Cargando…

Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features

Appropriate ovarian responses to the controlled ovarian stimulation strategy is the premise for a good outcome of the in vitro fertilization cycle. With the booming of artificial intelligence, machine learning is becoming a popular and promising approach for tailoring a controlled ovarian stimulatio...

Descripción completa

Detalles Bibliográficos
Autores principales:	Liu, Liu, Shen, Fujin, Liang, Hua, Yang, Zhe, Yang, Jing, Chen, Jiao
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871024/ https://www.ncbi.nlm.nih.gov/pubmed/35204580 http://dx.doi.org/10.3390/diagnostics12020492

Ejemplares similares

Prediction model of gonadotropin starting dose and its clinical application in controlled ovarian stimulation
por: Hua, Liang, et al.
Publicado: (2022)

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models
por: Mao, Jiashun, et al.
Publicado: (2021)

Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes
por: Xu, Zhe, et al.
Publicado: (2014)

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules
por: Zhang, Kai, et al.
Publicado: (2022)

Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images
por: Martínez-Más, José, et al.
Publicado: (2019)

Using CT radiomic features based on machine learning models to subtype adrenal adenoma
por: Qi, Shouliang, et al.
Publicado: (2023)

Machine learning-based solution reveals cuproptosis features in inflammatory bowel disease
por: Liu, Le, et al.
Publicado: (2023)

The Comprehensive Machine Learning Analytics for Heart Failure
por: Guo, Chao-Yu, et al.
Publicado: (2021)

Center-environment feature models for materials image segmentation based on machine learning
por: Han, Yuexing, et al.
Publicado: (2022)

Quantitative analysis of the MRI features in the differentiation of benign, borderline, and malignant epithelial ovarian tumors
por: Xiao, Fuxia, et al.
Publicado: (2022)

Generative and reproducible benchmarks or comprehensive evaluation machine learning classifiers
por: Orzechowski, Patryk, et al.
Publicado: (2022)

Evaluation of the dependence of radiomic features on the machine learning model
por: Demircioğlu, Aydin
Publicado: (2022)

466 Convolutional Neural Networks and Machine Learning in the Identification of Ultrasonographic Features of Ovarian Morphology
por: Pea, Jeffrey, et al.
Publicado: (2022)

Comprehensive analysis for the immune related biomarkers of platinum-based chemotherapy in ovarian cancer
por: Liu, Jiao, et al.
Publicado: (2023)

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram
por: Xu, Yanting, et al.
Publicado: (2021)

Evaluation of classification in single cell atac-seq data with machine learning methods
por: Guo, Hongzhe, et al.
Publicado: (2022)

Ovarian thecoma-fibroma groups: clinical and sonographic features with pathological comparison
por: Chen, Hui, et al.
Publicado: (2016)

Research on Short-Term Urban Traffic Congestion Based on Fuzzy Comprehensive Evaluation and Machine Learning
por: Mei, Yuan, et al.
Publicado: (2020)

Feature engineering for machine learning and data analytics
por: Dong, Guozhu, et al.
Publicado: (2018)

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning
por: Liu, Jinya, et al.
Publicado: (2022)

Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features
por: Mo, Jia-Jie, et al.
Publicado: (2019)

Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms
por: Wang, Zhen, et al.
Publicado: (2023)

The Comprehensive Analysis of Interferon-Related Prognostic Signature with regard to Immune Features in Ovarian Cancer
por: An, Yuanyuan, et al.
Publicado: (2022)

Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls
por: Maleki, Farhad, et al.
Publicado: (2022)

Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning
por: Xiang, Xuesong, et al.
Publicado: (2023)

Machine Learning to Quantitate Neutrophil NETosis
por: Elsherif, Laila, et al.
Publicado: (2019)

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation
por: Thomson, Hannah, et al.
Publicado: (2022)

Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer
por: Zheng, Qingyuan, et al.
Publicado: (2022)

Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors
por: Jan, Ya-Ting, et al.
Publicado: (2023)

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model
por: Wang, Yan, et al.
Publicado: (2019)

The impact of stage-related features in melanoma recurrence prediction: A machine learning approach
por: Wan, Guihong, et al.
Publicado: (2022)

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion
por: Loch-Olszewska, Hanna, et al.
Publicado: (2020)

Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides
por: Du, Zhenjiao, et al.
Publicado: (2022)

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review
por: Zhang, Xue, et al.
Publicado: (2016)

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation
por: Liu, Hai-Feng, et al.
Publicado: (2023)

Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis
por: Shen, Jing, et al.
Publicado: (2023)

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer
por: Wang, Libo, et al.
Publicado: (2022)

Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features
por: Tokuyama, Naoto, et al.
Publicado: (2021)

Recurrence prediction in clear cell renal cell carcinoma using machine learning of quantitative nuclear features
por: Matsubara, Shuya, et al.
Publicado: (2023)

Machine Learning‐Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening
por: Tanos, Rita, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_a34t28rl8440t5j113rhf1svev