Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction

Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.

Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.

Using Nearest Feature Line and Tunable Nearest Neighbor methods for prediction of protein subcellular locations

A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization

Prediction subcellular localization of Gram-negative bacterial proteins by support vector machine using wavelet denoising and Chou's pseudo amino acid composition

This paper proposes a novel feature fusion method for the protein subcellular multiple-site localization prediction. Several types of features are employed in this novel protein coding method. The first one is the composition of amino acids. The second is pseudo amino acid composition, which mainly extract the location information of each amino acid residues in protein sequence. Lastly, the information for local sequence of amino acids is taken into consideration in this research. Generally, k nearest neighbor, supporting vector machine and other methods, has been used in the field of protein subcellular localization prediction. In our research, the multi-label k nearest neighbor algorithm has been employed in the classification model. The overall accuracy rate may reach 66.7304% in Gnos-mploc dataset.

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.

Determining the subcellular localization of viral proteins is indispensable for understanding the activity of the virus and inferring viral protein functions. Although previous studies about predicting viral protein subcellular localization have been developed, they often have the following disadvantages: (i) only focusing on a part of proteins of a species (ii) not considering the presence of multi-location proteins and (iii) lacking interpretability for the results. To address these problems, this paper is firstly predicting all the subcellular localization of the whole viral proteome in the UniProtKB and is interpretable for the results. This paper gives high prediction accuracy for the single-location and multi-location viral proteins by the FUEL-mLoc predictor. More importantly, we did deeply analysis and interpretation of the subcellular localization of all viral proteins. Finally, we have found some essential GO terms which are interpretable for the results and are significant in predicting the subcellular localization of the viral proteins.

Protein subcellular location prediction is an important problem in bioinformatics. It is highly desirable to predict a protein's subcellular location from its sequence. We propose a novel prediction model combined with locality-sensitive hashing (LSH)-based approximate nearest neighbor searching (ANNS) and a global alignment dynamic programming algorithm. LSH was used to hash map protein sequence amino acid composition vector features, where sequences with similar features were placed into a hash bucket of corresponding key values in a hash table. Then, we determined similar sequences to the target sequence in the hash table, and compared them to the sequence of the closest Euclidean distance using the global alignment dynamic programming algorithm to predict the protein subcellular localization. Compared with other algorithms, this prediction model recorded relatively high overall accuracies on two benchmark datasets via jackknife testing, and it predicted target sequences quickly and effectively.

Adaboost algorithm with improved K-nearest neighbor classifiers is proposed to predict protein subcellular locations. Improved K-nearest neighbor classifier uses three sequence feature vectors including amino acid composition, dipeptide and pseudo amino acid composition of protein sequence. K-nearest neighbor uses Blast in classification stage. The overall success rates by the jackknife test on two data sets of CH317 and Gram1253 are 92.4% and 93.1%. Adaboost algorithm with the novel K-nearest neighbor improved by Blast is an effective method for predicting subcellular locations of proteins.

A huge number of protein sequences have been generated and collected. However, the functions of most of them are still unknown. Protein subcellular localization is important to elucidate protein function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been done to accomplish such a task, there is the need for further research to improve the accuracy of prediction. In this paper, with K-local Hyperplane Distance Nearest Neighbor algorithm (HKNN) as base classifier, an ensemble classifier is proposed to predict the subcellular locations of proteins in eukaryotic cells. Each basic HKNN classifiers are constructed from a separated feature set, and finally combined with majority voting scheme. Results obtained through 5-fold cross-validation test on the same protein dataset showed an improvement in pre-diction accuracy over existing algorithms.

The structural class and subcellular location are the two important features of proteins that are closely related to their biological functions. With the rapid increase in new protein sequences entering into data banks, it is highly desirable to develop a fast and accurate method for predicting the attributes of these features for them. This can expedite the functionality determination of new proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Various prediction methods have been developed during the last two decades. This review is devoted to presenting a systematic introduction and comparison of the existing methods in respect to the prediction algorithm and classification scheme. The attention is focused on the state-of-the-art, which is featured by the covarient-discriminant algorithm developed very recently, as well as some new classification schemes for protein structural classes and subcellular locations. Particularly, addressed are also the physical chemistry foundation of the existing prediction methods, and the essence why the covariant-discriminant algorithm is so powerful.

The subcellular localization of proteins is very important for further understanding their functions. This paper proposes a deep learning method called PBLoc to predict the protein subcellular localization from sequence information only. In the PBLoc method, the pre-trained protein model–ProtBert, is utilized to extract the features of the protein sequence, followed by a bidirectional GRU model to predict subcellular localization. In order to capture sequence features more accurately, an attention mechanism is also used in the model. To verify the performance of our proposed PBLoc method, it is applied to DeepLoc benchmark dataset. For comparison, other 8 SOTA algorithms are also executed. The results show that PBLoc without using any evolution feature outperforms the other compared algorithms.

Protein Subcellular Localization (PSL) prediction of recently evolved Unknown Protein Sequence (UPS) is vital for understanding the protein functions. Although PSL provides insight into the prediction of harmful and useful characteristics, diagnosis of disease and drug design. In the present work One-Hot-Encoding (OHE) and Convolutional Neural Network (CNN) based OCNN model is proposed for the functional characterization of protein sequence through the PSL. Gram-Positive (G+) dataset with 473 known protein sequence samples including four subcellular localizations is used for the training and validation of the OCNN model. As essential preprocessing raw protein sequence has been encoded using OHE, as well as the length of the encoded sequence are standardized and normalized through padding and capping. Next, encoded and standardized protein sequence samples are convoluted in the hidden layer of the OCNN model using ReLU, TanH, and Sigmoid activation function. After that Adam and Stochastic Gradient Decent (SGD) optimization function are utilized for the PSL prediction of the protein sequence samples. OCNN model achieved 92.94% of accuracy through combination of Sigmoid, Softmax, and Adam functions with known protein sequences. The validated OCNN model can be further utilized for the function prediction of UPS, where 64.83% accuracy is achieved through the combination of ReLU, Softmax, and Adam functions.

Protein Sub-Cellular Localization is very important information as they play a crucial role in their functions. Thus, prediction of protein Sub-Cellular Localization has become very promising and challenging problem in the field of Bioinformatics. Recently, a number of computational methods based on amino acid compositions or on the functional domain or sorting signal. But, they lack of contextual information of the protein sequence. In this paper, an ensemble classifier, PLoc-Euk is proposed to predict sub-cellular location for the eukaryotic proteins which uses multiple physico-chemical properties of amino acid along with their composition. PLoC-Euk aims to predict protein Sub-Cellular Localization in eukaryotes across five different locations, namely, Cell Wall, Cytoplasm, Extracellular, Mitochondrion, and Nucleus. The classifier is applied to the dataset extracted from http://www.bioinfo.tsinghua.edu.cn/~guotao/data/ and achieves 73.37% overall accuracy.