Method For Identification Of Biomarkers Research Articles

WSGMB: weight signed graph neural network for microbial biomarker identification.

The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework's identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn's disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.

IoSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets.

Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary information but suffers from an increased multiple-testing burden, data diversity inherent with different omics features, high-dimensionality, and so forth. Most of the available methods address subtype classification using dimension-reduction techniques to circumvent the sample size issue but interacting multiomics biomarker identification methods are unavailable. We propose a two-step model that first investigates phenotype-omics association using logistic regression. Then, selects disease-associated omics using sparse principal components which explores the interrelationship of multiple variables from two omics in a multivariate multiple regression framework. On the basis of this model, we developed a multiomics biomarker identification algorithm, interacting omics search (ioSearch), that jointly tests the effect of multiple omics with disease and between-omics associations by using pathway information that subsequently reduces the multiple-testing burden. Further, inference in terms of p values potentially makes it an easily interpretable biomarker identification tool. Extensive simulation demonstrates ioSearch as statistically powerful with a controlled Type-I error rate. Its application to publicly available breast cancer data sets identified relevant omics features in important pathways.

Disease biomarker identification based on sample network optimization

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

Impact of Gene Biomarker Discovery Tools Based on Protein-Protein Interaction and Machine Learning on Performance of Artificial Intelligence Models in Predicting Clinical Stages of Breast Cancer.

Breast cancer, as one of the most common diseases threatening the women's life, has attracted serious attention of the clinical and biomedical researchers worldwide. The genome-based studies along with their registered GEO datasets are frequent in the literature. Since several methodologies have been developed for analyzing and identifying gene biomarkers, it is necessary to evaluate their robustness. In this study, three well-known biomarker identification methods (i.e., ClusterOne, MCODE, and BioDiscML) were employed in order to identify the potential biomarkers. Then, the methods were ranked and evaluated using nonlinear classification models developed based on the identified sets of biomarkers. A combined BC microarray dataset consisting of GSE124647, GSE124646, and GSE15852 was used as training set, and two test datasets, GSE15852 and GSE25066, were used for the performance measurement of the trained models. The validation of the proposed models was carried out internally (leave-one-out, fivefold and tenfold cross-validation, random sampling, test on training set) and externally (test on test set). The results showed that ClusterOne, MCODE, and BioDiscML tools ranked first, second, and third, respectively, based on the area under the curve (AUC), accuracy, F1 score, precision, and recall metrics. Overall, it can be concluded that the descriptive values of gene biomarkers in terms of their biological aspects that have been determined by a given methodology and the predictive power of the models developed based on the identified gene biomarkers should be considered simultaneously while validating the biomarker identification approaches.

Network-based logistic regression integration method for biomarker identification

BackgroundMany mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets.MethodsIn this paper, we developed an integrative method for classification based on logistic regression. Different constant terms are set in the logistic regression model to measure the heterogeneity of the samples. By minimizing the differences of the constant terms within the same dataset, both the homogeneity within the same dataset and the heterogeneity in multiple datasets can be kept. The model is formulated as an optimization problem with a network penalty measuring the differences of the constant terms. The L1 penalty, elastic penalty and network related penalties are added to the objective function for the biomarker discovery purpose. Algorithms based on proximal Newton method are proposed to solve the optimization problem.ResultsWe first applied the proposed method to the simulated datasets. Both the AUC of the prediction and the biomarker identification accuracy are improved. We then applied the method to two breast cancer gene expression datasets. By integrating both datasets, the prediction AUC is improved over directly merging the datasets and MetaLasso. And it’s comparable to the best AUC when doing biomarker identification in an individual dataset. The identified biomarkers using network related penalty for variables were further analyzed. Meaningful subnetworks enriched by breast cancer were identified.ConclusionA network-based integrative logistic regression model is proposed in the paper. It improves both the prediction and biomarker identification accuracy.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

Epithelial mesenchymal transition (EMT) process has been shown as highly relevant to cancer prognosis. However, although different biological network-based biomarker identification methods have been proposed to predict cancer prognosis, EMT network has not been directly used for this purpose. In this study, we constructed an EMT regulatory network consisting of 87 molecules and tried to select features that are useful for prognosis prediction in Lung Adenocarcinoma (LUAD). To incorporate multiple molecular profiles, we obtained four types of molecular data including mRNA-Seq, copy number alteration (CNA), DNA methylation, and miRNA-Seq data from The Cancer Genome Atlas. The data were mapped to the EMT network in three alternative ways: mRNA-Seq and miRNA-Seq, DNA methylation, and CNA and miRNA-Seq. Each mapping was employed to extract five different sets of features using discretization and network-based biomarker identification methods. Each feature set was then used to predict prognosis with SVM and logistic regression classifiers. We measured the prediction accuracy with AUC and AUPR values using 10 times 10-fold cross validation. For a more comprehensive evaluation, we also measured the prediction accuracies of clinical features, EMT plus clinical features, randomly picked 87 molecules from each data mapping, and using all molecules from each data type. Counter-intuitively, EMT features do not always outperform randomly selected features and the prediction accuracies of the five feature sets are mostly not significantly different. Clinical features are shown to give the highest prediction accuracies. In addition, the prediction accuracies of both EMT features and random features are comparable as using all features (more than 17,000) from each data type.

MOLECULAR STRATIFICATION OF LUNG ADENOCARCINOMA USING GENE EXPRESSION

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe bioinformatics method for identification of biomarkers in lung adenocarcinoma using unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) samples, based on gene-expression profiles, recaptured the three molecular subgroups but switched the subtype for 14% of the samples, resulting in significant (P = 0.000344) survival differences among the refined subgroups. Our results were able to define three subgroups of lung adennocarcinoma that differed not only in gene expression patterns, but also in clinical properties, including patient survival.

Measuring similarity and improving stability in biomarker identification methods applied to Fourier‐transform infrared (FTIR) spectroscopy

FTIR spectroscopy is a powerful diagnostic tool that can also derive biochemical signatures of a wide range of cellular materials, such as cytology, histology, live cells, and biofluids. However, while classification is a well-established subject, biomarker identification lacks standards and validation of its methods. Validation of biomarker identification methods is difficult because, unlike classification, there is usually no reference biomarker against which to test the biomarkers extracted by a method. In this paper, we propose a framework to assess and improve the stability of biomarkers derived by a method, and to compare biomarkers derived by different method set-ups and between different methods by means of a proposed "biomarkers similarity index".

Biomarkers for pancreatic cancer: recent achievements in proteomics and genomics through classical and multivariate statistical methods.

Pancreatic cancer (PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies: (1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and (2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques (proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.

EllipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions

Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN.

Stability-based biomarker selection

Biomarker identification, i.e., finding those variables that indicate true differences between two or more populations, is an ever more important topic in the omics sciences. In most cases, the number of variables far exceeds the number of samples, making biomarker identification extremely difficult. We present a strategy based on the stability of putative biomarkers under perturbation of the data, and show that in several cases important gains can be achieved. The strategy is very general and can be applied with all common biomarker identification methods; it also has the advantage that it does not rely on error estimates from crossvalidation, that in this setting tend to be highly variable.

Biomarkers of aging in Drosophila.

Low environmental temperature and dietary restriction (DR) extend lifespan in diverse organisms. In the fruit fly Drosophila, switching flies between temperatures alters the rate at which mortality subsequently increases with age but does not reverse mortality rate. In contrast, DR acts acutely to lower mortality risk; flies switched between control feeding and DR show a rapid reversal of mortality rate. Dietary restriction thus does not slow accumulation of aging-related damage. Molecular species that track the effects of temperatures on mortality but are unaltered with switches in diet are therefore potential biomarkers of aging-related damage. However, molecular species that switch upon instigation or withdrawal of DR are thus potential biomarkers of mechanisms underlying risk of mortality, but not of aging-related damage. Using this approach, we assessed several commonly used biomarkers of aging-related damage. Accumulation of fluorescent advanced glycation end products (AGEs) correlated strongly with mortality rate of flies at different temperatures but was independent of diet. Hence, fluorescent AGEs are biomarkers of aging-related damage in flies. In contrast, five oxidized and glycated protein adducts accumulated with age, but were reversible with both temperature and diet, and are therefore not markers either of acute risk of dying or of aging-related damage. Our approach provides a powerful method for identification of biomarkers of aging.