A deep learning framework for improving protein interaction prediction using sequence properties

Guo, Yun; Chen, X

doi:10.1101/843755

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…More recent work has leveraged deep learning-based architectures such as stacked autoencoders, recurrent neural networks (RNNs), and recurrent convolutional neural networks (RCNNs) [9,15,34]. These models have achieved state-ofthe-art accuracy in the binary PPI classification task, as well as the ability to generalize to similar PPI characterization tasks such as interaction type prediction and binding affinity estimation [9,15]. Despite this success, they still demonstrate an inability to transfer learned knowledge to more general protein prediction tasks.…”

Section: Taskmentioning

confidence: 99%

Transforming the Language of Life

Nambiar

Heflin

Liu

et al. 2020

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Section: Taskmentioning

confidence: 99%

Transforming the Language of Life

Nambiar

Heflin

Liu

et al. 2020

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…As a subfield of machine learning approaches, deep learning methods have been shown to exhibit unprecedented performance in various areas of biological prediction [51][52][53][54][55][56][57][58][59][60][61] . We described a novel deep neural network model in the present study, termed AptaNet, for predicting API.…”

Section: Discussionmentioning

confidence: 99%

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Emami

Ferdousi

2021

Sci Rep

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Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet.

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“…More recent work using deep learning involves models that leverage architectures such as stacked autoencoders, recurrent neural networks (RNNs), and recurrent convolutional neural networks (RCNNs) [4,43,44]. These models have achieved state-of-the-art accuracy in the binary PPI classification task, as well as the ability to generalize to similar PPI characterization tasks such as interaction type prediction and binding affinity estimation [4,43]. Despite this success, they still demonstrate an inability to transfer learned knowledge to more general protein prediction tasks.…”

Section: Task: Protein-protein Interaction (Ppi) Predictionmentioning

confidence: 99%

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Nambiar

Liu

Hopkins

et al. 2020

Preprint

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The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the art approaches for protein family classification, while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.to identify functional characteristics is critical to understanding cellular functions as well as developing potential therapeutic applications [4]. Sequence-based methods to computationally infer protein characteristics have been critical for inferring protein function and other characteristics [5]. Thus, the development of computational methods to infer protein characteristics (which we generally describe as "protein prediction tasks") has become paramount in the field of bioinformatics and computational biology. Here, we develop a Transformer neural network to establish task-agnostic representations of protein sequences, and use the Transformer network to solve two protein prediction tasks. Background: Deep LearningDeep learning, a class of machine learning based on the use of artificial neural networks, has recently transformed the field of computational biology and medicine through its application towards long-standing problems such as image analysis, gene expression modeling, sequence variant calling, and putative drug discovery [6,7,8,9,10]. By leveraging deep learning, field specialists have been able to efficiently design and train models without the extensive feature engineering required by previous methods. In applying deep learning to sequence-based protein characterization tasks, we first consider the field of natural language processing (NLP), which aims to analyze human language through computational techniques [11]. Deep learning has recently proven to be a critical tool for NLP, achieving state-ofthe-art performance on benchmarks for named entity recognition, sentiment analysis, question answering, and text summarization, among others [12,13].Neural networks are functions that map one vector space to another. Thus, in order to use them for NLP tasks, we first need to represent words as real-valued vectors. Often referred to as word embeddings, these vector representations are typically "pre-trained" on an auxiliary task for which we have (or can automatically generate) a large amount of training data. The goal of this pre-training is to learn generically useful representations that enc...

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A deep learning framework for improving protein interaction prediction using sequence properties

Cited by 5 publications

References 43 publications

Transforming the Language of Life

Transforming the Language of Life

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

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