GAIL: An interactive webserver for inference and dynamic visualization of gene-gene associations based on gene ontology guided mining of biomedical literature

oppOntology: a MATLAB Toolbox for Enrichment Analysis

Biologists often use systems of ontologies to classify gene lists obtained by high-throughput gene or protein-sequencing instruments, and then enrichment scores were used to rank the ontology system. Therefore, the important molecular functional categories related to the phenotype can be conveniently viewed in the ontology system. Since the birth of GO (Gene Ontology) organization, various types of ontology software have been developed to calculate enrichment scores for the target gene list in the GO system. Herein, we provide an enrichment calculation application oppOntology (Omics Pilot Platform for Ontology) developed by MATLAB. oppOntology supports simultaneous calculation of multiple samples with manifold enrichment scores (GeneCount, GeneRatio, EnrichFactor, HypergeometricTest, and FisherExactTest). oppOntology can not only calculate enrichment scores for generic functional databases, such as GO, KEGG, HPO, and MsigDB, but also for self-defined functional category databases and customized GO Slim. Moreover, oppOntology supports online mapping of KEGG pathway diagrams in a batch way. The GUI (Graphical User Interface) of oppOntology is developed on the architecture of AppDesigner in MATLAB, and all input and output files are Microsoft Excel. oppOntology is an independent, easy-to-use enrichment calculation software, that can be available at https://github.com/HangZhouSheep/oppOntology .

Identification and validation of a prognostic signature and combination drug therapy for immunotherapy of head and neck squamous cell carcinoma

Immunotherapy has become a promising therapeutic option for Head and neck squamous cell carcinoma (HNSC). However, only a small percentage of patients could benefit from it, and the overall prognosis was far from satisfactory. In this study, by comprehensively computational analyses of hundreds of HNSC samples, a prognostic signature composed of 13 immune-related genes (IRGs) was constructed. The results of the analyses in multiple datasets indicated that our signature had high predictive accuracy and could serve as an independent prognostic predictor. Based on this signature and multiple clinical variables, we also established a prognostic nomogram to quantitatively predict the survival risk of individual patients. Moreover, this signature could accurately predict survival, reflect the immune microenvironment, and predict immunotherapy efficacy among HNSC patients. Two potential drugs (doxorubicin and daunorubicin) were also identified via Connectivity Map and molecular docking, which could be used for HNSC combination therapy. Taken together, we developed and validated a robust IRG-based prognostic signature to monitor the prognosis of HNSC, which could provide a solid foundation for individualized cancer immunotherapy.

Sparse Linear Discriminant Analysis using the Prior-Knowledge-Guided Block Covariance Matrix

There are two key challenges when using a linear discriminant analysis in the high-dimensional setting, including singularity of the covariance matrix and difficulty of interpreting the resulting classifier. Although several methods have been proposed to address these problems, they focused only on identifying a parsimonious set of variables maximizing classification accuracy. However, most methods did not consider dependency between variables and efficacy of selected variables appropriately. To address these limitations, here we propose a new approach that directly estimates the sparse discriminant vector without a need of estimating the whole inverse covariance matrix, by formulating a quadratic optimization problem. Furthermore, this approach also allows to integrate external information to guide the structure of covariance matrix. We evaluated the proposed model with simulation studies. We then applied it to the transcriptomic study that aims to identify genomic markers predictive of the response to cancer immunotherapy, where the covariance matrix was constructed based on the prior knowledge available in the pathway database.

PALMER: improving pathway annotation based on the biomedical literature mining with a constrained latent block model

Background

In systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases.

Results

In order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene–gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge.

Conclusions

We showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene–gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.