WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models

Abdollahi, Sina; Lin, Pin–Wen; Chiang, Jung-Hsien

doi:10.1109/jbhi.2021.3093441

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…Abdollahi et al 20 proposed a cancer-associated protein–protein interaction (PPI) based cancer prediction model. A deep learning algorithm is used in the model to understand the biological features of cancer.…”

Section: Related Workmentioning

confidence: 99%

“…No. Key techniques Dataset used Advantages Disadvantages 19 network-based driver gene prediction Patient data Critical data identification Limited discussion on methodolgy 20 cancer-associated protein–protein interaction (PPI) PPI data Improved accuracy Limited explanation on deep learning 21 deep multimodal stacked generalization approach for PPI Trained protein data Reduced energy consumption Limited graph attention 22 MMR-CRC DNA-immunohistochemistry (IHC) testing Sustainability in CRC prediction Lack of detailed MMR 23 CNN based approach PPI network Improved accuracy Lack of protein sample structure 24 DNL cancer prediction Clinical data samples Improved energy efficiency Lack of feature selection technique 25 multi-gene genetic programming algorithm Biological information’s protein amino acid ratio Reduced time and energy consumption Limited information on genetic progression 26 miRNA and lncRNA Three EPs of miRNA, lncRNA and PCG in database of the cancer genome atlas (TCGA) Solves optimization problems Complexity due to bigger dataset 27 DNN based lung cancer prediction …”

Section: Related Workmentioning

confidence: 99%

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Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Alruwaili,

Yousef,

Jumani

et al. 2024

Sci Rep

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Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Alruwaili,

Yousef,

Jumani

et al. 2024

Sci Rep

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“…Abdollahi et al [114] Developed WinBinVec, a window-based deep learning model to identify cancer PPIs.…”

Section: Author Contributionsmentioning

confidence: 99%

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Lee

2023

Molecules

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Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

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“…Up to now, numerous algorithms have been used in protein function prediction based on PPI networks, such as edge-betweenness clustering ( Dunn et al, 2005 ), Graphlet-based edge clustering ( Solava et al, 2012 ), clique percolation ( Adamcsek et al, 2006 ), GRAAL ( Kuchaiev et al, 2010 ), hybrid-property based method ( Hu et al, 2011 ), and IsoRank ( Singh et al, 2008 ). Moreover, advanced machine learning and deep learning techniques have also been used for protein function prediction, including collective classification ( Xiong et al, 2013 ; Wu et al, 2014 ), active learning ( Xiong et al, 2014 ), DeepInteract ( Sunil et al, 2017 ), ConvsPPIS ( Zhu et al, 2020 ), PhosIDN ( Yang et al, 2021 ) and WinBinVec ( Abdollahi et al, 2021 ), etc.…”

Section: Introductionmentioning

confidence: 99%

Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

et al. 2021

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Over the past decades, massive amounts of protein-protein interaction (PPI) data have been accumulated due to the advancement of high-throughput technologies, and but data quality issues (noise or incompleteness) of PPI have been still affecting protein function prediction accuracy based on PPI networks. Although two main strategies of network reconstruction and edge enrichment have been reported on the effectiveness of boosting the prediction performance in numerous literature studies, there still lack comparative studies of the performance differences between network reconstruction and edge enrichment. Inspired by the question, this study first uses three protein similarity metrics (local, global and sequence) for network reconstruction and edge enrichment in PPI networks, and then evaluates the performance differences of network reconstruction, edge enrichment and the original networks on two real PPI datasets. The experimental results demonstrate that edge enrichment work better than both network reconstruction and original networks. Moreover, for the edge enrichment of PPI networks, the sequence similarity outperformes both local and global similarity. In summary, our study can help biologists select suitable pre-processing schemes and achieve better protein function prediction for PPI networks.

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WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models

Cited by 7 publications

References 24 publications

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

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