Cargando…

Data Analytics in Bioinformatics A Machine Learning Perspective.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Satpathy, Rabinarayan
Otros Autores: Choudhury, Tanupriya, Satpathy, Suneeta, Mohanty, Sachi Nandan, Zhang, Xiaobo
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2021.
Temas:
Acceso en línea:Texto completo

MARC

LEADER 00000cam a2200000Mu 4500
001 EBOOKCENTRAL_on1235595818
003 OCoLC
005 20240329122006.0
006 m o d
007 cr |||||||||||
008 210130s2021 xx o ||| 0 eng d
040 |a EBLCP  |b eng  |c EBLCP  |d YDX  |d OCLCQ  |d OCLCL  |d REDDC  |d OCLCO 
019 |a 1232512310 
020 |a 9781119785613 
020 |a 1119785618 
020 |z 1119785537 
020 |z 9781119785538 
035 |a (OCoLC)1235595818  |z (OCoLC)1232512310 
050 4 |a QH324.2  |b .D383 2021 
082 0 4 |a 570.285  |q OCoLC  |2 23/eng/20231120 
049 |a UAMI 
100 1 |a Satpathy, Rabinarayan. 
245 1 0 |a Data Analytics in Bioinformatics  |h [electronic resource] :  |b A Machine Learning Perspective. 
260 |a Newark :  |b John Wiley & Sons, Incorporated,  |c 2021. 
300 |a 1 online resource (544 p.) 
500 |a Description based upon print version of record. 
505 0 |a Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- Part 1: THE COMMENCEMENT OF MACHINE LEARNING SOLICITATION TO BIOINFORMATICS -- 1 Introduction to Supervised Learning -- 1.1 Introduction -- 1.2 Learning Process & its Methodologies -- 1.2.1 Supervised Learning -- 1.2.2 Unsupervised Learning -- 1.2.3 Reinforcement Learning -- 1.3 Classification and its Types -- 1.4 Regression -- 1.4.1 Logistic Regression -- 1.4.2 Difference between Linear & Logistic Regression -- 1.5 Random Forest -- 1.6 K-Nearest Neighbor -- 1.7 Decision Trees 
505 8 |a 1.8 Support Vector Machines -- 1.9 Neural Networks -- 1.10 Comparison of Numerical Interpretation -- 1.11 Conclusion & Future Scope -- References -- 2 Introduction to Unsupervised Learning in Bioinformatics -- 2.1 Introduction -- 2.2 Clustering in Unsupervised Learning -- 2.3 Clustering in Bioinformatics-Genetic Data -- 2.3.1 Microarray Analysis -- 2.3.2 Clustering Algorithms -- 2.3.3 Partition Algorithms -- 2.3.4 Hierarchical Clustering Algorithms -- 2.3.5 Density-Based Approach -- 2.3.6 Model-Based Approach -- 2.3.7 Grid-Based Clustering -- 2.3.8 Soft Clustering -- 2.4 Conclusion -- References 
505 8 |a 3 A Critical Review on the Application of Artificial Neural Network in Bioinformatics -- 3.1 Introduction -- 3.1.1 Different Areas of Application of Bioinformatics -- 3.1.2 Bioinformatics in Real World -- 3.1.3 Issues with Bioinformatics -- 3.2 Biological Datasets -- 3.3 Building Computational Model -- 3.3.1 Data Pre-Processing and its Necessity -- 3.3.2 Biological Data Classification -- 3.3.3 ML in Bioinformatics -- 3.3.4 Introduction to ANN -- 3.3.5 Application of ANN in Bioinformatics -- 3.3.6 Broadly Used Supervised Machine Learning Techniques -- 3.4 Literature Review 
505 8 |a 3.4.1 Comparative Analysis of ANN With Broadly Used Traditional ML Algorithms -- 3.5 Critical Analysis -- 3.6 Conclusion -- References -- Part 2: MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION -- 4 Dimensionality Reduction Techniques: Principles, Benefits, and Limitations -- 4.1 Introduction -- 4.2 The Benefits and Limitations of Dimension Reduction Methods -- 4.3 Components of Dimension Reduction -- 4.3.1 Feature Selection -- 4.3.2 Feature Reduction -- 4.4 Methods of Dimensionality Reduction -- 4.4.1 Principal Component Analysis (PCA) 
505 8 |a 4.4.2 Missing Values Ratio (MVR) -- 4.4.3 Linear Discriminant Analysis (LDA) -- 4.4.4 Backward Feature Elimination (BFE) -- 4.4.5 Forward Feature Construction (FFC) -- 4.4.6 Independent Component Analysis (ICA) -- 4.4.7 Low Variance Filter (LVF) -- 4.4.8 High Correlation Filter -- 4.4.9 Random Forests (RF)/Ensemble Trees -- 4.4.10 t-Distributed Stochastic Neighbor Embedding (t-SNE) -- 4.4.11 Autoencoder -- 4.4.12 Factor Analysis (FA) -- 4.4.13 Uniform Manifold Approximation and Projection (UMAP) -- 4.4.14 Information Gain (IG) -- 4.4.15 Vector Quantization (VQ) -- 4.5 Conclusion -- References 
500 |a 5 Plant Disease Detection Using Machine Learning Tools With an Overview on Dimensionality Reduction. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Bioinformatics. 
650 6 |a Bio-informatique. 
700 1 |a Choudhury, Tanupriya. 
700 1 |a Satpathy, Suneeta. 
700 1 |a Mohanty, Sachi Nandan. 
700 1 |a Zhang, Xiaobo. 
758 |i has work:  |a Data analytics in bioinformatics (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFtbVgWdCfxDdPFTkPqpmq  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Satpathy, Rabinarayan  |t Data Analytics in Bioinformatics  |d Newark : John Wiley & Sons, Incorporated,c2021  |z 9781119785538 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6461960  |z Texto completo 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6461960 
938 |a YBP Library Services  |b YANK  |n 301896350 
938 |a YBP Library Services  |b YANK  |n 17228831 
994 |a 92  |b IZTAP