SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

Yu, Bin; Qiu, Wenying; Chen, Cheng; Ma, Anjun; Jiang, Jing; Zhou, Hongyan; Ma, Qin

doi:10.1093/bioinformatics/btz734

Cited by 147 publications

(69 citation statements)

References 35 publications

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“…Conversion of sequences into vector revealed good results in both groups used for analysis ( Table 1, 2 and Supplementary Table S2). In protein study, protein sequence converted into feature vectors showed good performance in cases of SVM and KNN (57)(58)(59)(60). RF00174, RF00059, RF00504, RF00522 predicted better than others with minority classes like RF01054, RF00634, RF00380 ( Table 1 and 2).…”

Section: Discussionmentioning

confidence: 99%

A Novel Riboswitch Classification based on Imbalanced Sequences achieved by Machine Learning

Beyene

Ling

Ristevski

et al. 2020

Preprint

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Riboswitch, a part of mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data.That is a circumstance in which the records of a dataset of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority class, that resulting with a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced dataset ranging from 4,826 instances (RF00174) to 39 (RF01051) instances. The dataset was divided into training and test set using new developed pipeline. From 5460 k-mers, 156 features were produced calculated based on CfsSubsetEval and BestFirst. Statistically tested result was significantly difference between balanced and imbalanced dataset (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch Beyene et al.2 motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying novel riboswitches. The scientific community can use python source code at https://github.com/Seasonsling/riboswitch, which can contribute to the process of developing software packages. Author SummaryMachine learning application has been used in many ways in bioinformatics and computational biology. Its use in riboswitch classification is still limited and existing attempt showed challenges due to imbalanced dataset. Algorithms classify dataset with majority and minority group, but they tend to ignore minority group and emphasize on majority class, consequential return a skewed classification We used new pipeline including SMOTE for balancing datasets that showed better classified riboswitch as well as improved performance of algorithms selected. Statistically significant difference observed between balanced and imbalanced in sensitivity, specificity, accuracy and F-score, this proved balanced dataset better for classification of riboswitch. Biological functions and motif search of k-mers in riboswitch families revealed their presence in interior loops, terminal loops and helices, some of the kmers were reported to be riboswitch motifs of aptamer domains and critical for metabolite binding. The pipeline can be used in machine learning and deep learning study in other domains of bioinformatics and computational biology suffering from imbalanced dataset. Finally, scientific community can use python source code, the work done and flow to develop packages.

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Section: Discussionmentioning

confidence: 99%

A Novel Riboswitch Classification based on Imbalanced Sequences achieved by Machine Learning

Beyene

Ling

Ristevski

et al. 2020

Preprint

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“…The XGboost algorithm is a widely used machine learning method that can build complex models and make accurate decisions when given adequate data [23]. The accuracy and high e ciency of XGBoost make it show excellent performance in clinical research, especially in the eld of vascular diseases [23][24][25]. The new prediction model can accurately predict the AIS risk in Chinese patients and all variables involved in the model can be simple and easily obtained on admission.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

Duan

Xue

Wang

et al. 2020

Preprint

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Background: Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of low-density lipoproteins (LDL) subclasses in the occurrence of AIS and develop a risk prediction model based on these characteristics to identify high-risk people.Methods: Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique.Results: Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd‐demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including total cholesterol (TC), Low density lipoprotein cholesterol (LDL‐C), and Triglyceride (TG). Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC=0.82±0.04).Conclusions: Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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“…Most importantly, a new prediction model including 12 variables were generated, which can help clinicians identify high-risk patients so that proper prevention measures can be taken to ease the potential burden and reduce suffering. (13,25) 18 (13,24) 0.318 LDL-3 (mg/dL) 7(2, 13) 3(1, 6) < 0.001 * LDL-4 (mg/dL) 0(0, 3) 0(0, 0) < 0.001 * LDL-5 (mg/dL) 0(0, 0) 0(0, 0) 0.032 * LDL-6 (mg/dL) 0(0, 0) 0(0, 0) 0.117 LDL-7 (mg/dL) 0(0, 0) 0(0, 0) 0.206 LDL, low density lipoprotein *p< 0.05 Table 3. Correlation analysis of LDL-C and its subclasses by Spearman correlation method…”

Section: Discussionmentioning

confidence: 99%

Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

Duan

Xue

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of LDL subclasses in the occurrence of AIS and develop a risk prediction model based on these characteristics to identify high-risk people. Methods: Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique. Results: Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd‐demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including TC, LDL‐C, and TG. Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC=0.82±0.04). Conclusions: Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

Cited by 147 publications

References 35 publications

A Novel Riboswitch Classification based on Imbalanced Sequences achieved by Machine Learning

A Novel Riboswitch Classification based on Imbalanced Sequences achieved by Machine Learning

Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

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