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Rachel L Welicky - One of the best experts on this subject based on the ideXlab platform.
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redescription and molecular characterisation of the fish ectoparasite anilocra capensis leach 1818 isopoda cymothoidae with description of six new species of anilocra leach 1818 from africa
Parasites & Vectors, 2019Co-Authors: Rachel L Welicky, Nico J SmitAbstract:Anilocra capensis Leach, 1818 is the only named species of Anilocra Leach, 1818 from South Africa. Anilocra is a large genus (> 40 species) with high levels of diversity reported from the Caribbean and Indo-West Pacific. Considering it is highly unlikely that all records of Anilocra from South Africa can be of a single species, the aim of this study was to better understand the diversity of Anilocra from this region and continent. To redescribe A. capensis, the syntypes of A. capensis and specimens recorded as A. capensis from Africa were borrowed from the Natural History Museum, London, UK, and The iZiko South African Museum, Cape Town. Newly collected fresh samples of A. capensis were collected from off Cape Town, South Africa. Morphological Redescriptions of the syntypes, and other museum and fresh material were conducted. Fresh samples were used to characterise molecularly A. capensis using the mitochondrial cytochrome c oxidase subunit 1 gene (cox1). Morphological analyses demonstrated that apart from A. capensis there are six Anilocra species new to science from Africa: Anilocra ianhudsoni n. sp., Anilocra bunkleywilliamsae n. sp., Anilocra paulsikkeli n. sp., Anilocra jovanasi n. sp., Anilocra angeladaviesae n. sp. and Anilocra hadfieldae n. sp. Of the species under study, specimens of A. capensis appear to demonstrate the most individual variation, which occurs in pleonite width, pleotelson form and uropod length. We determined that African species of Anilocra can be primarily differentiated by the proportional shape and size of the full body in dorsal view and pereonites 1, 6 and 7. Other defining morphological traits include proportional shape and size of the pereopods, and the antenna and antennula peduncles. Lastly, the molecular characterisation of A. capensis is provided and the interspecific divergence with Mediterranean species is smaller than that with Caribbean species. The results of this study provide a detailed redescription of A. capensis and the first molecular barcode for this organism. Six new species of Anilocra from Africa are described, establishing that the diversity of Anilocra in this region is greater than previously known. With this new understanding of species differences, we can accurately conduct detailed molecular and ecological analyses of Anilocra from Africa with certainty of the organism under study.
Nico J Smit - One of the best experts on this subject based on the ideXlab platform.
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redescription and molecular characterisation of the fish ectoparasite anilocra capensis leach 1818 isopoda cymothoidae with description of six new species of anilocra leach 1818 from africa
Parasites & Vectors, 2019Co-Authors: Rachel L Welicky, Nico J SmitAbstract:Anilocra capensis Leach, 1818 is the only named species of Anilocra Leach, 1818 from South Africa. Anilocra is a large genus (> 40 species) with high levels of diversity reported from the Caribbean and Indo-West Pacific. Considering it is highly unlikely that all records of Anilocra from South Africa can be of a single species, the aim of this study was to better understand the diversity of Anilocra from this region and continent. To redescribe A. capensis, the syntypes of A. capensis and specimens recorded as A. capensis from Africa were borrowed from the Natural History Museum, London, UK, and The iZiko South African Museum, Cape Town. Newly collected fresh samples of A. capensis were collected from off Cape Town, South Africa. Morphological Redescriptions of the syntypes, and other museum and fresh material were conducted. Fresh samples were used to characterise molecularly A. capensis using the mitochondrial cytochrome c oxidase subunit 1 gene (cox1). Morphological analyses demonstrated that apart from A. capensis there are six Anilocra species new to science from Africa: Anilocra ianhudsoni n. sp., Anilocra bunkleywilliamsae n. sp., Anilocra paulsikkeli n. sp., Anilocra jovanasi n. sp., Anilocra angeladaviesae n. sp. and Anilocra hadfieldae n. sp. Of the species under study, specimens of A. capensis appear to demonstrate the most individual variation, which occurs in pleonite width, pleotelson form and uropod length. We determined that African species of Anilocra can be primarily differentiated by the proportional shape and size of the full body in dorsal view and pereonites 1, 6 and 7. Other defining morphological traits include proportional shape and size of the pereopods, and the antenna and antennula peduncles. Lastly, the molecular characterisation of A. capensis is provided and the interspecific divergence with Mediterranean species is smaller than that with Caribbean species. The results of this study provide a detailed redescription of A. capensis and the first molecular barcode for this organism. Six new species of Anilocra from Africa are described, establishing that the diversity of Anilocra in this region is greater than previously known. With this new understanding of species differences, we can accurately conduct detailed molecular and ecological analyses of Anilocra from Africa with certainty of the organism under study.
Šmuc Tomislav - One of the best experts on this subject based on the ideXlab platform.
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Approaches for Multi-View Redescription Mining
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Mihelčić Matej, Šmuc TomislavAbstract:The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain Redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate Redescriptions. Multi-view Redescriptions are built using incremental view-extending heuristic from initially created two-view Redescriptions. In this work, we use different types of Predictive Clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general
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Multi-view redescription mining using tree-based multi-target prediction models
2020Co-Authors: Mihelčić Matej, Džeroski Sašo, Šmuc TomislavAbstract:The task of redescription mining is concerned with re-describing different subsets of entities contained in a dataset and revealing non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain Redescriptions and allow for the exploration and analysis of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework includes: a) the use of random forest of Predictive Clustering trees, with and without random output selection, and random forests of Extra Predictive Clustering trees, b) using Extra Predictive Clustering trees as a main rule generation mechanism in the framework and c) using random view subset projections. We provide multiple performance analyses of the proposed framework and demonstrate its usefulness in increasing the understanding of different machine learning models, which has become a topic of growing importance in machine learning and especially in the field of computer science called explainable data science
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Approaches For Multi-View Redescription Mining
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Mihelčić Matej, Šmuc TomislavAbstract:The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain Redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate Redescriptions. Multi-view Redescriptions are built using incremental view-extending heuristic from initially created two-view Redescriptions. In this work, we use different types of Predictive Clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
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Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients
'Public Library of Science (PLoS)', 2017Co-Authors: Mihelčić Matej, Šimić Goran, Leko, Mirjana Babić, Lavrač Nada, Džeroski Sašo, Šmuc TomislavAbstract:We used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p
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Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients
'Public Library of Science (PLoS)', 2017Co-Authors: Mihelčić Matej, Šimić Goran, Lavrač Nada, Džeroski Sašo, Babić Leko Mirjana, Šmuc TomislavAbstract:Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly
Mihelčić Matej - One of the best experts on this subject based on the ideXlab platform.
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Approaches for Multi-View Redescription Mining
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Mihelčić Matej, Šmuc TomislavAbstract:The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain Redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate Redescriptions. Multi-view Redescriptions are built using incremental view-extending heuristic from initially created two-view Redescriptions. In this work, we use different types of Predictive Clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general
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Multi-view redescription mining using tree-based multi-target prediction models
2020Co-Authors: Mihelčić Matej, Džeroski Sašo, Šmuc TomislavAbstract:The task of redescription mining is concerned with re-describing different subsets of entities contained in a dataset and revealing non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain Redescriptions and allow for the exploration and analysis of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework includes: a) the use of random forest of Predictive Clustering trees, with and without random output selection, and random forests of Extra Predictive Clustering trees, b) using Extra Predictive Clustering trees as a main rule generation mechanism in the framework and c) using random view subset projections. We provide multiple performance analyses of the proposed framework and demonstrate its usefulness in increasing the understanding of different machine learning models, which has become a topic of growing importance in machine learning and especially in the field of computer science called explainable data science
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Approaches For Multi-View Redescription Mining
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Mihelčić Matej, Šmuc TomislavAbstract:The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a number of approaches that obtain Redescriptions and allow for the exploration and analyses of attribute associations. The main limitation of existing approaches to this task is their inability to use more than two views. Our work alleviates this drawback. We present a memory efficient, extensible multi-view redescription mining framework that can be used to relate multiple, i.e. more than two views, disjoint sets of attributes describing one set of entities. The framework can use any multi-target regression or multi-label classification algorithm, with models that can be represented as sets of rules, to generate Redescriptions. Multi-view Redescriptions are built using incremental view-extending heuristic from initially created two-view Redescriptions. In this work, we use different types of Predictive Clustering trees algorithms (regular, extra, with random output selection) and the Random Forest thereof in order to improve the quality of final redescription sets and/or execution time needed to generate them. We provide multiple performance analyses of the proposed framework and compare it against the naive approach to multi-view redescription mining. We demonstrate the usefulness of the proposed multi-view extension on several datasets, including a use-case on understanding of machine learning models - a topic of growing importance in machine learning and artificial intelligence in general.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
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Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients
'Public Library of Science (PLoS)', 2017Co-Authors: Mihelčić Matej, Šimić Goran, Leko, Mirjana Babić, Lavrač Nada, Džeroski Sašo, Šmuc TomislavAbstract:We used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p
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Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients
'Public Library of Science (PLoS)', 2017Co-Authors: Mihelčić Matej, Šimić Goran, Lavrač Nada, Džeroski Sašo, Babić Leko Mirjana, Šmuc TomislavAbstract:Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly
Smit, Nico J. - One of the best experts on this subject based on the ideXlab platform.
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Redescription and molecular characterisation of the fish ectoparasite Anilocra capensis Leach, 1818 (Isopoda: Cymothoidae), with description of six new species of Anilocra Leach, 1818 from Africa
'Springer Science and Business Media LLC', 2019Co-Authors: Welicky, Rachel L., Smit, Nico J.Abstract:Background: Anilocra capensis Leach, 1818 is the only named species of Anilocra Leach, 1818 from South Africa. Anilocra is a large genus (>40 species) with high levels of diversity reported from the Caribbean and Indo-West Pacifc. Considering it is highly unlikely that all records of Anilocra from South Africa can be of a single species, the aim of this study was to better understand the diversity of Anilocra from this region and continent. Methods: To redescribe A. capensis, the syntypes of A. capensis and specimens recorded as A. capensis from Africa were borrowed from the Natural History Museum, London, UK, and The iZiko South African Museum, Cape Town. Newly collected fresh samples of A. capensis were collected from of Cape Town, South Africa. Morphological Redescriptions of the syntypes, and other museum and fresh material were conducted. Fresh samples were used to characterise molecularly A. capensis using the mitochondrial cytochrome c oxidase subunit 1 gene (cox1). Results: Morphological analyses demonstrated that apart from A. capensis there are six Anilocra species new to science from Africa: Anilocra ianhudsoni n. sp., Anilocra bunkleywilliamsae n. sp., Anilocra paulsikkeli n. sp., Anilocra jovanasi n. sp., Anilocra angeladaviesae n. sp. and Anilocra hadfeldae n. sp. Of the species under study, specimens of A. capensis appear to demonstrate the most individual variation, which occurs in pleonite width, pleotelson form and uropod length. We determined that African species of Anilocra can be primarily diferentiated by the proportional shape and size of the full body in dorsal view and pereonites 1, 6 and 7. Other defning morphological traits include proportional shape and size of the pereopods, and the antenna and antennula peduncles. Lastly, the molecular characterisation of A. capensis is provided and the interspecifc divergence with Mediterranean species is smaller than that with Caribbean species. Conclusions: The results of this study provide a detailed redescription of A. capensis and the frst molecular barcode for this organism. Six new species of Anilocra from Africa are described, establishing that the diversity of Anilocra in this region is greater than previously known. With this new understanding of species diferences, we can accurately conduct detailed molecular and ecological analyses of Anilocra from Africa with certainty of the organism under stud
