Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Siddesh, G. M.
Manisekhar, S. R.
Srinivasa, K. G.

Hardcover

Series: Algorithms for Intelligent Systems

Technology & EngineeringAnatomy & PhysiologyGeneral Computers

ISBN10: 9811524440
ISBN13: 9789811524448
Publisher: Springer Nature
Published: Jan 31 2020
Pages: 317
Weight: 1.41
Height: 0.75 Width: 6.14 Depth: 9.21
Language: English
Part 1: Bioinformatics.- Chapter 1. Introduction to Bioinformatics.- Chapter 2. Review about Bioinformatics, Databases, Sequence Alignment, Docking and Drug Discovery.- Chapter 3. Machine Learning for Bioinformatics.- Chapter 4. Impact of Machine Learning in Bioinformatics Research.-Chapter 5. Text-mining in Bioinformatics.- Chapter 6. Open Source Software Tools for Bioinformatics.- Part 2: Protein Structure Prediction and Gene Expression Analysis.- Chapter 7. A Study on Protein Structure Prediction.- Chapter 8. Computational Methods Used in Prediction of Protein Structure.- Chapter 9. Computational Methods for Inference of Gene Regulatory Networks from Gene Expression Data.- Chapter 10. Machine Learning Algorithms for Feature Selection from Gene Expression Data.- Part 3: Genomics and Proteomics.- Chapter 11. Unsupervised Techniques in Genomics.- Chapter 12. Supervised Techniques in Proteomics.- Chapter 13. Visualizing Codon Usage Within and Across Genomes: Concepts and Tools.- Chapter 14. Single-Cell Multiomics: Dissecting Cancer.

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