Produktbild: Data-Driven Reproductive Health

Data-Driven Reproductive Health Role of Bioinformatics and Machine Learning Methods

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.10.2025

Abbildungen

XI, 231 p. 25 illus., 23 illus. in color.

Herausgeber

Abhishek Sengupta + weitere

Verlag

Springer Singapore

Seitenzahl

231

Maße (L/B/H)

23,5/15,5/1,3 cm

Gewicht

421 g

Sprache

Englisch

ISBN

978-981-9774-53-1

Beschreibung

Portrait

Dr. Abhishek Sengupta is working as an assistant professor at the Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida, India. He completed his Ph.D. on the topic 'modelling and analysis of human energy metabolic network,' with a completely annotated model titled HEPNet: Human Energy Pool Network from Amity University, Noida, India. Dr. Sengupta is associated with a DBT-funded project on “AI in reproductive medicine” at the research lab located at the Amity Institute of Biotechnology, Noida, India. His lab focuses on projects employing multi-omics and machine learning approaches for reproductive health research. Some of the research projects from his lab are on the genital microbiome, endometrium transcriptomics, and viral/fungal infections with an emphasis on investigating, analyzing, and predicting pregnancy-related complications, recurrent implantation failure, IVF outcomes, STIs, and PCOS, as well as identify novel therapeutic targets and drugs. He has published many papers in peer-reviewed national and international journals.

Dr. Priyanka Narad  is currently working as Scientist in Indian Council of Medical Research, New Delhi and previously worked as an assistant professor at the Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida, India. She completed her Ph.D. in network biology and constructed a network of molecular complexities in various phases of human stem cells, with an emphasis on the induction and maintenance of human embryonic stem cells from Amity University, Noida, India. Dr. Narad along with Dr. Sengupta worked on a DBT-funded project on “AI in reproductive medicine” at the research lab located at the Amity Institute of Biotechnology, Noida, India. Some of the research projects from her lab are on the genital microbiome, endometrium transcriptomics, and viral/fungal infections with an emphasis on investigating, analyzing, and predicting pregnancy-related complications, recurrent implantation failure, IVF outcomes, STIs, and PCOS, as well as identify novel therapeutic targets and drugs. She has published many papers in peer-reviewed national and international journals.

Dr. Gaurav Majumdar is the chief embryologist at the Centre of IVF, Sir Ganga Ram Hospital, New Delhi, India. He completed his master's in clinical embryology from the National University of Singapore, Singapore; Ph.D. in pre-implantation genetic screening in human embryos. Dr. Majumdar has published many papers in peer-reviewed national and international journals and several chapters.

Dr. Deepak Modi is a scientist at the ICMR-National Institute for Research in Reproductive Health and leads the molecular and cellular biology group. He completed his M.Sc. Zoology and Ph.D. in applied biology from the University of Mumbai, Mumbai, India. Dr. Modi’s areas of interest include understanding endometrial receptivity and endometrial disorders, genetics of sexual development, male infertility and COVID-19, and reproduction. He has received many awards.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.10.2025

Abbildungen

XI, 231 p. 25 illus., 23 illus. in color.

Herausgeber

Verlag

Springer Singapore

Seitenzahl

231

Maße (L/B/H)

23,5/15,5/1,3 cm

Gewicht

421 g

Sprache

Englisch

ISBN

978-981-9774-53-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Data-Driven Reproductive Health
  • 1 Introduction to Data Mining in Reproductive Health.- 2 Reproductive Health Data Sources.- 3 Pre-processing and Integration of Reproductive Health Data.- 4 Multi-omics Approaches for Reproductive Health Data.- 5 Association Rule Mining in Reproductive Health Data.- 6 Modeling in Reproductive Health and Treatment Outcomes.- 7 Clustering Analysis of Reproductive Health Data.- 8 Text Mining and NLP in Reproductive Health.- 9 Time Series Analysis in Reproductive Health Data.- 10 Data Mining Ethics in Reproductive Health.- 11 Reproductive Health Data Mining: Case Studies.- 12 Future Directions and Emerging Trends in Reproductive Health.