AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

Valuable Insight ◽

Feature Subset ◽

Accurate Identification ◽

Test Set ◽

Mrna Stabilization

AbstractMotivationIdentification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules, is an important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, identifying RBPs directly from the sequence using computational methods can be useful to efficiently annotate RBPs and assist the experimental design. In this work, we present a method, called AIRBP, which is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by utilizing features extracted from evolutionary information, physiochemical properties, and disordered properties. Moreover, our method, AIRBP is trained on the useful feature-subset identified by the evolutionary algorithm (EA).ResultsThe results show that AIRBP attains Accuracy (ACC), F1-score, and MCC of 95.38%, 0.917, and 0.885, respectively, based on the benchmark dataset, using 10-fold cross-validation (CV). Further evaluation of AIRBP on independent test set reveals that it achieves ACC, F1-score, and MCC of 93.04%, 0.943, and 0.855, for Human test set; 91.60%, 0.942 and 0.789 for S. cerevisiae test set; and 91.67%, 0.953 and 0.594 for A. thaliana test set, respectively. These results indicate that AIRBP outperforms the current state-of-the-art method. Therefore, the proposed top-performing AIRBP can be useful for accurate identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat critical diseases.AvailabilityCode-data is available here: http://cs.uno.edu/~tamjid/Software/AIRBP/code_data.zip

Accurate identification of pavement materials that are susceptible to moisture damage with the use of advanced conditioning and test methods and the use of machine learning techniques

SN Applied Sciences ◽

10.1007/s42452-018-0086-8 ◽

2018 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

Ram Kumar Veeraragavan ◽

Nivedya M. K. ◽

Rajib B. Mallick

Keyword(s):

Machine Learning ◽

Moisture Damage ◽

Test Methods ◽

Accurate Identification ◽

Pavement Materials ◽

Learning Techniques

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

10.21203/rs.3.rs-15480/v4 ◽

2020 ◽

Author(s):

Hao Li ◽

Liqian Cui ◽

Liping Cao ◽

Yizhi Zhang ◽

Yueheng Liu ◽

...

Keyword(s):

Machine Learning ◽

Bipolar Disorder ◽

Functional Mri ◽

Added Value ◽

Support Vector ◽

Svm Classifier ◽

Accurate Identification ◽

Learning Techniques ◽

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

Epiblast morphogenesis is controlled by selective mRNA decay triggered by LIN28A relocation

10.1101/2021.03.15.433780 ◽

2021 ◽

Author(s):

Miha Modic ◽

Igor Ruiz de los Mozos ◽

Sebastian Steinhauser ◽

Emiel van Genderen ◽

Silvia Schirge ◽

...

Keyword(s):

Machine Learning ◽

Rna Sequencing ◽

Mrna Decay ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Rapid Decay ◽

Nuclear Retention ◽

Intrinsic Mechanism ◽

Mrna Binding

The embryonic progression from naïve to primed pluripotency is accompanied by the rapid decay of pluripotency-associated mRNAs and a concomitant radical morphogenetic sequence of epiblast polarization, rosette formation and lumenogenesis. The mechanisms triggering and linking these events remain poorly understood. Guided by machine learning and metabolic RNA sequencing, we identified RNA binding proteins (RBPs), especially LIN28A, as primary mRNA decay factors. Using mRNA-RBP interactome capture, we revealed a dramatic increase in LIN28A mRNA binding during the naïve-rosette-primed pluripotency transition, driven by its nucleolar-to-cytoplasmic translocation. Cytoplasmic LIN28A binds to 3′UTRs of pluripotency-associated mRNAs to directly stimulate their decay and drive lumenogenesis. Accordingly, forced nuclear retention of LIN28A impeded lumenogenesis, impaired gastrulation, and caused an unforeseen embryonic multiplication. Selective mRNA decay, driven by nucleo-cytoplasmic RBP translocation, therefore acts as an intrinsic mechanism linking cell identity switches to the control of embryonic growth and morphogenesis.

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

10.21203/rs.3.rs-15480/v3 ◽

2020 ◽

Author(s):

Hao Li ◽

Liqian Cui ◽

Liping Cao ◽

Yizhi Zhang ◽

Yueheng Liu ◽

...

Keyword(s):

Machine Learning ◽

Bipolar Disorder ◽

Functional Mri ◽

Added Value ◽

Support Vector ◽

Svm Classifier ◽

Accurate Identification ◽

Learning Techniques ◽

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Limitations: The sample size was small, and we were unable to eliminate the potential effects of medications. Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques

Applied Sciences ◽

10.3390/app10249019 ◽

2020 ◽

Vol 10 (24) ◽

pp. 9019

Author(s):

Alejandro Rodríguez-González ◽

Juan Manuel Tuñas ◽

Lucia Prieto Santamaría ◽

Diego Fernández Peces-Barba ◽

Ernestina Menasalvas Ruiz ◽

...

Keyword(s):

Machine Learning ◽

Unbalanced Data ◽

Correct Identification ◽

Imbalanced Dataset ◽

Accurate Identification ◽

Textual Information ◽

Meta Model ◽

Learning Techniques ◽

Data Problem

Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem.

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

BMC Psychiatry ◽

10.1186/s12888-020-02886-5 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Hao Li ◽

Liqian Cui ◽

Liping Cao ◽

Yizhi Zhang ◽

Yueheng Liu ◽

...

Keyword(s):

Machine Learning ◽

Bipolar Disorder ◽

Functional Mri ◽

Added Value ◽

Support Vector ◽

Svm Classifier ◽

Accurate Identification ◽

Learning Techniques ◽

Abstract Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022). Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

Articles selected by Faculty of 1000: proteomics of PH domains; machine-learning prediction of RNA-binding proteins; coated vesicle proteomics; classifying protein domain combinations; amino acid usage within proteomes

Genome Biology ◽

10.1186/gb-2004-5-4-321 ◽

2004 ◽

Vol 5 (4) ◽

Author(s):

Keyword(s):

Machine Learning ◽

Amino Acid ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Protein Domain ◽

Amino Acid Usage ◽

Coated Vesicle ◽

Ph Domains

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology

International Journal of Genomics ◽

10.1155/2016/7604641 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 10

Author(s):

Jieru Zhang ◽

Ying Ju ◽

Huijuan Lu ◽

Ping Xuan ◽

Quan Zou

Keyword(s):

Machine Learning ◽

Extraction Methods ◽

Support Vector ◽

Learning Technology ◽

Accurate Identification ◽

Learning Techniques ◽

Hybrid Machine ◽

Computational Identification ◽

Identification Techniques

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

10.21203/rs.3.rs-15480/v2 ◽

2020 ◽

Author(s):

Hao Li ◽

Liqian Cui ◽

Liping Cao ◽

Yizhi Zhang ◽

Yueheng Liu ◽

...

Keyword(s):

Machine Learning ◽

Bipolar Disorder ◽

Functional Mri ◽

Added Value ◽

Support Vector ◽

Svm Classifier ◽

Accurate Identification ◽

Learning Techniques ◽

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed for years. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5%, sensitivity of 86.4%, and specificity of 88.9% in the test data (p=0.0022).Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.