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A Review for Artificial-Intelligence-Based Protein Subcellular Localization
2024
Biomolecules
Preserved Fulltext
To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. ...
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. ...
SURF: Speeded Up Robust Features. SIFT: Scale-Invariant Feature Transform. SVM: Support Vector Machine. KNN: K-Nearest Neighbor. RF: Random Forest. ...
doi:10.3390/biom14040409
pmid:38672426
pmcid:PMC11048326
fatcat:slimbidr5jaghlgws5qgl6uuce
X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction
[article]
2021
bioRxiv
pre-print
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Motivation: Identifying the drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. ...
For informative protein representation, we constructed a structure-aware graph neural network method from the protein sequence by combining predicted contact maps and graph neural networks. ...
Acknowledgments We thank the Galixir team for its support and discussion, and with special thanks to Jixian Zhang, Zixuan Liu and Da Wei for the experimental design discussion and technical support. ...
doi:10.1101/2021.06.17.448780
fatcat:imttzr64cvdw5lec2enomcptbm
Guest Editorial for Special Section on the 14th International Conference on Intelligent Computing (ICIC)
2020
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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The next paper, "Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature" by Zhen Shen, Su-Ping Deng, and De-Shuang Huang, proposed an improved capsule network to predict RNA-protein ...
ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. ...
This section ends with the paper, "Using Weighted Extreme Learning Machine Combined with Scale-invariant Feature Transform to Predict Protein-Protein Interaction from Protein Evolutionary Information" ...
doi:10.1109/tcbb.2020.2989800
fatcat:ftkyapn3rzahdfjqbashrp5l3m
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?
[article]
2021
arXiv
pre-print
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This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. ...
attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; (vi) and finally truly end-to-end ...
The authors thank Kliment Olechnovič from Vilnius University for his help with illustrating Voronoi cells and proof-reading the manuscript, and Bowen Jing for his feedback on the manuscript. ...
arXiv:2105.07407v2
fatcat:6szubg7q2rajlj3l4vyzqri3nm
Persistent spectral theory-guided protein engineering
[article]
2022
bioRxiv
pre-print
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While protein engineering, which iteratively optimizes protein fitness by screening the gigantic mutational space, is constrained by experimental capacity, various machine learning models have substantially ...
invariant, shape evolution, and sequence disparity in the protein fitness landscape. ...
The ESM transformer was also used to generate evolutionary score to predict fitness. ...
doi:10.1101/2022.12.18.520933
fatcat:yimhafumbzextaili5tbeeihae
Protein structure prediction by AlphaFold2: are attention and symmetries all you need?
2021
Acta Crystallographica Section D: Structural Biology
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Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to ...
In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate ...
Funding information This work is supported by the DARPA PANACEA program grant HR0011-19-2-0022 and NCI grant U54-CA225088. ...
doi:10.1107/s2059798321007531
pmid:34342271
pmcid:PMC8329862
fatcat:sam47cns4fhg3hgo273qoshlta
PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences
2017
International Journal of Molecular Sciences
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In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. ...
Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment ...
Shen et al. adopted a SVM model to predict PPI network by combining Skernel function of protein pairs with a conjoint triad feature [18] . ...
doi:10.3390/ijms18051029
pmid:28492483
pmcid:PMC5454941
fatcat:45hctmtlljakhec4cwyi4ryp2q
Prediction of Drug-Target Interactions by Ensemble Learning Method from Protein Sequence and Drug Fingerprint
2020
IEEE Access
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Then, the LOOP is used to extract the feature vectors from PSSM, and the sub-structure information of drug molecule is represented as fingerprint features. ...
Specifically, the target protein sequence is firstly transformed as the PSSM, in which the evolutionary information of protein is retained. ...
Comparison of RF with other models Many machine learning models have been used to predict DTIs and most of them are based on traditional classifiers. ...
doi:10.1109/access.2020.3026479
fatcat:3hoeffsxrjbkpdmdfjbkiialti
Advances of Deep Learning in Protein Science: A Comprehensive Survey
[article]
2024
arXiv
pre-print
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In recent years, deep learning has emerged as a powerful tool for protein modeling due to its ability to learn complex patterns and representations from large-scale protein data. ...
Next, the survey presents various applications of deep learning in the field of proteins, including protein structure prediction, protein-protein interaction prediction, protein function prediction, etc ...
Researchers have recognized the potential of deep learning models to learn complex patterns and extract meaningful features from large-scale protein data, which includes information from protein sequences ...
arXiv:2403.05314v1
fatcat:w5knidms6zewfivkbu3owdumxe
Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction
[article]
2024
arXiv
pre-print
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In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. ...
This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions. ...
of information were extracted from protein sequence to create a feature dataset, including one-hot encoded protein sequences, evolutionary information, 57 predicted accessible surface area, 58 secondary ...
arXiv:2310.18249v2
fatcat:evv5hindergzjc6xa6ykf3h2oq
E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction
[article]
2022
bioRxiv
pre-print
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EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant ...
(i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. ...
While initial models focused on feature engineering with machine learning 6, 18, 25, 26 , subsequent work sought to capture more complex patterns using deep learning 7, 9, 12, 13, 16 . ...
doi:10.1101/2022.12.14.520476
fatcat:twtk6bkzgjeflexfwosbwkvs5m
PCLPred: A Bioinformatics Method for Predicting Protein–Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation
2018
International Journal of Molecular Sciences
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The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. ...
In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. ...
Using the PPI dataset, we show that the proposed method can quickly and effectively differentiate interactive protein pairs from large-scale data. ...
doi:10.3390/ijms19041029
pmid:29596363
pmcid:PMC5979371
fatcat:oca3fuiamzf4xiv6faecxmvw3m
Evaluating Protein Transfer Learning with TAPE
[article]
2019
bioRxiv
pre-print
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Protein modeling is an increasingly popular area of machine learning research. ...
Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to ...
We thank the AWS Educate program for providing us with the resources to train our models. ...
doi:10.1101/676825
fatcat:hd7zpclltnbq3ifruwwa7ppu3u
Protein Function Analysis through Machine Learning
2022
Biomolecules
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Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. ...
The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. ...
doi:10.3390/biom12091246
pmid:36139085
pmcid:PMC9496392
fatcat:yx37j5tyubhkrf5unj462mlvnm
Geometric Transformers for Protein Interface Contact Prediction
[article]
2022
arXiv
pre-print
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compatible with DeepInteract, thereby validating the effectiveness of the Geometric Transformer for learning rich relational-geometric features for downstream tasks on 3D protein structures. ...
In this work, we present the Geometric Transformer, a novel geometry-evolving graph transformer for rotation and translation-invariant protein interface contact prediction, packaged within DeepInteract ...
ETHICS STATEMENT DeepInteract is designed to be used for machine learning of protein molecular data. ...
arXiv:2110.02423v5
fatcat:ubsmffmqp5dh5g4s4mm6p62t2a
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