iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec

Zheng, Jie; Xiao, Xuan; Qiu, Wang-Ren

doi:10.3389/fgene.2021.738274

Cited by 6 publications

(5 citation statements)

References 43 publications

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“…We evaluated the BERT_Mean + DWT feature extraction method and compared it with several other classic protein and drug feature extraction methods, such as Pr ord2vec (a 64-D vector is obtained to represent the protein, it was extracted by an un-supervised word2vec model and implicated important biophysical and biochemical information ( Yang et al, 2018 ; Zhang et al, 2020 ), BERT_First (the first row of

is obtained to represented protein, it is a 1024-D vector) ( Nambiar et al, 2020 ), FP2_Word2vec ( Jaeger et al, 2018 ), drug_Node2vec ( Grover and Leskovec, 2016 ; Tetko et al, 2020 ), drug_Word2vec ( Zhang et al, 2020 ; Zheng et al, 2021 ), drug_GCN ( Chen et al, 2020 ). Figures 3 – 6 show the Matthews correlation coefficient (MCC) for the datasets

, and

obtained for each approach in CNN + BRL classifier via 10-fold cross validation.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, many word-embedding methods have been used for protein feature extraction, for example, Zheng et al identified the ion channel-drug interaction using both word2vec and node2vec as molecular representation learning methods ( Zheng et al, 2021 ). However, there are still imperfect, like in these word-embedding methods may map every word with their unique vector, therefore this representation is context-independent.…”

Section: Methodsmentioning

confidence: 99%

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DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

2022

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Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

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, and

obtained for each approach in CNN + BRL classifier via 10-fold cross validation.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

2022

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“…Once the benchmark dataset has been prepared for the study, the next important step is formulating the samples and extracting the best feature set for constructing a robust and superior computational model. In recent years, various feature encoding strategies have been used to form biological sequence fragments, such as PseKNC ( 17 ), One-hot ( 21 , 22 ), physicochemical features, and word2vec ( 23 – 25 ). This study selected some of the most common feature encoding approaches, including six physicochemical feature encoding strategies and the frequency of occurrence of k-nearest neighbor nucleic acids, to describe RNA fragments.…”

Section: Methodsmentioning

confidence: 99%

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA

Lou,

Qiu,

Liu

et al. 2023

Front. Immunol.

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N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites. In this paper, we propose a comprehensive ensemble learning model, the Stacking-based heterogeneous integrated ac4C model, engineered explicitly to identify ac4C sites. This innovative model integrates three distinct feature extraction methodologies: Kmer, electron-ion interaction pseudo-potential values (PseEIIP), and pseudo-K-tuple nucleotide composition (PseKNC). The model also incorporates the robust Cluster Centroids algorithm to enhance its performance in dealing with imbalanced data and alleviate underfitting issues. Our independent testing experiments indicate that our proposed model improves the Mcc by 15.61% and the ROC by 5.97% compared to existing models. To test our model’s adaptability, we also utilized a balanced dataset assembled by the authors of iRNA-ac4C. Our model showed an increase in Sn of 4.1%, an increase in Acc of nearly 1%, and ROC improvement of 0.35% on this balanced dataset. The code for our model is freely accessible at https://github.com/louliliang/ST-ac4C.git, allowing users to quickly build their model without dealing with complicated mathematical equations.

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“…For protein feature extraction, numerous word-embedding techniques were recently utilized [ 35 ], but these techniques may map each word to its vector, making this representation context-independent. In response to the exponential growth of textual data, the first fine-tuning-based representation model, bidirectional encoder representations from transformers (BERT) [ 36 ], can generate distinct representations for the same word based on context [ 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

A Robust Drug–Target Interaction Prediction Framework with Capsule Network and Transfer Learning

Huang,

Chen

et al. 2023

IJMS

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Drug–target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug–target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug–target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug–target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.

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iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec

Cited by 6 publications

References 43 publications

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA

A Robust Drug–Target Interaction Prediction Framework with Capsule Network and Transfer Learning

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