Predictive Modeling of Recurrent Implantation Failure and Pre-eclampsia Using Machine Learning and Gene Expression Profiling-Reference-Cited by-同舟云学术

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Predictive Modeling of Recurrent Implantation Failure and Pre-eclampsia Using Machine Learning and Gene Expression Profiling

Published:2023-07-06 Issue: Volume: Page:
ISSN:
Container-title:2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
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Author:

Sharma Priyanka¹,Pandey Shruti¹,Ray Sonalika²,Mazumder Mohit²,Gupta Payal¹,Sengupta Abhishek¹,Chaurasia Ankur³,Sengupta Abhishek¹

Affiliation:

1. Amity University,Systems Biology and Data Analytics Lab Amity Institute of Biotechnology,Noida,Uttar Pradesh,India

2. Pine Biotech,North Bethesda,MD,20852

3. Amity University Uttar Pradesh,Amity Institute of Biotechnology,Noida,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10306338/10306339/10307984.pdf?arnumber=10307984

Reference16 articles.

1. A review of omics approaches to study preeclampsia

2. Machine learning vs. classic statistics for the prediction of IVF outcomes

3. Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes

4. Integrative Analysis of Multi-Omics Identified the Prognostic Biomarkers in Acute Myelogenous Leukemia

5. Machine Learning to Predict Pre-Eclampsia and Intrauterine Growth Restriction in Pregnant Women

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