Application of Bioinformatics in Cancers
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2019Beskrivning: 1 electronic resource (418 p.)Innehållstyp: - text
- computer
- online resource
- 9783039217885
- 9783039217892
- Technology, Engineering, Agriculture, Industrial processes
- Biochemical engineering
- Biotechnology
- AID
- APOBEC
- Bioinformatics tool
- Bufadienolide-like chemicals
- Computational Immunology
- DNA
- DNA sequence profile
- GEO DataSets
- HNSCC
- HP
- KRAS mutation
- Monte Carlo
- Neoantigen Prediction
- Network Analysis
- PD-L1
- R package
- RNA
- StAR
- TCGA
- TCGA mining
- The Cancer Genome Atlas
- activation induced deaminase
- alternative splicing
- anti-cancer
- artificial intelligence
- bioinformatics
- biomarker discovery
- biomarker signature
- biomarkers
- biostatistics
- brain
- brain metastases
- breast cancer
- breast cancer detection
- breast cancer prognosis
- cancer
- cancer CRISPR
- cancer biomarker
- cancer biomarkers
- cancer modeling
- cancer prognosis
- cancer treatment
- cancer-related pathways
- cell-free DNA
- chemotherapy
- circulating tumor DNA (ctDNA)
- classification
- clinical
- colorectal cancer
- comorbidity score
- concatenated deep feature
- copy number aberration
- copy number variation
- curation
- curative surgery
- datasets
- decision support systems
- deep learning
- denoising autoencoders
- differential gene expression analysis
- diseases genes
- drug resistance
- environmental factors
- epig
Open Access Unrestricted online access star
This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.
Creative Commons Licence cc by-nc-nd cc https://creativecommons.org/licenses/by-nc-nd/4.0/
eng
Freely available e-book