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192 results on '"Nabavi, Sheida"'

1. Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammograms

Author

Bai, Jun, Jin, Annie, Adams, Madison, Yang, Clifford, and Nabavi, Sheida

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early detection and accurate diagnosis of breast abnormalities, which is crucial for improving patient outcomes and reducing the mortality rate of breast cancer. To address the limitations of traditional screening methods, a novel unsupervised feature correlation network was developed to predict maps indicating breast abnormal variations using longitudinal 2D mammograms. The proposed model utilizes the reconstruction process of current year and prior year mammograms to extract tissue from different areas and analyze the differences between them to identify abnormal variations that may indicate the presence of cancer. The model is equipped with a feature correlation module, an attention suppression gate, and a breast abnormality detection module that work together to improve the accuracy of the prediction. The proposed model not only provides breast abnormal variation maps, but also distinguishes between normal and cancer mammograms, making it more advanced compared to the state-of the-art baseline models. The results of the study show that the proposed model outperforms the baseline models in terms of Accuracy, Sensitivity, Specificity, Dice score, and cancer detection rate.

Published
2023

3. A Multimodal Graph Neural Network Framework of Cancer Molecular Subtype Classification

Author

Li, Bingjun and Nabavi, Sheida

Subjects
Quantitative Biology - Genomics, Computer Science - Machine Learning
Abstract

The recent development of high-throughput sequencing creates a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Current multi-omics integrative models mainly use early fusion by concatenation or late fusion based on deep neural networks. Due to the nature of biological systems, graphs are a better representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods lack testing on a more complex cancer classification task. We propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs that combines both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We test the proposed model on TCGA Pan-cancer dataset and TCGA breast cancer dataset for molecular subtype and cancer subtype classification, respectively. The proposed model outperforms four current state-of-the-art baseline models in multiple evaluation metrics. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information., Comment: 18 pages, 4 figure

Published
2023

4. Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images

Author

Behzadi, Mohammad Mahdi, Madani, Mohammad, Wang, Hanzhang, Bai, Jun, Bhardwaj, Ankit, Tarakanova, Anna, Yamase, Harold, Nam, Ga Hie, and Nabavi, Sheida

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. However, most of the algorithms rely on the fully annotated datasets which are expensive to generate. In this work, we proposed a novel weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: (1) extracting discriminative areas in a histopathology image by employing the Multiple Instance Learning (MIL) algorithm based on Transformers, (2) representing the image by constructing a graph using the discriminative patches, and (3) classifying the image into its Gleason grades by developing a Graph Convolutional Neural Network (GCN) based on the gated attention mechanism. We evaluated our algorithm using publicly available datasets, including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross validated the algorithm on an independent dataset. Results show that the proposed model achieved state-of-the-art performance in the Gleason grading task in terms of accuracy, F1 score, and cohen-kappa. The code is available at https://github.com/NabaviLab/Prostate-Cancer.

Published
2022

5. A Scaled Denoising Attention-Based Transformer for Breast Cancer Detection and Classification

Author

Junayed, Masum Shah, Nabavi, Sheida, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
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10. Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing.

Author

Weiner, Samson, Li, Bingjun, and Nabavi, Sheida

Subjects
GAUSSIAN mixture models, ESTIMATION theory, GENE frequency, DNA sequencing, PYTHON programming language
Abstract

Motivation Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline. Results We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses. Availability and implementation SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Data from A Unique Morphological Phenotype in Chemoresistant Triple-Negative Breast Cancer Reveals Metabolic Reprogramming and PLIN4 Expression as a Molecular Vulnerability

Author

Sirois, Isabelle, primary, Aguilar-Mahecha, Adriana, primary, Lafleur, Josiane, primary, Fowler, Emma, primary, Vu, Viet, primary, Scriver, Michelle, primary, Buchanan, Marguerite, primary, Chabot, Catherine, primary, Ramanathan, Aparna, primary, Balachandran, Banujan, primary, Légaré, Stéphanie, primary, Przybytkowski, Ewa, primary, Lan, Cathy, primary, Krzemien, Urszula, primary, Cavallone, Luca, primary, Aleynikova, Olga, primary, Ferrario, Cristiano, primary, Guilbert, Marie-Christine, primary, Benlimame, Naciba, primary, Saad, Amine, primary, Alaoui-Jamali, Moulay, primary, Saragovi, Horace Uri, primary, Josephy, Sylvia, primary, O'Flanagan, Ciara, primary, Hursting, Stephen D., primary, Richard, Vincent R., primary, Zahedi, René P., primary, Borchers, Christoph H., primary, Bareke, Eric, primary, Nabavi, Sheida, primary, Tonellato, Peter, primary, Roy, Josée-Anne, primary, Robidoux, André, primary, Marcus, Elizabeth A., primary, Mihalcioiu, Catalin, primary, Majewski, Jacek, primary, and Basik, Mark, primary

Published
2023
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23. Supplementary Data S18 from A Unique Morphological Phenotype in Chemoresistant Triple-Negative Breast Cancer Reveals Metabolic Reprogramming and PLIN4 Expression as a Molecular Vulnerability

Author

Sirois, Isabelle, primary, Aguilar-Mahecha, Adriana, primary, Lafleur, Josiane, primary, Fowler, Emma, primary, Vu, Viet, primary, Scriver, Michelle, primary, Buchanan, Marguerite, primary, Chabot, Catherine, primary, Ramanathan, Aparna, primary, Balachandran, Banujan, primary, Légaré, Stéphanie, primary, Przybytkowski, Ewa, primary, Lan, Cathy, primary, Krzemien, Urszula, primary, Cavallone, Luca, primary, Aleynikova, Olga, primary, Ferrario, Cristiano, primary, Guilbert, Marie-Christine, primary, Benlimame, Naciba, primary, Saad, Amine, primary, Alaoui-Jamali, Moulay, primary, Saragovi, Horace Uri, primary, Josephy, Sylvia, primary, O'Flanagan, Ciara, primary, Hursting, Stephen D., primary, Richard, Vincent R., primary, Zahedi, René P., primary, Borchers, Christoph H., primary, Bareke, Eric, primary, Nabavi, Sheida, primary, Tonellato, Peter, primary, Roy, Josée-Anne, primary, Robidoux, André, primary, Marcus, Elizabeth A., primary, Mihalcioiu, Catalin, primary, Majewski, Jacek, primary, and Basik, Mark, primary

Published
2023
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24. Supplementary Data - Tables S1-S17 from A Unique Morphological Phenotype in Chemoresistant Triple-Negative Breast Cancer Reveals Metabolic Reprogramming and PLIN4 Expression as a Molecular Vulnerability

Author

Sirois, Isabelle, primary, Aguilar-Mahecha, Adriana, primary, Lafleur, Josiane, primary, Fowler, Emma, primary, Vu, Viet, primary, Scriver, Michelle, primary, Buchanan, Marguerite, primary, Chabot, Catherine, primary, Ramanathan, Aparna, primary, Balachandran, Banujan, primary, Légaré, Stéphanie, primary, Przybytkowski, Ewa, primary, Lan, Cathy, primary, Krzemien, Urszula, primary, Cavallone, Luca, primary, Aleynikova, Olga, primary, Ferrario, Cristiano, primary, Guilbert, Marie-Christine, primary, Benlimame, Naciba, primary, Saad, Amine, primary, Alaoui-Jamali, Moulay, primary, Saragovi, Horace Uri, primary, Josephy, Sylvia, primary, O'Flanagan, Ciara, primary, Hursting, Stephen D., primary, Richard, Vincent R., primary, Zahedi, René P., primary, Borchers, Christoph H., primary, Bareke, Eric, primary, Nabavi, Sheida, primary, Tonellato, Peter, primary, Roy, Josée-Anne, primary, Robidoux, André, primary, Marcus, Elizabeth A., primary, Mihalcioiu, Catalin, primary, Majewski, Jacek, primary, and Basik, Mark, primary

Published
2023
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25. Suppl Figure 1-6, Suppl Table S0 and Suppl Methods from A Unique Morphological Phenotype in Chemoresistant Triple-Negative Breast Cancer Reveals Metabolic Reprogramming and PLIN4 Expression as a Molecular Vulnerability

Author

Sirois, Isabelle, primary, Aguilar-Mahecha, Adriana, primary, Lafleur, Josiane, primary, Fowler, Emma, primary, Vu, Viet, primary, Scriver, Michelle, primary, Buchanan, Marguerite, primary, Chabot, Catherine, primary, Ramanathan, Aparna, primary, Balachandran, Banujan, primary, Légaré, Stéphanie, primary, Przybytkowski, Ewa, primary, Lan, Cathy, primary, Krzemien, Urszula, primary, Cavallone, Luca, primary, Aleynikova, Olga, primary, Ferrario, Cristiano, primary, Guilbert, Marie-Christine, primary, Benlimame, Naciba, primary, Saad, Amine, primary, Alaoui-Jamali, Moulay, primary, Saragovi, Horace Uri, primary, Josephy, Sylvia, primary, O'Flanagan, Ciara, primary, Hursting, Stephen D., primary, Richard, Vincent R., primary, Zahedi, René P., primary, Borchers, Christoph H., primary, Bareke, Eric, primary, Nabavi, Sheida, primary, Tonellato, Peter, primary, Roy, Josée-Anne, primary, Robidoux, André, primary, Marcus, Elizabeth A., primary, Mihalcioiu, Catalin, primary, Majewski, Jacek, primary, and Basik, Mark, primary

Published
2023
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27. A Multimodal Graph Neural Network Framework for Cancer Molecular Subtype Classification

Author

Li, Bingjun and Nabavi, Sheida

Subjects
Genomics (q-bio.GN), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Biological sciences, Quantitative Biology - Genomics, Machine Learning (cs.LG)
Abstract

The recent development of high-throughput sequencing creates a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Current multi-omics integrative models mainly use early fusion by concatenation or late fusion based on deep neural networks. Due to the nature of biological systems, graphs are a better representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods lack testing on a more complex cancer classification task. We propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs that combines both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We test the proposed model on TCGA Pan-cancer dataset and TCGA breast cancer dataset for molecular subtype and cancer subtype classification, respectively. The proposed model outperforms four current state-of-the-art baseline models in multiple evaluation metrics. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information., Comment: 18 pages, 4 figure

Published
2023
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43. Two-dimensional pulse response and media noise modeling for bit-patterned media

Author

Nabavi, Sheida, Kumar, B.V.K. Vijaya, and Bain, James A.

Subjects
Fluctuations (Physics) -- Analysis, Business, Electronics, Electronics and electrical industries
Abstract

In high density bit-patterned media (BPM), bits or magnetic islands are very close leading to significant inter-track interference (ITI) in addition to inter-symbol interference (ISI). Also, in BPM, major sources of media noise are the fluctuations in the location of the magnetic islands and fluctuations in the sizes of the magnetic islands. The location fluctuations can happen in both along-the-track and in cross-track directions. The size fluctuations affect both ISI and ITI. In order to obtain a more realistic channel model that includes the 2-D interference and the 2-D impact of media noise, we have developed a 2-D pulse response simulator for BPM. Utilizing the 2-D pulse response, we model the ITI and the media noise. Simulation results show that to achieve an acceptable bit error rate (BER), the location and size fluctuations should be smaller than 8% of the bit period at a density of 1.5 Tb/[in.sup.2]. Index Terms--Bit-patterned media, media noise modeling, 2-D pulse response.

Published
2008

44. Additional file 1 of Convolutional neural network for automated mass segmentation in mammography

Author

Abdelhafiz, Dina, Jinbo Bi, Ammar, Reda, Yang, Clifford, and Nabavi, Sheida

Abstract

Additional file 1 Supplementary materials (semantic segmentation using FCN, semantic segmentation using SegNet, semantic segmentation using Dilated-Net, localization using Faster R-CNN, comparison between state-of-the-art DL models, Supplementary Table 1, Supplementary Figures 1–4).

Published
2020
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