ORCID Profile
0000-0002-9565-217X
Current Organisations
RMIT University
,
University of Melbourne
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Publisher: IEEE
Date: 2013
Publisher: IEEE
Date: 06-2009
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 09-2016
Publisher: Elsevier BV
Date: 11-2018
Publisher: Cold Spring Harbor Laboratory
Date: 13-11-2020
DOI: 10.1101/2020.11.13.381301
Abstract: The key method for determining the structure of a protein to date is X-ray crystallography, which is a very expensive technique that suffers from high attrition rate. On the contrary, a sequence-based predictor that is capable of accurately determining protein crystallization property, would not only overcome such limitations, but also would reduce the trial-and-error settings required to perform crystallization. In this work, to predict protein crystallizability, we have developed a novel sequence-based hybrid method that employs two separate, yet fully automated, concepts for extracting features from protein sequences. Specifically, we use a deep convolutional neural network on a publicly available dataset to extract descriptive features directly from the sequences, then fuse such feature with structural-and-physio-chemical driven features (such as amino-acid composition or AAIndex-based physicochemical properties). Dimentionality reduction is then performed on the resulting features and the output vectors are applied to train optimized gradient boosting machine (XGBoostt). We evaluate our method through three publicly available test sets, and show that our proposed DHS-Crystallize algorithm outperforms state-of-the-art methods, and achieves higher performance compared to using DCNN-deriven features, or structural-and-physio-chemical driven features alone.
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 09-2013
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 03-2016
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 05-2017
DOI: 10.1109/FG.2017.149
Publisher: MDPI AG
Date: 10-08-2023
DOI: 10.3390/APP13169114
Abstract: Background: The Sella Turcica is a critical structure from an orthodontic perspective, and its morphological characteristics can help in understanding craniofacial deformities. However, accurately extracting Sella Turcica shapes can be challenging due to the indistinct edges and indefinite boundaries present in X-ray images. This study aimed to develop and validate an automated Sella Morphology Network (SellaMorph-Net), a hybrid deep learning pipeline for segmenting Sella Turcica structure and extracting different morphological types Methods: The SellaMorph-Net model proposed in this study combined attention-gating and recurrent residual convolutional layers (AGM and RrCL) to enhance the encoder’s abilities. The model’s output was then passed through a squeeze-and-excitation (SE) module to improve the network’s robustness. In addition, dropout layers were added to the end of each convolution block to prevent overfitting. A Zero-shot classifier was employed for multiple classifications, and the model’s output layer used five colour codes to represent different morphological types. The model’s performance was evaluated using various quantitative metrics, such as global accuracy and mean pixel-wise Intersection over Union (IoU) and dice coefficient, based on qualitative results Results: The study collected 1653 radiographic images and categorised them into four classes based on the predefined shape of Sella Turcica. These classes were further ided into three subgroups based on the complexity of the Sella structures. The proposed SellaMorph-Net model achieved a global accuracy of 97.570, mean pixel-wise IoU scores of 0.7129, and a dice coefficient of 0.7324, significantly outperforming the VGG-19 and InceptionV3 models. The publicly available IEEE ISBI 2015 challenge dataset and our dataset were used to evaluate the test performance between the state-of-the-art and proposed models. The proposed model provided higher testing results, which were 0.7314 IoU and 0.7768 dice for our dataset and 0.7864 IoU and 0.8313 dice for the challenge dataset Conclusions: The proposed hybrid SellaMorph-Net model provides an accurate and reliable pipeline for detecting morphological types of Sella Turcica using full lateral cephalometric images. Future work will focus on further improvement and utilisation of the developed model as a prognostic tool for predicting anomalies related to Sella structures.
Publisher: Springer Science and Business Media LLC
Date: 04-01-2021
Publisher: Springer Science and Business Media LLC
Date: 09-09-2020
Publisher: IEEE
Date: 02-2017
No related grants have been discovered for Azadeh Alavi.