Machine Learning and Systems Biology in Genomics and Health

This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.

Autorentext
Dr. Shailza Singh is Scientist-E and Incharge of Bioinformatics and High Performance Computing Facility, National Centre for Cell Science, Pune, India Her research chiefly focuses on systems and synthetic biology. She also specializes in infectious diseases such as leishmaniasis. Her research group is working to integrate the action of regulatory circuits, cross-talk between pathways, and non-linear kinetics of biochemical processes through mathematical modeling. Dr. Singh has been honored with the DBT-RGYI, DST Young Scientist and INSA Bilateral Exchange Programme awards, and was selected by the DBT for a SAKURA EXCHANGE Programme in Science in the field of Artificial Intelligence and Machine learning to Tokyo in 2018. She serves as a reviewer for prestigious international grants such as the Research Councils UK; for national grants from the DBT, DST and CSIR; and for several prominent international journals, e.g. Parasite and Vectors, PLOS One, BMC Infectious Disease, BMC Research Notes, Oncotarget, and the International Journal of Cancer.

Inhalt

1. Overview of the application of Machine Learning in Genomics

Marjana Novic

2. Feed Forward MultiLayer Perceptron Model for infectious disease diagnosis

Shailza Singh

3. Multilayer Perceptron Model in cancer genetics

Ramrup Sarkar

4. AI for therapeutic Biomarker discovery

Arita Masanori

5. Machine learning for Precision Medicine

Ritesh Kumar

6. Big Data Analysis and its implication in genetic diseases

Shailesh Kumar

7. Bio-computation for Image Recognition

Balamurgan Shanmugam

8. AI in Food Toxicology- A Genomic Revolution

Elena Caro Bernat

9. Random Forest and SVM models in Gut Microbiota

Imatiyaz Hassan

10. Deep learning for Microscopic Images

Wolfgang Parak

11. Decision analytic framework for Drug Discovery: Role of Big Data

Natalja Fjdorova

12. Deep Learning Techniques and Ethical considerations in Health Care industry

Kipp Johnson

13. Identification of Novel RNAs in Plants by Big Data Analysis

14. Role of Genomics in Cancer Therapeutics

Rahul Kumar

15. Peptides screened through Machine Learning

Kumardeep Chaudhary

16. Microbial genomic research and AI

Harinder Singh

17. Galaxy Platform for Next-generation Sequencing Data Analysis

Deepak Singla

18 Machine Learning Epitope based Vaccine Designing

Sandeep Dhanda

19. Delving into microbial genome through Artificial Intelligence: Past, Present and Future

Surendra Vikram

20. Big Data and Biocuration: The Big B's of Biological Sciences

    Saurav Raghuvanshi