Atjaunināt sīkdatņu piekrišanu

E-grāmata: Computational Intelligence Methods for Bioinformatics and Biostatistics: 17th International Meeting, CIBB 2021, Virtual Event, November 15-17, 2021, Revised Selected Papers

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
  • Formāts: EPUB+DRM
  • Sērija : Lecture Notes in Bioinformatics 13483
  • Izdošanas datums: 25-Nov-2022
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783031208379
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 65,42 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Computational Intelligence Methods for Bioinformatics and Biostatistics: 17th International Meeting, CIBB 2021, Virtual Event, November 15-17, 2021, Revised Selected PapersPalielināt
  • Formāts: EPUB+DRM
  • Sērija : Lecture Notes in Bioinformatics 13483
  • Izdošanas datums: 25-Nov-2022
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783031208379
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This book constitutes revised selected papers from the 17th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2021, which was held virtually during November 1517, 2021.





The 19 papers included in these proceedings were carefully reviewed and selected from 26 submissions, and they focus on bioinformatics, computational biology, health informatics, cheminformatics, biotechnology, biostatistics, and biomedical imaging.
Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to
Chemical AI.- Development of Bayesian network for multiple sclerosis risk
factor interaction analysis.- Real-Time Automatic Plankton Detection,
Tracking and Classification on Raw Hologram.- The first in-silico model of
leg movement activity during sleep.- Transfer learning and magnetic resonance
imaging techniques for deep neural network-based diagnosis of early cognitive
decline and dementia.- Improving bacterial sRNA identification by combining
genomic context and sequence-derived features.- High-dimensional multi-trait
GWAS by reverse prediction of genotypes using machine learning methods.- A
Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor
Drug Sensitivity.- A Rule-based Approach for Generating Synthetic Biological
Pathways.- Machine Learning Classifiers based on  Dimensionality Reduction
Techniques for the  Early Diagnosis of Alzheimers Disease using Magnetic
Resonance Imaging and Positron Emission Tomography Brain Data.- Text Mining
Enhancements for Image Recognition of Gene Names and Gene
Relations.- Sentence Classification to Detect Tables for Helping Extraction
of Regulatory Interactions in Bacteria.- RF-Isolation: a Novel Representation
of Structural Connectivity Networks for Multiple Sclerosis
Classification.- Summarizing Global SARSCoV2 Geographical Spread by
Phylogenetic Multitype Branching Models.- Explainable AI Models for COVID-19
Diagnosis using CT-Scan Images and Clinical Data.- The need of standardised
metadata to encode causal relationships: Towards safer data-driven machine
learning biological solutions.- Deep Recurrent Neural Networks for the
Generation of Synthetic Coronavirus Spike Protein Sequences.- Recent
Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data.- Soft
brain ageing indicators based on light-weight LeNet-like neural networks and
localized 2D brain age biomarkers.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97830312083796e.html
Keywords: