Computation in BioInformatics. Группа авторовЧитать онлайн книгу.

      1  Cover

      2  Title Page

      3  Copyright

      4  Preface

      5  1 Bioinfomatics as a Tool in Drug Designing 1.1 Introduction 1.2 Steps Involved in Drug Designing 1.3 Various Softwares Used in the Steps of Drug Designing 1.4 Applications 1.5 Conclusion References

      6  2 New Strategies in Drug Discovery 2.1 Introduction 2.2 Road Toward Advancement 2.3 Methodology 2.4 Role of OMICS Technology 2.5 High-Throughput Screening and Its Tools 2.6 Chemoinformatic 2.7 Concluding Remarks and Future Prospects References

      7  3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective 3.1 Introduction 3.2 Bioinformatics and Drug Discovery 3.3 Bioinformatics Tools in Early Drug Discovery 3.4 Future Directions With Bioinformatics Tool 3.5 Conclusion Acknowledgements References

      8  4 Role of Data Mining in Bioinformatics 4.1 Introduction 4.2 Data Mining Methods/Techniques 4.3 DNA Data Analysis 4.4 RNA Data Analysis 4.5 Protein Data Analysis 4.6 Biomedical Data Analysis 4.7 Conclusion and Future Prospects References

      9  5 In Silico Protein Design and Virtual Screening 5.1 Introduction 5.2 Virtual Screening Process 5.3 Machine Learning and Scoring Functions 5.4 Conclusion and Future Prospects References

      10  6 New Bioinformatics Platform-Based Approach for Drug Design 6.1 Introduction 6.2 Platform-Based Approach and Regulatory Perspective 6.3 Bioinformatics Tools and Computer-Aided Drug Design 6.4 Target Identification 6.5 Target Validation 6.6 Lead Identification and Optimization 6.7 High-Throughput Methods (HTM) 6.8 Conclusion and Future Prospects References

      11  7 Bioinformatics and Its Application Areas 7.1 Introduction 7.2 Review of Bioinformatics 7.3 Bioinformatics Applications in Different Areas 7.4 Conclusion References

      12  8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression 8.1 Introduction 8.2 Data Processing 8.3 Normalization of Microarray Data Using the RMA Method 8.4 Statistical Analysis for Differential Gene Expression 8.5 Conclusion References

      13  9 Machine Learning in Bioinformatics 9.1 Introduction and Background 9.2 Machine Learning Applications in Bioinformatics 9.3 Machine Learning Approaches