scholarly journals SKIPHOS: non-kinase specific phosphorylation site prediction with random forests and amino acid skip-gram embeddings

2019 ◽  
Author(s):  
Thanh Hai Dang ◽  
Quang Thinh Trac ◽  
Huy Kinh Phan ◽  
Manh Cuong Nguyen ◽  
Quynh Trang Pham Thi
Keyword(s):  
Prediction Model ◽  
Random Forests ◽  
Protein Modification ◽  
Prediction Models ◽  
Phosphorylation Site ◽  
Supplementary Information ◽  
Phosphorylation Sites ◽  
Site Prediction ◽  
Link Type ◽  
Development And Aging

AbstractMotivationPhosphorylation, which is catalyzed by kinase proteins, is in the top two most common and widely studied types of known essential post-translation protein modification (PTM). Phosphorylation is known to regulate most cellular processes such as protein synthesis, cell division, signal transduction, cell growth, development and aging. Various phosphorylation site prediction models have been developed, which can be broadly categorized as being kinase-specific or non-kinase specific (general). Unlike the latter, the former requires a large enough number of experimentally known phosphorylation sites annotated with a given kinase for training the model, which is not the case in reality: less than 3% of the phosphorylation sites known to date have been annotated with a responsible kinase. To date, there are a few non-kinase specific phosphorylation site prediction models proposed.ResultsThis paper proposes SKIPHOS, a non-kinase specific phosphorylation site prediction model based on random forests on top of a continuous distributed representation of amino acids. Experimental results on the benchmark dataset and the independent test set demonstrate that SKIPHOS compares favorably to recent state-of-the-art related methods for three phosphorylation residues. Although being trained on phosphorylation sites in mamals, SKIPHOS can yield predictions for Y residues better than PHOSFER, a recently proposed plants-specific phosphorylation prediction model.Availability and ImplementationSKIPHOS Web Server is freely available for non-commercial use at http://fit.uet.vnu.edu.vn/SKIPHOS or http://112.137.130.46:[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals DeepPhos: prediction of protein phosphorylation sites with deep learning

2019 ◽  
Vol 35 (16) ◽  
pp. 2766-2773 ◽  
Author(s):  
Fenglin Luo ◽  
Minghui Wang ◽  
Yu Liu ◽  
Xing-Ming Zhao ◽  
Ao Li
Keyword(s):  
Deep Learning ◽  
Protein Phosphorylation ◽  
Phosphorylation Site ◽  
Supplementary Information ◽  
Phosphorylation Sites ◽  
Post Translational Modification ◽  
Neuron Network ◽  
Site Prediction ◽  
Specific Prediction ◽  
Group Family

Abstract Motivation Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites. Results In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction. Availability and implementation The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niraj Thapa ◽  
Meenal Chaudhari ◽  
Anthony A. Iannetta ◽  
Clarence White ◽  
Kaushik Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chlamydomonas Reinhardtii ◽  
Protein Phosphorylation ◽  
Short Term Memory ◽  
Phosphorylation Site ◽  
Specific Protein ◽  
Training Dataset ◽  
Phosphorylation Sites ◽  
Site Prediction ◽  
Model Combining

AbstractProtein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.

scholarly journals Mitigating Data Scarcity in Protein Binding Prediction Using Meta-Learning

2019 ◽  
Author(s):  
Yunan Luo ◽  
Jianzhu Ma ◽  
Xiaoming Zhao ◽  
Yufeng Su ◽  
Yang Liu ◽  
...  
Keyword(s):  
Protein Binding ◽  
Prediction Model ◽  
Prediction Models ◽  
Learning Phase ◽  
Phosphorylation Sites ◽  
Binding Prediction ◽  
Data Scarcity ◽  
Peptide Interactions ◽  
Meta Learning ◽  
Binding Peptides

AbstractA plethora of biological functions are performed through various types of protein-peptide binding. Prime examples include the protein kinase phosphorylation on peptide substrates and the binding of major histocompatibility complex to neoantigens in the immune system. Understanding the specificity of protein-peptide interactions is critical for unraveling the architectures of functional pathways and the mechanisms of cellular processes in human cells. Despite mass-spectrometric techniques were developed for the identification of protein-peptide interactions, our understanding of the preferences of proteins on their binding peptides is still rudimentary. As a complementary direction, a line of computational prediction methods has been recently proposed to predict protein-peptide bindings which efficiently provide rich functional annotations on a large scale. To achieve a high prediction accuracy, these computational methods require a sufficient amount of data to build the prediction model. However, the number of experimentally verified protein-peptide bindings is often limited in real cases. For example, a majority of protein kinases have very few experimentally verified phosphorylation sites (e.g., less than 30 sites) in existing databases. These methods are thus limited to building accurate prediction models for only well-characterized proteins with a large volume of known binding peptides and cannot be extended to predict new binding peptides for less-studied proteins. In this paper, we introduce a generic framework to address this issue of data scarcity in protein binding prediction. We demonstrate the applicability of our framework in predicting kinase-specific phosphorylation sites. Our method uses an effective training strategy to build a prediction model with robust transferability. The model is able to predict the phosphorylation sites of a less-studied kinase, even if there is only a small number of phosphorylation sites known for this kinase. To achieve this, we train the model via a meta-learning phase followed by a few-shot learning phase. We demonstrate our framework has better transferability than state-of-the-art methods and is effective in utilizing limited data to accurately predict phosphorylation sites for less-characterized kinases. The implementation of our framework is available at https://github.com/luoyunan/MetaKinase.

Chlamy-EnPhosSite: A deep learning-based approach for Chlamydomonas reinhardtii-specific phosphorylation site prediction

2021 ◽  
Author(s):  
Niraj Thapa ◽  
Meenal Chaudhari ◽  
Anthony A. Iannetta ◽  
Clarence White ◽  
Kaushik Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chlamydomonas Reinhardtii ◽  
Protein Phosphorylation ◽  
Short Term Memory ◽  
Phosphorylation Site ◽  
Specific Protein ◽  
Training Dataset ◽  
Phosphorylation Sites ◽  
Site Prediction ◽  
Novel Approach

Abstract Protein phosphorylation is one of the most important post-translational modifications (PTMs) and involved in myriad cellular processes. Although many non-organism-specific computational phosphorylation site prediction tools and a few tools for organism-specific phosphorylation site prediction exist, none are currently available for Chlamydomonas reinhardtii. Herein, we present a novel deep learning (DL) based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. Our novel approach called Chlamy-EnPhosSite (based on ensemble approach combining convolutional neural networks (CNN) and long short-term memory LSTM) produces AUC and MCC of 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a blinded study and approximately 90% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 77% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.

scholarly journals Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246640
Author(s):  
Tomohisa Seki ◽  
Yoshimasa Kawazoe ◽  
Kazuhiko Ohe
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Test Data ◽  
Random Forests ◽  
Prediction Models ◽  
Laboratory Data ◽  
Data Set ◽  
Prediction Capability

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient’s severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization.

scholarly journals Bioinformatics analysis and collection of protein post-translational modification sites in human viruses

2020 ◽  
Author(s):  
Yujia Xiang ◽  
Quan Zou ◽  
Lilin Zhao
Keyword(s):  
Viral Protein ◽  
Protein Modification ◽  
Phosphorylation Site ◽  
Protein Domains ◽  
Human Interaction ◽  
Phosphorylation Sites ◽  
Post Translational Modification ◽  
Post Translational Modifications ◽  
Human Proteins ◽  
Human Viruses

AbstractIn viruses, post-translational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for better understanding the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus-human interactions using comprehensive viral PTM datasets. To fill this gap, firstly, we developed a viral post-translational modification database (VPTMdb) for collecting systematic information of viral PTM data. The VPTMdb contains 912 PTM sites that integrate 414 experimental-confirmed PTM sites with 98 proteins in 45 human viruses manually extracted from 162 publications and 498 PTMs extracted from UniProtKB/Swiss-Prot. Secondly, we investigated the viral PTM sequence motifs, the function of target human proteins, and characteristics of PTM protein domains. The results showed that (i) viral PTMs have the consensus motifs with human proteins in phosphorylation, SUMOylation and N-glycosylation. (ii) The function of human proteins that targeted by viral PTM proteins are related to protein targeting, translation, and localization. (iii) Viral PTMs are more likely to be enriched in protein domains. The findings should make an important contribution to the field of virus-human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. Finally, an online web server was implemented for users to download viral protein PTM data and predict phosphorylation sites of interest.Author summaryPost-translational modifications (PTMs) plays an important role in the regulation of viral proteins; However, due to the limitation of data sets, there has been no detailed investigation of viral protein PTMs characteristics. In this manuscript, we collected experimentally verified viral protein post-translational modification sites and analysed viral PTMs data from a bioinformatics perspective. Besides, we constructed a novel feature-based machine learning model for predicting phosphorylation site. This is the first study to explore the roles of viral protein modification in virus infection using computational methods. The valuable viral protein PTM data resource will provide new insights into virus-host interaction.

scholarly journals RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hamid D. Ismail ◽  
Ahoi Jones ◽  
Jung H. Kim ◽  
Robert H. Newman ◽  
Dukka B. KC
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Phosphorylation Site ◽  
Structural Features ◽  
Phosphorylation Sites ◽  
Independent Dataset ◽  
Site Prediction ◽  
The Past ◽  
Primary Amino ◽  
Fold Cross Validation

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.

scholarly journals Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

BMJ ◽  
2020 ◽  
pp. m1328 ◽  
Author(s):  
Laure Wynants ◽  
Ben Van Calster ◽  
Gary S Collins ◽  
Richard D Riley ◽  
Georg Heinze ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
General Population ◽  
Prediction Model ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Link Type ◽  
Diagnostic Models

Abstract Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 14 217 titles were screened, and 107 studies describing 145 prediction models were included. The review identified four models for identifying people at risk in the general population; 91 diagnostic models for detecting covid-19 (60 were based on medical imaging, nine to diagnose disease severity); and 50 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequently reported predictors of diagnosis and prognosis of covid-19 are age, body temperature, lymphocyte count, and lung imaging features. Flu-like symptoms and neutrophil count are frequently predictive in diagnostic models, while comorbidities, sex, C reactive protein, and creatinine are frequent prognostic factors. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in prognostic models. All models were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and vague reporting. Most reports did not include any description of the study population or intended use of the models, and calibration of the model predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Hence, we do not recommend any of these reported prediction models for use in current practice. Immediate sharing of well documented individual participant data from covid-19 studies and collaboration are urgently needed to develop more rigorous prediction models, and validate promising ones. The predictors identified in included models should be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/ , registration https://osf.io/wy245 . Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 2 of the original article published on 7 April 2020 ( BMJ 2020;369:m1328), and previous updates can be found as data supplements ( https://www.bmj.com/content/369/bmj.m1328/related#datasupp ).

XGBLC: an improved survival prediction model based on XGBoost

2021 ◽  
Author(s):  
Baoshan Ma ◽  
Ge Yan ◽  
Bingjie Chai ◽  
Xiaoyu Hou
Keyword(s):  
Survival Analysis ◽  
Prediction Model ◽  
Prediction Models ◽  
Expression Profiles ◽  
Genomic Data ◽  
Supplementary Information ◽  
High Dimensional ◽  
Brier Score ◽  
Survival Prediction ◽  
Model Based

Abstract Motivation Survival analysis using gene expression profiles plays a crucial role in the interpretation of clinical research and assessment of disease therapy programs. Several prediction models have been developed to explore the relationship between patients’ covariates and survival. However, the high-dimensional genomic features limit the prediction performance of the survival model. Thus, an accurate and reliable prediction model is necessary for survival analysis using high-dimensional genomic data. Results In this study, we proposed an improved survival prediction model based on XGBoost framework called XGBLC, which used Lasso-Cox to enhance the ability to analyze high-dimensional genomic data. The novel first- and second-order gradient statistics of Lasso-Cox were defined to construct the loss function of XGBLC. We extensively tested our XGBLC algorithm on both simulated and real-world datasets, and estimated the performance of models with 5-fold cross-validation. Based on 20 cancer datasets from The Cancer Genome Atlas (TCGA), XGBLC outperforms five state-of-the-art survival methods in terms of C-index, Brier score and AUC. The results show that XGBLC still keeps good accuracy and robustness by comparing the performance on the simulated datasets with different scales. The developed prediction model would be beneficial for physicians to understand the effects of patient’s genomic characteristics on survival and make personalized treatment decisions. Availability and implementation The implementation of XGBLC algorithm based on R language is available at: https://github.com/lab319/XGBLC Supplementary information Supplementary data are available at Bioinformatics online.

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