Omicseq: a web-based search engine for exploring omics datasets

Sun, Xiaobo; Pittard, W. Stephen; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S.

doi:10.1093/nar/gkx258

Cited by 11 publications

(5 citation statements)

References 34 publications

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“…In addition, none of the tests developed to date are sufficiently fine-tuned to predict overall survival (OS). These limitations are attributable, at least in part, to the fact that no complete atlas of prognosis-related genes has been available, with only limited numbers of genes having been extensively investigated in this regard [14]. This situation highlights the need for unbiased comprehensive approaches to unveil and list all prognosis-related molecules, with a next generation of molecular profiles being anticipated as a result of the application of large-scale sequencing to tumour genomes and transcriptomes [[15], [16], [17]].…”

Section: Introductionmentioning

confidence: 99%

A 23 gene–based molecular prognostic score precisely predicts overall survival of breast cancer patients

Shimizu

Nakayama

2019

eBioMedicine

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Background Although many prognosis-predicting molecular scores for breast cancer have been developed, they are applicable to only limited disease subtypes. We aimed to develop a novel prognostic score that is applicable to a wider range of breast cancer patients. Methods We initially examined The Cancer Genome Atlas breast cancer cohort to identify potential prognosis-related genes. We then performed a meta-analysis of 36 international breast cancer cohorts to validate such genes. We trained artificial intelligence models (random forest and neural network) to predict prognosis precisely, and we finally validated our prediction with the log-rank test. Findings We identified a comprehensive list of 184 prognosis-related genes, most of which have been not extensively studied to date. We then established a universal molecular prognostic score (mPS) that relies on the expression status of only 23 of these genes. The mPS system is almost universally applicable to breast cancer patients (log-rank P < 0.05) in a manner independent of platform (microarray or RNA sequencing). Interpretation The mPS system is simple and cost-effective to apply and yet is able to reveal previously unrecognized heterogeneity among patient subpopulations in a platform-independent manner. The combination of mPS and clinical stage stratifies prognosis even more precisely and should prove of value for avoidance of overtreatment. In addition, the prognosis-related genes uncovered in this study are potential drug targets. Fund This work was supported by KAKENHI grants from the Ministry of Education, Culture, Sports, Science, and Technology of Japan to H.S. (19K20403) and to K.I·N (18H05215).

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Section: Introductionmentioning

confidence: 99%

A 23 gene–based molecular prognostic score precisely predicts overall survival of breast cancer patients

Shimizu

Nakayama

2019

eBioMedicine

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“…website description of the source database from which the datasets were derived) was helpful for providing additional general information about dataset topic. Similarly, it may be possible to incorporate meta-data about the probability of a match between the genes mentioned in the query by name, and the actual data content of the dataset, as proposed in reference ( 18 ). This match could be incorporated as a feature, together with the textual matches, into the general ranking models described in this paper.…”

Section: Methodsmentioning

confidence: 99%

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

2018

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The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

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“…In particular, data integration is fundamental to interconnect such large amounts of available data-heterogeneous both in the described information and in the representation formats; towards this goal, several solutions have been proposed in the last few years [7][8][9][10]. Along with the integration of data, it is essential to make available convenient and efficient instruments to analyze and describe such data by means of meaningful measures (examples are [11][12][13][14][15]).…”

Section: Introductionmentioning

confidence: 99%

Genomic data integration and user-defined sample-set extraction for population variant analysis

et al. 2022

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Background Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. Results Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. Conclusions The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed.

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Omicseq: a web-based search engine for exploring omics datasets

Cited by 11 publications

References 34 publications

A 23 gene–based molecular prognostic score precisely predicts overall survival of breast cancer patients

A 23 gene–based molecular prognostic score precisely predicts overall survival of breast cancer patients

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

Genomic data integration and user-defined sample-set extraction for population variant analysis

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