Random distributed logistic regression framework for predicting potential lncRNA‒disease association.
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ABSTRACT:
SUBMITTER: Sun Y
PROVIDER: S-EPMC8373264 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:There is more and more evidence that the mutation and dysregulation of long non-coding RNA (lncRNA) are associated with numerous diseases, including cancers. However, experimental methods to identify associations between lncRNAs and diseases are expensive and time-consuming. Effective computational approaches to identify disease-related lncRNAs are in high demand; and would benefit the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In light of some limitations of existing computational methods, we developed a global network random walk model for predicting lncRNA-disease associations (GrwLDA) to reveal the potential associations between lncRNAs and diseases. GrwLDA is a universal network-based method and does not require negative samples. This method can be applied to a disease with no known associated lncRNA (isolated disease) and to lncRNA with no known associated disease (novel lncRNA). The leave-one-out cross validation (LOOCV) method was implemented to evaluate the predicted performance of GrwLDA. As a result, GrwLDA obtained reliable AUCs of 0.9449, 0.8562, and 0.8374 for overall, novel lncRNA and isolated disease prediction, respectively, significantly outperforming previous methods. Case studies of colon, gastric, and kidney cancers were also implemented, and the top 5 disease-lncRNA associations were reported for each disease. Interestingly, 13 (out of the 15) associations were confirmed by literature mining.
| S-EPMC5622075 | biostudies-literature
Project description:BackgroundPrivacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced.MethodologyIn this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data.Experiments and resultsWe try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios.ConclusionLogistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.
| S-EPMC4101668 | biostudies-literature
Project description:Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA-disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA-disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA-disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA-disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA-disease associations.
| S-EPMC8417042 | biostudies-literature
Project description:We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications.
| S-EPMC3676314 | biostudies-literature
Project description:Background and goalThe Random Forest (RF) algorithm for regression and classification has considerably gained popularity since its introduction in 2001. Meanwhile, it has grown to a standard classification approach competing with logistic regression in many innovation-friendly scientific fields.ResultsIn this context, we present a large scale benchmarking experiment based on 243 real datasets comparing the prediction performance of the original version of RF with default parameters and LR as binary classification tools. Most importantly, the design of our benchmark experiment is inspired from clinical trial methodology, thus avoiding common pitfalls and major sources of biases.ConclusionRF performed better than LR according to the considered accuracy measured in approximately 69% of the datasets. The mean difference between RF and LR was 0.029 (95%-CI =[0.022,0.038]) for the accuracy, 0.041 (95%-CI =[0.031,0.053]) for the Area Under the Curve, and -?0.027 (95%-CI =[-0.034,-0.021]) for the Brier score, all measures thus suggesting a significantly better performance of RF. As a side-result of our benchmarking experiment, we observed that the results were noticeably dependent on the inclusion criteria used to select the example datasets, thus emphasizing the importance of clear statements regarding this dataset selection process. We also stress that neutral studies similar to ours, based on a high number of datasets and carefully designed, will be necessary in the future to evaluate further variants, implementations or parameters of random forests which may yield improved accuracy compared to the original version with default values.
| S-EPMC6050737 | biostudies-literature
Project description:BackgroundAccumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources.ResultsTo improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models.ConclusionsCross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.
| S-EPMC7099795 | biostudies-literature
Project description:BackgroundMixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved in 2016 that this method is inappropriate in some situations and proposed GMMAT, a score test for the mixed logistic regression (MLR). However, this test does not produces an estimation of the variants' effects. We propose two computationally efficient methods to estimate the variants' effects. Their properties and those of other methods (MLM, logistic regression) are evaluated using both simulated and real genomic data from a recent GWAS in two geographically close population in West Africa.ResultsWe show that, when the disease prevalence differs between population strata, MLM is inappropriate to analyze binary traits. MLR performs the best in all circumstances. The variants' effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p values inflation or deflation when population strata are not clearly identified in the sample.ConclusionThe two proposed methods are implemented in the R package milorGWAS available on the CRAN. Both methods scale up to at least 10,000 individuals. The same computational strategies could be applied to other models (e.g. mixed Cox model for survival analysis).
| S-EPMC7684894 | biostudies-literature
Project description:BackgroundIn genome-wide association studies, it is widely accepted that multilocus methods are more powerful than testing single-nucleotide polymorphisms (SNPs) one at a time. Among statistical approaches considering many predictors simultaneously, scan statistics are an effective tool for detecting susceptibility genomic regions and mapping disease genes. In this study, inspired by the idea of scan statistics, we propose a novel sliding window-based method for identifying a parsimonious subset of contiguous SNPs that best predict disease status.ResultsWithin each sliding window, we apply a forward model selection procedure using generalized ridge logistic regression for model fitness in each step. In power simulations, we compare the performance of our method with that of five other methods in current use. Averaging power over all the conditions considered, our method dominates the others. We also present two published datasets where our method is useful in causal SNP identification.ConclusionsOur method can automatically combine genetic information in local genomic regions and allow for linkage disequilibrium between SNPs. It can overcome some defects of the scan statistics approach and will be very promising in genome-wide case-control association studies.
| S-EPMC3224109 | biostudies-literature
Project description:BackgroundThere is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak.ObjectiveTo develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees.MethodsWe developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets.ResultsThe computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy.ConclusionThe proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.
| S-EPMC3628043 | biostudies-literature
Project description:In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.
| S-EPMC5295400 | biostudies-literature