A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

Wang, Pan; Zhang, Guiyang; Yu, Zu-Guo; Huang, Guohua

doi:10.3389/fgene.2021.752732

Cited by 10 publications

(5 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [83] introduced a technique using deep learning combined with XGBoost, termed DeepPPISPXGB, to forecast protein-protein interaction sites and protein functionality. The deep learning framework acted as a mechanism to filter out superfluous data from protein sequences.…”

Section: A Extreme Gradient Boosting (Xgboost) Techniquementioning

confidence: 99%

“…Incorrectly tuned parameters could lead to poor performance, (6) RF can be sensitive to noisy labels, (7) Training a RF on large biological datasets is computationally intensive, (8) RF can get stuck in local optima, which may not be the global optimum for the problem space Autoencoders [83][84][85][86][87] The input protein data is passed through an "encoder" network, which compresses it into a "latent" or "hidden" lower-dimensional representation. This hidden layer captures the essential features needed to describe the protein.…”

Section: Random Forest (Rf) [80-82]mentioning

confidence: 99%

See 1 more Smart Citation

Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations

Taha

2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: A Extreme Gradient Boosting (Xgboost) Techniquementioning

confidence: 99%

Section: Random Forest (Rf) [80-82]mentioning

confidence: 99%

Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations

Taha

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Predicting the sites of protein–protein interactions has also been a subject of focus. Wang et al [ 78 ] introduced DeepPPISP-XGB, a method integrating deep learning and XGBoost for the prediction of PPI sites. In another study, Orasch et al [ 74 ] presented a new deep learning architecture based on graph representation learning for predicting interaction sites and interactions of proteins.…”

Section: Representation Learning and Autoencoder For Protein–protein ...mentioning

confidence: 99%

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

Lee

2023

Molecules

View full text Add to dashboard Cite

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.

show abstract

“…and it has been used extensively for protein classification problems 63,64 . As our analysis had revealed a gradient of adaptations with the order extracellular > periplasmic > cytoplasmic,…”

Section: Machine Learning Model Design and Validationmentioning

confidence: 99%

A novel artificial intelligence tool for identification of extracellular proteins doubles current estimate of the marine secretome

Baltar,

Zaragoza-Solas

2023

Preprint

View full text Add to dashboard Cite

Microbes are the engines driving the elemental cycles. In order to interact with their environment and the community, microbes secrete proteins into the environment (known collectively as the secretome), where they remain active for prolonged periods of time. Despite the environmental relevance of microbes and the vast quantities of marine prokaryotic sequences available, our knowledge of the marine secretome remains limited due to the available in silico methods for the study of secreted proteins, which are not suited to their use in large datasets due to their low throughput. An alternative and potentially better approach to characterise the secretome is to combine modern machine learning tools with the evolutionary adaptation changes of the proteome to the marine environment. In this study, we found and described adaptations of marine extracellular proteins, which vary between phyla, resulting in differences in ATP costs, amino acid composition and nitrogen and sulphur content. With these adaptations in mind, we developed ‘Ayu’, a machine prediction tool that does not employ homology-based predictors (as current methods do) and achieves better performance than current state-of-the-art software at a fraction of the time. When applied to oceanic samples (Tara Oceans dataset), our method was able to recover more than double the proteins compared to the most widely used method to identify secreted proteins, indicating that more than half of secreted proteins are presently unconsidered in current secrtome analysis. The application of this powerful novel to open ocean samples allowed to better characterise the real composition of the marine secretome, allowing for a better recognition and understanding of the secretome in marine ecosystems.

show abstract

A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

Cited by 10 publications

References 104 publications

Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations

Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations

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

A novel artificial intelligence tool for identification of extracellular proteins doubles current estimate of the marine secretome

Contact Info

Product

Resources

About