Incremental Feature Selection Method Research Articles

Matrix-based incremental feature selection method using weight-partitioned multigranulation rough set

Incremental feature selection methods have gained increasing research attention as they improve the efficiency of feature selection for dynamic datasets. Multigranulation rough set, as an extension of rough set theory, allows for a comprehensive and rational analysis of problems from multiple hierarchical and granular perspectives. However, existing research on granularity partitioning relies on the decision maker's subjective experience, which lacks convincing power. In this paper, we propose a generalized multigranulation neighborhood rough set based on weight partition model, using a matrix form. We discuss several properties and define a new entropy measure to evaluate feature importance. A heuristic feature selection algorithm is developed based on this entropy to search for the optimal subset. Furthermore, we discuss dynamic updating mechanism and design two incremental feature selection algorithms. Finally, we conduct experiments on 12 public datasets to evaluate the performance of the proposed algorithms and validate their effectiveness and efficiency in feature selection for both static and dynamic datasets.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

Machine Learning Reveals Impacts of Smoking on Gene Profiles of Different Cell Types in Lung.

Smoking significantly elevates the risk of lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. This risk is attributed to the harmful chemicals in tobacco smoke that damage lung tissue and impair lung function. Current research on the impact of smoking on gene expression in specific lung cells is limited. This study addresses this gap by analyzing gene expression profiles at the single-cell level from 43,539 lung endothelial cells, 234,349 lung epithelial cells, 189,843 lung immune cells, and 16,031 lung stromal cells using advanced machine learning techniques. The data, categorized by different lung cell types, were classified into three smoking states: active smoker, former smoker, and never smoker. Each cell sample encompassed 28,024 feature genes. Employing an incremental feature selection method within a computational framework, several specific genes have been identified as potential markers of smoking status in different lung cell types. These include B2M, EEF1A1, and TPT1 in lung endothelial cells; FTL and MT-ATP8 in lung epithelial cells; HLA-B and HLA-C in lung immune cells; and HSP90B1 and LCN2 in lung stroma cells. Additionally, this study developed quantitative rules for representing the gene expression patterns related to smoking. This research highlights the potential of machine learning in oncology, enhancing our molecular understanding of smoking's harm and laying the groundwork for future mechanism-based studies.

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy

The labor dispute is one of the most common civil disputes. It can be resolved in the order of the following steps, which include mediation in arbitration, arbitration award, first-instance mediation, first-instance judgment, and second-instance judgment. The process can cease at any step when it is successfully resolved. In recent years, due to the increasing rights awareness of employees, the number of labor disputes has been rising annually. However, resolving labor disputes is time-consuming and labor-intensive, which brings a heavy burden to employees and dispute resolution institutions. Using artificial intelligence algorithms to identify and predict the critical path of labor dispute resolution is helpful for saving resources and improving the efficiency of, and reducing the cost of dispute resolution. In this study, a machine learning approach based on Shapley Additive exPlanations (SHAP) and a soft voting strategy is applied to predict the critical path of labor dispute resolution. We name our approach LDMLSV (stands for Labor Dispute Machine Learning based on SHapley additive exPlanations and Voting). This approach employs three machine learning models (Random Forest, Extra Trees, and CatBoost) and then integrates them using a soft voting strategy. Additionally, SHAP is used to explain the model and analyze the feature contribution. Based on the ranking of feature importance obtained from SHAP and an incremental feature selection method, we obtained an optimal feature subset comprising 33 features. The LDMLSV achieves an accuracy of 0.90 on this optimal feature subset. Therefore, the proposed approach is a highly effective method for predicting the critical path of labor dispute resolution.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.

IIFS: An improved incremental feature selection method for protein sequence processing

MotivationDiscrete features can be obtained from protein sequences using a feature extraction method. These features are the basis of downstream processing of protein data, but it is necessary to screen and select some important features from them as they generally have data redundancy. ResultHere, we report IIFS, an improved incremental feature selection method that exploits a new subset search strategy to find the optimal feature set. IIFS combines nonadjacent sorting features to prevent the drawbacks of data explosion and excessive reliance on feature sorting results. The comparative experimental results on 27 feature sorting data show that IIFS can find more accurate and important features compared to existing methods.The IIFS approach also handles data redundancy more efficiently and finds more representative and discriminatory features while ensuring minimal feature dimensionality and good evaluation metrics. Moreover, we wrap this method and deploy it on a web server for access at http://112.124.26.17:8005/.

Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells.

Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.

Incremental unsupervised feature selection for dynamic incomplete multi-view data

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume that all views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I2MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I2MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental learning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body's immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2-4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1-4, and days 7-10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution.

Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types. In the present study, we investigated transcriptionally active chromosome segments identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types. Each cell was represented by existence status on 20,783 genes that were obtained from 436,206 active chromosome segments. The gene features were deeply analyzed by Boruta, resulting in 3897 genes, which were ranked in a list by Monte Carlo feature selection. Such list was further analyzed by incremental feature selection (IFS) method, yielding essential genes, classification rules and an efficient random forest (RF) classifier. To improve the performance of the optimal RF classifier, its features were further processed by autoencoder, light gradient boosting machine and IFS method. The final RF classifier with MCC of 0.838 was constructed. Some marker genes such as H2-Dmb2, which are specifically expressed in antigen-presenting cells (e.g., dendritic cells or macrophages), and Tenm2, which are specifically expressed in T cells, were identified in this study. Our analysis revealed numerous potential epigenetic modification patterns that are unique to particular cell types, thereby advancing knowledge of the critical functions of chromatin accessibility in cell processes.

Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.

Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.

Algorithm for early diagnosis of hepatocellular carcinoma based on gene pair similarity

The article proposes an algorithm based on intelligent methods for the early diagnosis of hepatocellular carcinoma (HCC), known as liver cancer, which is rated third cause of cancer deaths in the world. Initial diagnosis of HСC is based on laboratory studies, computer tomography and X-ray examination. However, in some cases, identifying cancerous tissues as similar non-cancerous tissues (cirrhotic tissues and normal tissues) made it necessary to perform gene analysis for the diagnosis. To predict HCC based on such numerous, diverse and heterogeneous unstructured data, preference is given to the method of artificial intelligence, i.e., machine learning. It shows the possibility of applying machine learning methods to solve the problem of accurate identification of HCC due to the compatibility of HCC tissues with identical CwoHCC non-cancerous tissues. The technology of gene pair profiling using relevant peer databases is described and the Within-Sample Relative Expression Orderings (REO) technique is used to determine the gene pair’s similarity. The article also presents a new approach based on The Within-Sample Relative Expression Orderings technique for determining the gene pair’s similarity, Incremental feature selection method for feature selection, and Support Vector Machine methods for gene pair classification. The proposed approach constitutes the methodological basis of a decision support system for the early diagnosis of HCC, and the development of such a system may be beneficial for physician decision support in the relevant field

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8+ T cells.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers around the world. Precision oncology will be crucial for further improving the prognosis of HCC patients. Compared with traditional bulk RNA-seq, single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of a great deal of individual cells assayed in an unbiased manner, showing the potential to deeply reveal tumor heterogeneity. In this study, based on the scRNA-seq results of primary neoplastic cells and paired normal liver cells from eight HCC patients, a new strategy of machine learning algorithms was applied to screen core biomarkers that distinguished HCC tumor tissues from the adjacent normal liver. Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. Our results provided new insights into HCC pathogenesis that might be valuable for HCC diagnosis and therapy.

Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.

The spleen and lymph nodes are important functional organs for human immune system. The identification of cell types for spleen and lymph nodes is helpful for understanding the mechanism of immune system. However, the cell types of spleen and lymph are highly diverse in the human body. Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. A total of 28,211 cell data (training vs. test = 14,435 vs. 13,776) involving 24 cell types were collected for this study. For the training dataset, it was analyzed by Boruta and minimum redundancy maximum relevance (mRMR) one by one, resulting in an mRMR feature list. This list was fed into the incremental feature selection (IFS) method, incorporating four classification algorithms (deep forest, random forest, K-nearest neighbor, and decision tree). Some essential features were discovered and the deep forest with its optimal features achieved the best performance. A group of related proteins (CD4, TCRb, CD103, CD43, and CD23) and genes (Nkg7 and Thy1) contributing to the classification of spleen and lymph nodes cell types were analyzed. Furthermore, the classification rules yielded by decision tree were also provided and analyzed. Above findings may provide helpful information for deepening our understanding on the diversity of cell types.

Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.

Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted treatment is meaningful. A computational workflow was designed in this study to identify key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection. First, the methylation features in the expression profile were deeply analyzed by a Monte Carlo feature selection method. A feature list was generated. Next, this ranked feature list was fed into the incremental feature selection method to determine the optimal features for different classification algorithms, thereby further building optimal classifiers. These selected key features were analyzed by functional enrichment to detect their biofunctional information. Furthermore, a set of rules were set up by a white-box algorithm, decision tree, to uncover different methylation patterns on various severity of SARS-CoV-2 infection. Some genes (PARP9, MX1, IRF7), corresponding to essential methylation sites, and rules were validated by published academic literature. Overall, this study contributes to revealing potential expression features and provides a reference for patient stratification. The physicians can prioritize and allocate health and medical resources for COVID-19 patients based on their predicted severe clinical outcomes.

Subcellular Localization Prediction of Human Proteins Using Multifeature Selection Methods.

Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were applied to build prediction models for human protein subcellular localizations. To build an effective predictive model, we employed a variety of robust machine learning algorithms, including Boruta feature selection, minimum redundancy maximum relevance, Monte Carlo feature selection, and LightGBM. Then, the incremental feature selection method with random forest and support vector machine was used to discover the essential features. Furthermore, 38 key features were determined by integrating results of different feature selection methods, which may provide critical insights into the subcellular location of proteins. Their biological functions of subcellular localizations were discussed according to recent publications. In summary, our computational framework can help advance the understanding of subcellular localization prediction techniques and provide a new perspective to investigate the patterns of protein subcellular localization and their biological importance.