph-based description of the FAD-binding sites, each group could be further characterized and uniquely described. The study demonstrates that the newly developed methods are superior to the existing ones. The results indicate that both the known protein-protein and the protein-FAD interactions can be classified into a reduced number of types and that in general terms these classifications are consistent with the proteins' functions<br>Tiivistelmä Suurin osa proteiinien toiminnasta tapahtuu vuorovaikutuksessa muiden molekyylien kanssa. Proteiinit, jotka osallistuvat samanlaisiin vuorovaikutuksiin todennäköisesti toimivat samalla tavalla. Kahden proteiinin todennäköisyys esiintyä samanlaisissa vuorovaikutustilanteissa voidaan määrittää tutkimalla niiden rakenteellista samankaltaisuutta. Tämä väitöskirjatyö käsittelee proteiini-proteiini- ja proteiini-ligandi -vuorovaikutusten vertailuun käytettyjen menetelmien kehitystä, ja niiden soveltamista rakenteeseen perustuvissa luokittelujärjestelmissä. Tunnettuja dimeerisiä proteiinikomplekseja tutkittiin uudella MultiMer-align-ohjelmaan (MM-align) perustuvalla menetelmällä. Vertailun tulokset osoittavat, että uusi menetelmä suoriutui MM-alignia paremmin merkittävässä osassa tapauksista. Tuloksia käytettiin myös kompleksien luokitteluun, jonka tuloksena oli 1761 erilaista proteiinien välistä vuorovaikutustyyppiä. Luonnossa esiintyvien proteiinien välisten vuorovaikutusten määrän arvioitiin tilastollisen mallin avulla olevan noin 4000. Tilastollisen mallin avulla saatiin vertailtua sekä sekvenssin (”quaternary families”) sekä rakenteen (”quaternary folds”) mukaan ryhmiteltyjen proteiinikompleksien määriä. Proteiinien ja pienien orgaanisten ligandien välisiä vuorovaikutuksia tutkittiin sekvenssistä riippumattomilla menetelmillä. Uudella menetelmällä testattiin kolmea eri samankaltaisuutta mittaavaa metriikkaa. Näistä parasta käytettiin viiden muun tunnetun menetelmän kanssa vertailemaan kaikkia tunnettuja proteiini-FAD (Flavin-Adenine-Dinucleotide, flaviiniadeniinidinukleotidi) -komplekseja. Proteiini-ligandikontaktien osalta uusi menetelmä kuvasi kompleksien samankaltaisuutta muita menetelmiä paremmin. Vertailun tuloksia hyödyntäen proteiini-FAD-kompleksit luokiteltiin edelleen 237 ryhmään. Suurimmassa osassa tapauksista luokittelujärjestelmä oli onnistunut jakamaan kompleksit ryhmiin niiden toiminnallisuuden mukaisesti. Ryhmät voitiin määritellä yksikäsitteisesti kuvaamalla FAD:n sitoutumispaikka graafisesti. Väitöskirjatyö osoittaa, että siinä kehitetyt menetelmät ovat parempia kuin aikaisemmin käytetyt menetelmät. Tulokset osoittavat, että sekä proteiinien väliset että proteiini-FAD -vuorovaikutukset voidaan luokitella rajattuun määrään vuorovaikutustyyppejä ja yleisesti luokittelu on yhtenevä proteiinien toiminnan suhteen
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Podder, Mohua. "Robust genotype classification using dynamic variable selection." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1602.

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Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based on linear models for individual probe signals and using dynamic variable selection at the prediction level. The algorithms combine separate analyses based on the multiple probe sets to give a final confidence score for each candidate genotypes. Our proposed classification model achieved an accuracy level of >99.4% with 100% call rate for the SNP genotype data which is comparable with existing genotyping technologies. We discussed the appropriateness of the proposed model related to other existing high-throughput genotype calling algorithms. In this thesis we have explored three new ideas for classification with high dimensional data: (1) ensembles of various sets of predictors with built-in dynamic property; (2) robust classification at the prediction level; and (3) a proper confidence measure for dealing with failed predictor(s). We found that a mixture model for classification provides robustness against outlying values of the explanatory variables. Furthermore, the algorithm chooses among different sets of explanatory variables in a dynamic way, prediction by prediction. We analyzed several data sets, including real and simulated samples to illustrate these features. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any ‘bad data’ corresponding to image artifacts on the microarray slide or failure of a specific chemistry. Though motivated by this genotyping application, the proposed methodology would apply to other classification problems where the explanatory variables fall naturally into groups or outliers in the explanatory variables require variable selection at the prediction stage for robustness.
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Alomair, Lamya. "Combining Protein Interactions and Functionality Classification in NS3 to Determine Specific Antiviral Targets in Dengue." Thesis, George Mason University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10279841.

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<p> Dengue virus (DENV) is a serious worldwide health concern putting about 2.5 billion people in more than 100 countries at-risk Dengue is a member of the flaviviridae family, is transmitted to human via mosquitos. Dengue is a deadly viral disease. Unfortunately, there are no vaccines or antiviral that can prevent this infection and that is why researchers are diligently working to find cures. The DENV genome codes for multiple nonstructural proteins one of which is the NS3 enzyme that participates in different steps of the viral life cycle including viral replication, viral RNA genome synthesis and host immune mechanism. Recent studies suggest the role of fatty acid biogenesis during DENV infection, including posttranslational protein modification. Phosphorylation is among the protein post translational modifications and plays essential roles in protein folding, interactions, signal transduction, survival and apoptosis. </p><p> In silico study provides a powerful approach to gain a better understanding of the biological systems at the gene level. NS3 has the potential to be phosphorylated by any of the &sim;500 human kinases. We predicted potential kinases that might phosphorylate NS3 and calculated Dena ranking score using neural network and other machine learning based webserver programs. These scores enabled us to investigate and identify the top kinases that phosphorylate DENV NS3. We hypothesize that preventing the phosphorylation of NS3 may interrupt the viral replication and participate in antiviral evasion. Using multiple sequence alignment bioinformatics tools we verified the results of the highly conserved residues and the residues around active sites whose phosphorylation may have a potential effect on viral replication. We further verified the results with multiple bioinformatics tools. Moreover, we included the Zika virus in our research and analysis taking into consideration the facts that Zika is related to the dengue virus because it belongs to the same Flavivirus genus affecting humans which might lead to a lot of similarities between Zika and Dengue, and that Zika is available for <i>in vitro</i> testing. </p><p> Our studies propose that the Host-Mediated Phosphorylation of NS3 would affect its capability to interact with NS5 and knocking out one of the interacting proteins may inhibit viral replication. These results will open new doors for further investigation and future work is expected to help identify the key inhibition mechanisms.</p><p>
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Wang, Chen. "Novel software tool for microsatellite instability classification and landscape of microsatellite instability in osteosarcoma." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1554829925088174.

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Larsson, Anders. "Systematics of Woodsia : Ferns, bioinformatics and more." Doctoral thesis, Uppsala universitet, Systematisk biologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-232233.

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Ferns are one of the three main clades of vascular plants. They have few easily studied morphological characters, reflected in a historically unstable classification. The fern genus Woodsia is known to have a complex evolutionary history including numerous polyploid taxa and hybrids. It is a cosmopolitan group of small rock loving ferns mainly found in montane areas. This thesis aims at analyzing the patterns of diploid and polyploid evolution in Woodsia and to resolve and classify the relationships of Woodsiaceae and the other families in the large fern clade Eupolypods II. The Eupolypods II family relationships were inferred with DNA sequences from 81 specimens representing all major lineages. This resulted in the first well supported phylogeny of this clade and revealed Woodsiaceae to be non-monophyletic. The genera previously placed in this family were reclassified into five new or resurrected families. Swedish fern genera that have changed family classification are Woodsia (hällebräknar), now in the monogeneric family Woodsiaceae, Athyrium (majbräknar), now  in Athyriaceeae and Cystopteris (stenbräknar) and Gymnocarpium (ekbräknar) now in Cystopteridaceae. To analyze the evolution of Woodsia, phylogenies were produced from five plastid and two nuclear regions sequenced from 188 specimens. The results show that most taxa in Woodsia are polyploid. Polyploidization is the most common mode of speciation in the genus with an estimated polyploid speciation rate of 54%. The polyploids are mostly young and many of the polyploid taxa seem to have formed multiple times. The results also address several taxonomic and biogeographic questions. In the process of the work we made methodological advancements and developed 20 new low copy nuclear marker regions as well as a software pipeline for finding primers in transcriptome datasets. The alignment editor software AliView was developed for handling the increasing size datasets in a user friendly way. In conclusion this thesis provides new insights into the complexities of the evolution of a fern genus in which much of the diversity is accommodated in young species formed through polyploidization. It provides a framework of phylogenetic relationships at different levels that both answers long standing questions and generates new ones.
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Books on the topic "Bioinformatics classification"

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Okun, Oleg. Feature selection and ensemble methods for bioinformatics: Algorithmic classification and implementations. Medical Information Science Reference, 2011.

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1954-, Heiner Monika, and SpringerLink (Online service), eds. Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Jagota, Arun K. Data Analysis and Classification for Bioinformatics. Bioinformatics By the Bay, 2000.

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Classification Analysis of DNA Microarrays Wiley Series in Bioinformatics. IEEE Computer Society Press, 2013.

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Yan, Qing. Translational Bioinformatics and Systems Biology Methods for Personalized Medicine. Elsevier Science & Technology Books, 2017.

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Cooperation in Classification and Data Analysis Studies in Classification Data Analysis and Knowledge Orga. Springer, 2009.

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Bandyopadhyay, Sanghamitra, and Sriparna Saha. Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches, and Applications. Springer, 2012.

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Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series). Springer, 2007.

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Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2006.

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Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2007.

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Book chapters on the topic "Bioinformatics classification"

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Sidhu, Amandeep S., Matthew I. Bellgard, and Tharam S. Dillon. "Classification of Information About Proteins." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_12.

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Marsden, Russell L., and Christine A. Orengo. "The Classification of Protein Domains." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_5.

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Xu, Ying, Juan Cui, and David Puett. "Cancer Classification and Molecular Signature Identification." In Cancer Bioinformatics. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1381-7_3.

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Shi, Yi, Zhipeng Cai, and Guohui Lin. "Classification Accuracy Based Microarray Missing Value Imputation." In Bioinformatics Algorithms. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470253441.ch14.

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Ahn, Hongshik, and Hojin Moon. "Classification: Supervised Learning with High-Dimensional Biological Data." In Statistical Bioinformatics. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470567647.ch6.

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Tsuda, Koji. "Graph Classification Methods in Chemoinformatics." In Handbook of Statistical Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16345-6_16.

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Harris, Keith, Lisa McMillan, and Mark Girolami. "Inferring Meta-covariates in Classification." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04031-3_14.

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Mock, Florian, and Manja Marz. "Sequence Classification with Machine Learning at the Example of Viral Host Prediction." In Virus Bioinformatics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003097679-10.

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Saidi, Rabie, Mondher Maddouri, and Engelbert Mephu Nguifo. "Biological Sequences Encoding for Supervised Classification." In Bioinformatics Research and Development. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71233-6_18.

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Busa-Fekete, Róbert, András Kocsor, and Sándor Pongor. "Tree-Based Algorithms for Protein Classification." In Computational Intelligence in Bioinformatics. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76803-6_6.

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Conference papers on the topic "Bioinformatics classification"

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Menager, Herve, Zoe Lacroix, and Pierre Tuffery. "Bioinformatics Services Discovery Using Ontology Classification." In 2007 IEEE Congress on Services (Services 2007). IEEE, 2007. http://dx.doi.org/10.1109/services.2007.20.

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Fauzi bin Othman, Mohd, and Thomas Moh Shan Yau. "Neuro Fuzzy Classification and Detection Technique for Bioinformatics Problems." In First Asia International Conference on Modelling & Simulation (AMS'07). IEEE, 2007. http://dx.doi.org/10.1109/ams.2007.70.

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Helmy, Tarek, and Zeehasham Rasheed. "Multi-category bioinformatics dataset classification using extreme learning machine." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983354.

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Boczko, Erik M., Andrew Di Lullo, and Todd R. Young. "Binary Classification Based on Potentials." In 2009 Ohio Collaborative Conference on Bioinformatics (OCCBIO). IEEE, 2009. http://dx.doi.org/10.1109/occbio.2009.31.

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Guixian Xu, Zhendong Niu, P. Uetz, Xu Gao, and Hongfang Liu. "Comparison of classification methods on protein-protein interaction document classification." In 2008 IEEE International Conference on Bioinformatics and Biomedcine Workshops. IEEE, 2008. http://dx.doi.org/10.1109/bibmw.2008.4686213.

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"PROGNOSIS OF BREAST CANCER BASED ON A FUZZY CLASSIFICATION METHOD." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002716601230130.

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"THE PLASMODIUM GLUTATHIONE S-TRANSFERASE - Bioinformatics Characterization and Classification into the Sigma Class." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002747401730180.

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Kunik, V., Z. Solan, S. Edelman, E. Ruppin, and D. Horn. "Motif extraction and protein classification." In 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05). IEEE, 2005. http://dx.doi.org/10.1109/csb.2005.39.

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SMALTER, AARON M., J. HUAN, and GERALD H. LUSHINGTON. "CHEMICAL COMPOUND CLASSIFICATION WITH AUTOMATICALLY MINED STRUCTURE PATTERNS." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0007.

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Verma, Aayushi, and Shikha Mehta. "A comparative study of ensemble learning methods for classification in bioinformatics." In 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence (Confluence). IEEE, 2017. http://dx.doi.org/10.1109/confluence.2017.7943141.

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Reports on the topic "Bioinformatics classification"

1

Kamalakaran, Sitharthan, and Josh Dubnau. A Strategy to Rapidly Re-Sequence the NF1 Genomic Loci Using Microarrays and Bioinformatics for Molecular Classification of the Disease. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada478099.

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