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Sheida Nabavi
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2020 – today
- 2024
- [j14]Samson Weiner
, Bingjun Li, Sheida Nabavi
Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing. Bioinform. 40(8) (2024) - [j13]Bingjun Li, Sheida Nabavi:
A multimodal graph neural network framework for cancer molecular subtype classification. BMC Bioinform. 25(1): 27 (2024) - [j12]Mohammad Mahdi Behzadi, Mohammad Madani, Hanzhang Wang, Jun Bai, Ankit Bhardwaj, Anna Tarakanova, Harold Yamase, Ga Hie Nam, Sheida Nabavi
Weakly-supervised deep learning model for prostate cancer diagnosis and Gleason grading of histopathology images. Biomed. Signal Process. Control. 95: 106351 (2024) - [j11]Jun Bai, Annie Jin, Madison Adams, Clifford Yang, Sheida Nabavi
Unsupervised feature correlation model to predict breast abnormal variation maps in longitudinal mammograms. Comput. Medical Imaging Graph. 113: 102341 (2024) - 2023
- [c26]Sahand Hamzehei
3D Biological/Biomedical Image Registration with enhanced Feature Extraction and Outlier Detection. BCB 2023: 1:1-1:10 - [c25]Bingjun Li
Contrastive Learning in Single-cell Multiomics Clustering. BCB 2023: 65:1 - [c24]Bingjun Li, Sheida Nabavi:
scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model. BIBM 2023: 2075-2080 - [c23]Ya-sine Agrignan, Shanglin Zhou, Jun Bai, Sahidul Islam
A Deep Learning Approach for Ventricular Arrhythmias Classification using Microcontroller. ISQED 2023: 1-5 - [c22]Masum Shah Junayed
A Scaled Denoising Attention-Based Transformer for Breast Cancer Detection and Classification. MLMI@MICCAI (2) 2023: 346-356 - [i3]Bingjun Li
A Multimodal Graph Neural Network Framework for Cancer Molecular Subtype Classification. CoRR abs/2302.12838 (2023) - [i2]Jun Bai, Annie Jin, Madison Adams, Clifford Yang, Sheida Nabavi:
Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammograms. CoRR abs/2312.16772 (2023) - 2022
- [c21]Jun Bai, Annie Jin, Andre Jin, Tianyu Wang, Clifford Yang, Sheida Nabavi:
Applying graph convolution neural network in digital breast tomosynthesis for cancer classification. BCB 2022: 37:1-37:10 - [c20]Jun Bai, Bingjun Li
Semi-supervised classification of disease prognosis using CR images with clinical data structured graph. BCB 2022: 44:1-44:9 - [i1]Mohammad Mahdi Behzadi, Mohammad Madani, Hanzhang Wang, Jun Bai, Ankit Bhardwaj, Anna Tarakanova, Harold Yamase, Ga Hie Nam, Sheida Nabavi:
Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images. CoRR abs/2212.12844 (2022) - 2021
- [j10]Tianyu Wang, Jun Bai, Sheida Nabavi
Single-cell classification using graph convolutional networks. BMC Bioinform. 22(1): 364 (2021) - [j9]Jun Bai, Russell Posner, Tianyu Wang
Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review. Medical Image Anal. 71: 102049 (2021) - [c19]Fatima Zare, Jacob Stark, Sheida Nabavi:
Copy number variation detection using single cell sequencing data. BCB 2021: 26:1-26:6 - [c18]Bingjun Li
Cancer molecular subtype classification by graph convolutional networks on multi-omics data. BCB 2021: 50:1-50:9 - [c17]Tianyu Wang, Bingjun Li
Single-cell RNA sequencing data clustering using graph convolutional networks. BIBM 2021: 2163-2170 - 2020
- [j8]Dina Abdelhafiz
Convolutional neural network for automated mass segmentation in mammography. BMC Bioinform. 21-S(1): 192 (2020) - [j7]Fatima Zare
Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations. IEEE ACM Trans. Comput. Biol. Bioinform. 17(3): 868-876 (2020) - [c16]Fatima Zare, Javad Noorbakhsh, Tianyu Wang, Jeffrey H. Chuang, Sheida Nabavi:
Integrative Deep Learning for PanCancer Molecular Subtype Classification Using Histopathological Images and RNAseq Data. BCB 2020: 8:1-8:8
2010 – 2019
- 2019
- [j6]Tianyu Wang, Boyang Li, Craig E. Nelson, Sheida Nabavi
Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinform. 20(1): 40:1-40:16 (2019) - [j5]Dina Abdelhafiz
Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform. 20-S(11): 281:1-281:20 (2019) - [c15]Fatima Zare, Sheida Nabavi:
Copy Number Variation Detection Using Total Variation. BCB 2019: 423-428 - [c14]Dina Abdelhafiz
Residual Deep Learning System for Mass Segmentation and Classification in Mammography. BCB 2019: 475-484 - [c13]Tianyu Wang, Sheida Nabavi:
Single-cell RNAseq Imputation Based on Matrix Completion with Side Information. BIBM 2019: 2763-2770 - 2018
- [j4]Fatima Zare, Abdelrahman Hosny, Sheida Nabavi:
Noise cancellation using total variation for copy number variation detection. BMC Bioinform. 19-S(11): 361:1-361:12 (2018) - [c12]Tianyu Wang, Sheida Nabavi:
Single-cell Clustering Based on Word Embedding and Nonparametric Methods. BCB 2018: 130-138 - [c11]Nurislam Tursynbek, Ghazal Ghahramany, Sheida Nabavi, Amin Zollanvari
Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas. BIBM 2018: 2423-2428 - [c10]Fatima Zare, Sardar Ansari, Kayvan Najarian, Sheida Nabavi:
Copy number variation detection using partial alignment information. BIBM 2018: 2435-2441 - [c9]Dina Abdelhafiz, Sheida Nabavi, Reda Ammar, Clifford Yang, Jinbo Bi:
Convolutional Neural Network for Automated Mass Segmentation in Mammography. ICCABS 2018: 1 - 2017
- [j3]Fatima Zare, Michelle Dow, Nicholas Monteleone, Abdelrahman Hosny, Sheida Nabavi:
An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinform. 18(1): 286:1-286:13 (2017) - [c8]Abdelrahman Hosny, Fatima Zare, Sheida Nabavi:
Varsimlab: A Docker-based Pipeline to Automatically Synthesize Short Reads with Genomic Aberrations. BCB 2017: 581 - [c7]Fatima Zare, Sardar Ansari, Kayvan Najarian, Sheida Nabavi:
Bias and Noise Cancellation for Robust Copy Number Variation Detection. BCB 2017: 591 - [c6]Tianyu Wang, Sheida Nabavi:
Differential gene expression analysis in single-cell RNA sequencing data. BIBM 2017: 202-207 - [c5]Fatima Zare, Sardar Ansari, Kayvan Najarian, Sheida Nabavi:
Noise cancellation for robust copy number variation detection using next generation sequencing data. BIBM 2017: 230-236 - [c4]Dina Abdelhafiz, Sheida Nabavi, Reda Ammar, Clifford Yang:
Survey on deep convolutional neural networks in mammography. ICCABS 2017: 1 - 2016
- [j2]Sheida Nabavi, Daniel Schmolze
EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes. Bioinform. 32(4): 533-541 (2016) - 2015
- [c3]Sheida Nabavi, Andrew H. Beck:
Earth mover's distance for differential analysis of heterogeneous genomics data. GlobalSIP 2015: 963-966 - 2010
- [j1]Sheida Nabavi, Seungjune Jeon, B. V. K. Vijaya Kumar
An Analytical Approach for Performance Evaluation of Bit-Patterned Media Channels. IEEE J. Sel. Areas Commun. 28(2): 135-142 (2010)
2000 – 2009
- 2008
- [c2]Sheida Nabavi, B. V. K. Vijaya Kumar
Mitigating the Effects of Track Mis-Registration in Bit-Patterned Media. ICC 2008: 2061-2065 - 2007
- [c1]Sheida Nabavi, B. V. K. Vijaya Kumar
Two-Dimensional Generalized Partial Response Equalizer for Bit-Patterned Media. ICC 2007: 6249-6254
Coauthor Index
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last updated on 2024-09-10 01:17 CEST by the dblp team
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