Biology subjects

Ramsey, S. A.

Publications and source records attributed to Ramsey, S. A..

8 recordsLinked to original sources

bioRxiv2026

Validated Semantic Scoring Improves Filtering Generic Concepts from Knowledge-Graphs for Drug Repurposing Queries

When an ontology-based biomedical knowledge graph (a KG, containing information about genes, pathways, phenotypes, and molecular interactions) is analyzed using a computational reasoning system, the system can return unwanted generic concepts. Such generic concepts, in the context of a query about potential drug treatments for a disease, might include 'pharmaceutical preparations' or 'antibiotics'. Answers can be filtered based on hand-curated block-lists or ontology Information Content (IC), but to our knowledge, (1) neither filtering method has been validated against expert judgments of generality, and (2) IC is not available for the majority of nodes in the representative KG used in this work. In this study, three domain experts rated representative lists of KG concepts for generality. We built a semantic precision score (SPS) from a concept's name and description alone, using a biomedical language model (BioLORD). For the purpose of filtering generic answers from biomedical query results, we compared (using queries for 200 diseases) the SPS against a curated concept block-list and two IC thresholds. IC agreed moderately with the experts (Spearman's {rho} = 0.59), but correlations were negligible or negative within IC bands. The SPS was well-correlated with the experts ({rho} = 0.76) globally and within SPS bands, and remained informative on concepts without IC ({rho} = 0.59). Hard filtering of query results using the SPS removed 1,502 of 33,161 query answers, nearly three times the block-list's 513, while removing fewer of the query-specific DrugCentral indications. Hard filtering by IC removed either few answers or many approved drugs. We additionally compared the filtering methods' performance on an independent evaluation set of queries for 200 diseases. The SPS filter removed 5,135 of 91,203 answers compared with the block-list's 1,419 and lost none of 1,296 indications compared with 17. Further, SPS filtering removed approved drugs no more often from the half of DrugCentral not used in training than from the half used in training. Semantic meaning captures expert judgments of specificity more faithfully than ontology position, and semantic scoring can replace IC and manually maintained block-lists for filtering drug-repurposing results.

bioinformatics↗

bioRxiv2025

PloverDB: A high-performance platform for serving biomedical knowledge graphs as standards-compliant web APIs

Knowledge graphs are increasingly being used to integrate heterogeneous biomedical knowledge and data. General-purpose graph database management systems such as Neo4j are often used to host and search knowledge graphs, but such tools come with overhead and leave biomedical-specific standards compliance and reasoning to the user. Interoperability across biomedical knowledge bases and reasoning systems necessitates the use of standards such as those adopted by the Biomedical Data Translator consortium. We present PloverDB, a comprehensive software platform for hosting and efficiently serving biomedical knowledge graphs as standards-compliant web application programming interfaces. In addition to fundamental back-end knowledge reasoning tasks, PloverDB automatically handles entity resolution, exposure of standardized metadata and test data, and multiplexing of knowledge graphs, all in a single platform designed specifically for efficient query answering and ease of deployment. PloverDB increases data accessibility and utility by allowing data providers to quickly serve their biomedical knowledge graphs as standards-compliant web services. Availability and Implementation: PloverDBs source code and technical documentation are publicly available under an MIT License at github:RTXteam/PloverDB, archived on Zenodo at doi:10.5281/zenodo.15454600. Contactamykglen@gmail.com

bioinformatics↗

bioRxiv2024

Comparative Transcriptomes of Canine and Human Prostate Cancers Identify Mediators of Castration Resistance

Prostate cancer continues to be one of the most lethal cancers in men. While androgen-deprivation therapy is initially effective in treating prostate cancer, most cases of advanced prostate cancer eventually progress to castration-resistant prostate cancer (CRPC), which is incurable. Similarly, the most aggressive form of prostatic carcinoma occurs in dogs that have been castrated. To identify molecular similarities between canine prostate cancer and human CRPC, we performed a comparative analysis of gene expression profiles. Through this transcriptomic analysis, we found that prostatic carcinoma in castrated dogs demonstrate an androgen indifferent phenotype, characterized by low androgen receptor and neuroendocrine associated genes. Notably, we identified two genes, ISG15 and AZGP1 that were consistently up- and downregulated, respectively, in both canine prostatic carcinoma and human CRPC. Additionally, we identified several other genes, including GPX3, S100P, and IFITM1, that exhibited similar expression patterns in both species. Protein-protein interaction network analysis demonstrated that these 5 genes were part of a larger network of interferon-induced genes, suggesting that they may act together in signaling pathways that are disrupted in prostate cancer. Accordingly, our findings suggest that the interferon pathway may play a role in the development and progression of CRPC in both dogs and humans and chart a new therapeutic approach.

genetics↗

bioRxiv2022

ARAX: a graph-based modular reasoning tool for translational biomedicine

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSWith the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine--supporting that language--that federates semantically integrated knowledge-bases. ResultsWe introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface. ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the users query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning, and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAXs application and utility in specific disease contexts, we present several use-case examples. Availability and ImplementationThe source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web application programming interface (API) endpoint at arax.rtx.ai/api/arax/v1.3/ui/. Contactdmk333@psu.edu

bioinformatics↗

bioRxiv2021

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

BackgroundBiomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). ResultsTo create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building--and hosting a web API for querying--a biomedical knowledge graph that uses an Extract-Transform-Load (ETL) approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. ConclusionRTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

bioinformatics↗

bioRxiv2021

Genome-Scale Metabolic Model Accurately Predicts Fermentation of Glucose by Chromochloris zofingiensis

Algae have the potential to be sources of renewable fuels and chemicals. One particular strain, Chromochloris zofingiensis, is of interest due to the co-production of triacylglycerols (TAGs) and astaxanthin, a valuable nutraceutical. To aid in future engineering efforts, we have developed the first genome-scale metabolic model on C. zofingiensis, iChr1915. This model includes 1915 genes, 3413 metabolic reactions and 2652 metabolites. We performed detailed biomass composition analysis for three growth conditions: autotrophic, mixotrophic and heterotrophic and used this data to develop biomass formation equations for each growth condition. The completed model was then used to predict flux distributions for each growth condition; interestingly, for heterotrophic growth, the model predicts the excretion of fermentation products due to overflow metabolism. We confirmed this experimentally via metabolomics of spent medium and fermentation product assays. An in silico gene essentiality analysis was performed on this model, as well as a flux variability analysis to test the production capabilities of this organism. In this work, we present the first genome scale metabolic model of C. zofingiensis and demonstrate its use predicting metabolic activity in different growth conditions, setting up a foundation for future metabolic engineering studies in this organism.

systems biology↗

bioRxiv2021

Predicting chemotherapy response using a variational autoencoder approach

MotivationMultiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon adenocarcinoma, pancreatic adenocarcinoma, bladder carcinoma, sarcoma, and breast invasive carcinoma. ResultsWe found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumors gene expression profile leads to better area under the receiver operating characteristic curve (AUROC) classification performance than either the original gene expression profile or the PCA principal components of the gene expression profile, in four out of five cancer types that we tested. Availabilitygithub.com/ATHED/VAE_for_chemotherapy_drug_response_prediction Contactramseyst@oregonstate.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Voting-based integration algorithm improves causal network learning from interventional and observational data: an application to cell signaling network inference

In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a new method, "Learn and Vote," for inferring causal interactions from multi-experiment datasets. In our method, experiment-specific networks are learned from the data and then combined by weighted averaging to construct a consensus network. Through empirical studies on synthetic and real-world datasets, we found that for most of the larger-sized network datasets that we analyzed, our method is more accurate than state-of-the-art network inference approaches.

bioinformatics↗