2,030 research outputs found
A Bayesian Hierarchical Model to Derive Novel Gene Networks from Gene Ontology Fingerprints
We developed a Bayesian hierarchical model to identify gene networks based on the similarity score generated from comparing the gene ontology fingerprints of gene pairs. Genes in this network were assumed to have similar biological functions that can be indicated by their ontology fingerprints. Our results indicate that different pathways show consistent score threshold that allow us to distinguish biological relevant gene—gene connections in the network
Using Ontology Fingerprints to evaluate genome-wide association study results
We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation
Statically Checking Web API Requests in JavaScript
Many JavaScript applications perform HTTP requests to web APIs, relying on
the request URL, HTTP method, and request data to be constructed correctly by
string operations. Traditional compile-time error checking, such as calling a
non-existent method in Java, are not available for checking whether such
requests comply with the requirements of a web API. In this paper, we propose
an approach to statically check web API requests in JavaScript. Our approach
first extracts a request's URL string, HTTP method, and the corresponding
request data using an inter-procedural string analysis, and then checks whether
the request conforms to given web API specifications. We evaluated our approach
by checking whether web API requests in JavaScript files mined from GitHub are
consistent or inconsistent with publicly available API specifications. From the
6575 requests in scope, our approach determined whether the request's URL and
HTTP method was consistent or inconsistent with web API specifications with a
precision of 96.0%. Our approach also correctly determined whether extracted
request data was consistent or inconsistent with the data requirements with a
precision of 87.9% for payload data and 99.9% for query data. In a systematic
analysis of the inconsistent cases, we found that many of them were due to
errors in the client code. The here proposed checker can be integrated with
code editors or with continuous integration tools to warn programmers about
code containing potentially erroneous requests.Comment: International Conference on Software Engineering, 201
Integration of the Gene Ontology into an object-oriented architecture
BACKGROUND: To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. RESULTS: Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). CONCLUSION: We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes
Why do Companies meet with the SEC Chair?
We examine whether meetings between the SEC Chair and public companies facilitate regulatory capture. Our analyses indicate that firms seek out meetings with the SEC Chair, as meetings are more likely to occur for politically active rather than industry leading firms, and meetings are more likely to occur during periods when the firm is under nonpublic investigation. In addition, we find that firms with meetings benefit from reduced monetary penalties, and that these reduced penalties are attributable, in part, to the favorable selection of the adjudication forum. These findings extend our understanding of how regulatory capture occurs at the SEC, and suggest that closed-door meetings between the SEC Chair and public companies may facilitate regulatory capture by providing a forum that helps firms negotiate for and obtain favorable regulatory outcomes
Opportunities in Software Engineering Research for Web API Consumption
Nowadays, invoking third party code increasingly involves calling web
services via their web APIs, as opposed to the more traditional scenario of
downloading a library and invoking the library's API. However, there are also
new challenges for developers calling these web APIs. In this paper, we
highlight a broad set of these challenges and argue for resulting opportunities
for software engineering research to support developers in consuming web APIs.
We outline two specific research threads in this context: (1) web API
specification curation, which enables us to know the signatures of web APIs,
and (2) static analysis that is capable of extracting URLs, HTTP methods etc.
of web API calls. Furthermore, we present new work on how we combine (1) and
(2) to provide IDE support for application developers consuming web APIs. As
web APIs are used broadly, research in supporting the consumption of web APIs
offers exciting opportunities.Comment: Erik Wittern and Annie Ying are both first author
Anticancer drug synergy prediction in understudied tissues using transfer learning
ocaa212Objective: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. Materials and Methods: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. Results: We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. Conclusions: Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.Peer reviewe
Genome3D: A Viewer-Model Framework for Integrating and Visualizing Multi-Scale Epigenomic Information within a Three-Dimensional Genome
Background New technologies are enabling the measurement of many types of genomic and epigenomic information at scales ranging from the atomic to nuclear. Much of this new data is increasingly structural in nature, and is often difficult to coordinate with other data sets. There is a legitimate need for integrating and visualizing these disparate data sets to reveal structural relationships not apparent when looking at these data in isolation.
Results We have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In order to integrate and visualize large volume of data, novel statistical and mathematical approaches have been developed to reduce the size of the data. To our knowledge, this is the first such tool developed that can visualize human genome in three-dimension. We describe here the major features of Genome3D and discuss our multi-scale data framework using a representative basic physical model. We then demonstrate many of the issues and benefits of multi-resolution data integration.
Conclusions Genome3D is a software visualization tool that explores a wide range of structural genomic and epigenetic data. Data from various sources of differing scales can be integrated within a hierarchical framework that is easily adapted to new developments concerning the structure of the physical genome. In addition, our tool has a simple annotation mechanism to incorporate non-structural information. Genome3D is unique is its ability to manipulate large amounts of multi-resolution data from diverse sources to uncover complex and new structural relationships within the genome
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