Protein Classification with Extended-Sequence Coding by Sliding Window

Rodrigues, Thenner Silva; Cardoso, Fernanda C.; Teixeira, Santuza Maria Ribeiro; Oliveira, Sérgio C.; Braga, Antônio de Pádua

doi:10.1109/tcbb.2011.78

Cited by 4 publications

(2 citation statements)

References 55 publications

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“…Meanwhile, we can still localize individual protein fragments. This is due to the sliding window , property of the CNN within the leap block.…”

Section: Materials and Methodsmentioning

confidence: 99%

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MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

Feng,

Zhang,

Zhou

et al. 2024

J. Chem. Inf. Model.

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Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug−target interaction predictions.

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“…Meanwhile, we can still localize individual protein fragments. This is due to the sliding window , property of the CNN within the leap block.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Meanwhile, we can still localize individual protein fragments. This is due to the sliding window 43,44 property of the CNN within the leap block. Visualization of the three-dimensional structure is achieved using the PDB 45 Web site and Pymol.…”

Section: Molecular Level Interpretability Analysismentioning

confidence: 99%

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

Feng,

Zhang,

Zhou

et al. 2024

J. Chem. Inf. Model.

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A PSO-AB classifier for solving sequence classification problems

Tsai

Chen

2015

Applied Soft Computing

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Imbalanced Protein Data Classification Using Ensemble FTM-SVM

Dai

2015

IEEE Trans.on Nanobioscience

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Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems. The protein in one class are seen as positive examples while those outside the class are seen as negative examples. However, the class imbalance problem will arise in this case because the number of protein in one class is usually much smaller than that of the protein outside the class. To handle the challenge, we propose a novel framework to classify the protein. We firstly use free scores (FS) to perform feature extraction for protein; then, the inverse random under sampling (IRUS) is used to create a large number of distinct training sets; next, we use a new ensemble approach to combine these distinct training sets with a new fuzzy total margin support vector machine (FTM-SVM) that we have constructed. we call the novel ensemble classifier as ensemble fuzzy total margin support vector machine (EnFTM-SVM). We then give a full description of our method, including the details of its derivation. Finally, experimental results on fourteen benchmark protein data sets indicate that the proposed method outperforms many state-of-the-art protein classifying methods.

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Protein Classification with Extended-Sequence Coding by Sliding Window

Cited by 4 publications

References 55 publications

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

A PSO-AB classifier for solving sequence classification problems

Imbalanced Protein Data Classification Using Ensemble FTM-SVM

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