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BMC Bioinformatics|June 7, 2019
Deep convolutional neural networks for mammography: advances, challenges and applicationsDina Abdelhafiz, Clifford Yang, Reda Ammar, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|September 18, 2018
Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number VariationsFatima Zare, Sardar Ansari, Kayvan Najarian, et al.
BMC Bioinformatics|January 20, 2019
Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing dataTianyu Wang, Boyang Li, Craig E Nelson, et al.
Bioinformatics Advances|February 16, 2026
SNP-based prediction of schizophrenia using machine learningZamart Ramazanova, Bakhyt Matkarimov, Sheida Nabavi, et al.
BMC Bioinformatics|December 10, 2020
Convolutional neural network for automated mass segmentation in mammographyDina Abdelhafiz, Jinbo Bi, Reda Ammar, et al.
Medical Physics|March 10, 2022
Feature fusion Siamese network for breast cancer detection comparing current and prior mammogramsJun Bai, Annie Jin, Tianyu Wang, et al.
Medical Image Analysis|April 26, 2021
Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A reviewJun Bai, Russell Posner, Tianyu Wang, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|January 26, 2024
Unsupervised feature correlation model to predict breast abnormal variation maps in longitudinal mammogramsJun Bai, Annie Jin, Madison Adams, et al.
BMC Bioinformatics|June 2, 2017
An evaluation of copy number variation detection tools for cancer using whole exome sequencing dataFatima Zare, Michelle Dow, Nicholas Monteleone, et al.
Bioinformatics (Oxford, England)|October 31, 2015
EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classesSheida Nabavi, Daniel Schmolze, Mayinuer Maitituoheti, et al.
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