DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters

Taju, Semmy Wellem; Nguyen, Trinh Trung Duong; Le, Nguyen Quoc Khanh; Kusuma, Rosdyana Mangir Irawan; Ou, Yu‐Yen

doi:10.1093/bioinformatics/bty302

Cited by 41 publications

(22 citation statements)

References 51 publications

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“…The PSSM features were generated by summing the same amino acid rows in the PSSM profile and then divided by the sequence length and finally scaled by some feature normalization techniques (see “Feature normalizations” section). Indeed, PSSM is the best feature to produce relevant evolutionary information from FASTA format and more importantly, this feature can be applied to various problems, with promising results when compared to other feature extraction methods.…”

Section: Methodsmentioning

confidence: 99%

“…We trained our models using CNNs which are one of the most popular deep learning or DNNs architectures in the branch of machine‐learning field. Basically, CNN is taking biological inspiration from the visual cortex and this approach is most commonly applied to analyze visual images . CNN is a neural network type consisting of more than one hidden layer and a type of artificial intelligence demonstrated by machines through programming to do tasks such as predicting, classifying which also be performed by humans in general.…”

Section: Methodsmentioning

confidence: 99%

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DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Taju

2019

J Comput Chem

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The movement of ions across the cell membrane is an essential for many biological processes. This study is focused on ion channels and ion transporters (pumps) as types of border guards control the incessant traffic of ions across cell membranes. Ion channels and ion transporters function to regulate membrane potential and electrical signaling and play important roles in cell proliferation, migration, apoptosis, and differentiation. In their behaviors, it is found that ion channels differ significantly from ion transporters. Therefore, a method for automatically classifying ion transporters and ion channels from membrane proteins is proposed by training deep neural networks and using the position‐specific scoring matrix profile as an input. The key of novelty is the three‐stage approach, in which five techniques for data normalization are used; next three imbalanced data techniques are applied to the minority classes and then, six classifiers are compared with the proposed method. © 2019 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Taju

2019

J Comput Chem

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“…Evolutionary information in the form of position-specific scoring matrices has long been the de-facto features in many protein bioinformatics problems. [39][40][41][42][43][44] They can be flexibly employed to input into numerous traditional machine learning algorithms as well as a popular deep learning architecture namely convolutional neural network. Two previous studies mentioned above on electron complex classification also utilized this type of information as main features.…”

Section: Baseline Classification Performance With Pssm Featuresmentioning

confidence: 99%

Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain

Nguyen

et al. 2020

Molecular Informatics

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We herein proposed a novel approach based on the language representation learning method to categorize electron complex proteins into 5 types. The idea is stemmed from the the shared characteristics of human language and protein sequence language, thus advanced natural language processing techniques were used for extracting useful features. Specifically, we employed transfer learning and word embedding techniques to analyze electron complex sequences and create efficient feature sets before using a support vector machine algorithm to classify them. During the 5-fold cross-validation processes, seven types of sequence-based features were analyzed to find the optimal features. On an average, our final classification models achieved the accuracy, specificity, sensitivity, and MCC of 96 %, 96.1 %, 95.3 %, and 0.86, respectively on cross-validation data. For the independent test data, those corresponding performance scores are 95.3 %, 92.6 %, 94 %, and 0.87. We concluded that using feature extracted using these representation learning methods, the prediction performance of simple machine learning algorithm is on par with existing deep neural network method on the task of categorizing electron complexes while enjoying a much faster way for feature generation. Furthermore, the results also showed that the combination of features learned from the representation learning methods and sequence motif counts helps yield better performance.

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“…We used four widely-used metrics sensitivity (Sen), specificity (Spec), accuracy (Acc) and Matthews's correlation coefficient (MCC) (see, e.g. [28,[44][45][46][47]) to measure the predictive performance. In Additional file 2, the formulae of these metrics are presented.…”

Section: Assessment Of Predictive Abilitymentioning

confidence: 99%

TNFPred: Identifying tumor necrosis factors using hybrid features based on word embeddings

Nguyen

et al. 2019

Preprint

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BackgroundCytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes including immunity regulation, hematopoiesis, and inflammation.As one important family of cytokines, tumor necrosis factors have association with the regulation of a various biological processes such as proliferation and differentiation of cells, apoptosis, lipid metabolism, and coagulation. The implication of these cytokines can also be seen in various diseases such as insulin resistance, autoimmune diseases, and cancer. Considering the interdependence between this kind of cytokine and others, classifying tumor necrosis factors from other cytokines is a challenge for biological scientists. In this research, we employed a word embedding technique to create hybrid features which was proved to efficiently identify tumor necrosis factors given cytokine sequences. We segmented each protein sequence into protein words and created corresponding word embedding for each word. Then, word embedding-based vector for each sequence was created and input into machine learning classification models. When extracting feature sets, we not only diversified segmentation sizes of protein sequence but also conducted different combinations among split grams to find the best features which generated the optimal prediction. Furthermore, our methodology follows Chou's 5-step rules to build a reliable classification tool. ResultsWith our proposed hybrid features, prediction models obtain more promising performance compared to seven prominent sequenced-based feature kinds. Results from 10 independent runs on the surveyed dataset show that on an average, our optimal models obtain an area under the curve of 0.984 and 0.998 on 5-fold cross-validation and independent test, respectively. ConclusionsThese results show that biologists can use our model to identify tumor necrosis factors from other cytokines efficiently. Moreover, this study proves that natural language processing techniques can be applied reasonably to help biologists solve bioinformatics problems efficiently.

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DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 41 publications

References 51 publications

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain

TNFPred: Identifying tumor necrosis factors using hybrid features based on word embeddings

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