| 000 | 03568nam a22005655i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-32729-2 | ||
| 003 | DE-He213 | ||
| 005 | 20240207153616.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 230809s2023 sz | s |||| 0|eng d | ||
| 020 |
_a9783031327292 _9978-3-031-32729-2 |
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_a570.15195 _223 |
| 245 | 1 | 0 |
_aStatistical Methods at the Forefront of Biomedical Advances _h[electronic resource] / _cedited by Yolanda Larriba. |
| 250 | _a1st ed. 2023. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
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| 300 |
_aXI, 276 p. 71 illus., 57 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 |
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| 500 | _aAcceso multiusuario | ||
| 505 | 0 | _aForecasting of SARS-COV-2 dynamic: beyond the classical SIR model -- Derivation of OED methods for applications in cytogenetic biodosimetry -- Minimum Phi-divergence tests for ophthalmological data grouped in clusters -- Multimodality test for determining which genes distinguish oncological patients -- Exploring the relationship between different cancer sites using disease mapping models -- Advances in flow cytometry gating based on distances and divergences in probability spaces -- Application of quantile regression models for biomedical data -- Hippocampus shape analysis via skeletal models and kernel smoothing -- Methods for analysis of microbiome count data with applications -- The FMM model as a classifier of cardiovascular pathologies -- A novel statistical insight of the circadian markers that govern cardiac rhythms -- Sleep and circadian synchronization assessment from multivariate recordings. . | |
| 520 | _aThis book presents novel statistics methods and reproducible software that helps to solve challenging problems in biomedicine. Specifically, it consists of a collection of 11 chapters contributed by some of the leading experts in the mathematical and statistical field which address new challenges in very disparate biomedical areas, such as genomics, cancer, circadian biology, microbiome, mental disorders, and more. The mathematical rigor is written in a user-friendly way to serve a general biomedical audience ranging from trainees or students to doctors, as well as scientific researchers, university departments, and PhD students. | ||
| 541 |
_fUABC ; _cPerpetuidad |
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| 650 | 0 | _aBiometry. | |
| 650 | 0 | _aBiomedical engineering. | |
| 650 | 0 | _aBioinformatics. | |
| 650 | 0 | _aBiological models. | |
| 650 | 1 | 4 | _aBiostatistics. |
| 650 | 2 | 4 | _aBiomedical Engineering and Bioengineering. |
| 650 | 2 | 4 | _aComputational and Systems Biology. |
| 650 | 2 | 4 | _aBiological Models. |
| 700 | 1 |
_aLarriba, Yolanda. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031327285 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031327308 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031327315 |
| 856 | 4 | 0 |
_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-32729-2 |
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