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008			220301s2022 si | s |||| 0|eng d
020			_a9789811692215
024	7		_a10.1007/978-981-16-9221-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC262 _b2022 EB
245	0	0	_aComputational Intelligence in Oncology : _bApplications in Diagnosis, Prognosis and Therapeutics of Cancers _cedited by Khalid Raza
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XX, 467 páginas) _b94 ilustraciones, 70 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v1016
505	0		_aPart 1: Preliminaries -- Chapter 1. Computational Intelligence in Oncology: Past, Present, and Future -- Chapter 2. Machine Learning-based Models and their Applications in Diagnosis, Prognosis and Effective Cancer Therapeutics: Current State-of-the-art -- Chapter 3. Computational Intelligent Systems in Oncology: A Way towards Translational Healthcare -- Chapter 4. Computational Resources for Oncology Research: A Comprehensive Analysis -- Part 2: Cancer Detection, Diagnosis, Survival and Recurrence Prediction -- Chapter 5. Application of Convolutional Neural Networks in Cancer Diagnosis -- Chapter 6. Automatic Cancer Detection Using Probabilistic Convergence Theory -- Chapter 7. Computational Intelligence Methods for Cancer Survival Prediction -- Chapter 8. Breast Cancer Survival Prediction Using Machine Learning -- Chapter 9. Deep Learning Models for Classification of Brain Tumor with Magnetic Resonance Imaging Images Dataset -- Chapter 10. Predicting the Cancer Recurrence using Artificial Neural Networks -- Chapter 11. Computer Intelligence in Detection of Malignant or Premalignant Oral Lesions: The Story so far -- Chapter 12. Fuzzy Logic-based Hybrid Models for Clinical Decision Support Systems in Cancer -- Part 3: Predicting Cancer Biomarkers, Therapeutic Targets, Drug Response, and Drug Design, Discovery, and Development -- Chapter 13. Predicting Biomarkers and Therapeutic Targets in Cancer -- Chapter 14. Computational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target Prediction -- Chapter 15. Computational Intelligence Based Cheminformatics Model as Cancer Therapeutics.
520			_aThis book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes. According to WHO's recent report, cancer is a leading cause of death worldwide, accounting for over 10 million deaths in the year 2020. Therefore, its early diagnosis, prognosis, and classification to a subtype have become necessary as it facilitates the subsequent clinical management and therapeutics plan. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning, and nature-inspired algorithms, have been widely utilized in various aspects of oncology research, viz. diagnosis, prognosis, therapeutics, and optimized clinical management. Appreciable progress has been made toward the understanding the hallmarks of cancer development, progression, and its effective therapeutics. However, notwithstanding the extrinsic and intrinsic factors which lead to drastic increment in incidence cases, the detection, diagnosis, prognosis, and therapeutics remain an apex challenge for the medical fraternity. With the advent in CI-based approaches, including nature-inspired techniques, and availability of clinical data from various high-throughput experiments, medical consultants, researchers, and oncologists have seen a hope to devise and employ CI in various aspects of oncology. The main aim of the book is to occupy state-of-the-art applications of CI methods which have been derived from core computer sciences to back medical oncology. This edited book covers artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary algorithms, various nature-inspired algorithms, and hybrid intelligent systems which are widely appreciated for the diagnosis, prognosis, and optimization of therapeutics of various cancers. Besides, this book also covers multi-omics exploration, gene expression analysis, gene signature identification of cancers, genomic characterization of tumors, anti-cancer drug design and discovery, drug response prediction by means of CI, and applications of IoT, IoMT, and blockchain technology in cancer research.
988			_aSpringer_Robotics_2022
650		7	_2embne _9139207 _aOncología
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9789811692208
776	0	8	_iPrinted edition: _z9789811692222
776	0	8	_iPrinted edition: _z9789811692239
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi-org.ezproxy.universidadeuropea.es/10.1007/978-981-16-9221-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eu _zSI