000 | 03813nam a22005535i 4500 | ||
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001 | 978-3-319-97919-9 | ||
003 | DE-He213 | ||
005 | 20240423125156.0 | ||
007 | cr nn 008mamaa | ||
008 | 181129s2019 sz | s |||| 0|eng d | ||
020 |
_a9783319979199 _9978-3-319-97919-9 |
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024 | 7 |
_a10.1007/978-3-319-97919-9 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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072 | 7 |
_aUYQE _2bicssc |
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_aCOM021030 _2bisacsh |
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_aUNF _2thema |
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_aUYQE _2thema |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aWan, Cen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aHierarchical Feature Selection for Knowledge Discovery _h[electronic resource] : _bApplication of Data Mining to the Biology of Ageing / _cby Cen Wan. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aXIV, 120 p. 52 illus., 23 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aAdvanced Information and Knowledge Processing, _x2197-8441 |
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505 | 0 | _aIntroduction -- Data Mining Tasks and Paradigms -- Feature Selection Paradigms -- Background on Biology of Ageing and Bioinformatics -- Lazy Hierarchical Feature Selection -- Eager Hierarchical Feature Selection -- Comparison of Lazy and Eager Hierarchical Feature Selection Methods and Biological Interpretation on Frequently Selected Gene Ontology Terms Relevant to the Biology of Ageing -- Conclusions and Research Directions. | |
520 | _aThis book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation providesthe resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research. | ||
650 | 0 | _aData mining. | |
650 | 0 | _aBioinformatics. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aComputational and Systems Biology. |
650 | 2 | 4 | _aArtificial Intelligence. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319979182 |
776 | 0 | 8 |
_iPrinted edition: _z9783319979205 |
830 | 0 |
_aAdvanced Information and Knowledge Processing, _x2197-8441 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-97919-9 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c175085 _d175085 |