Browsing by Author "Ratliff, Timothy"

Now showing 1 - 3 of 3
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    PAGER: constructing PAGs and new PAG-PAG relationships for network biology
    (Oxford University Press, 2015-06-15) Yue, Zongliang; Kshirsagar, Madhura M.; Nguyen, Thanh; Suphavilai, Chayaporn; Neylon, Michael T.; Zhu, Liugen; Ratliff, Timothy; Chen, Jake Yue; Department of Computer & Information Science, School of Science
    In this article, we described a new database framework to perform integrative "gene-set, network, and pathway analysis" (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene-gene relationships and two types of 3 174 323 PAG-PAG regulatory relationships-co-membership based and regulatory relationship based. To help users assess each PAG's biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG-PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.
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    "Super Gene Set" Causal Relationship Discovery from Functional Genomics Data
    (IEEE, 2018-11) Yue, Zongliang; Neylon, Michael T.; Nguyen, Thanh; Ratliff, Timothy; Chen, Jake Yue; BioHealth Informatics, School of Informatics and Computing
    In this article, we present a computational framework to identify "causal relationships" among super gene sets. For "causal relationships," we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to "pathways, annotated lists, and gene signatures," or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.
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    Targeted elastin-like polypeptide fusion protein for near-infrared imaging of human and canine urothelial carcinoma
    (Impact Journals, 2022-09-06) Aayush, Aayush; Darji, Saloni; Dhawan, Deepika; Enstrom, Alexander; Broman, Meaghan M.; Idrees, Muhammad T.; Kaimakliotis, Hristos; Ratliff, Timothy; Knapp, Deborah; Thompson, David; Pathology and Laboratory Medicine, School of Medicine
    Cystoscopic visualization of bladder cancer is an essential method for initial bladder cancer detection and diagnosis, transurethral resection, and monitoring for recurrence. We sought to develop a new intravesical imaging agent that is more specific and sensitive using a polypeptide based NIR (near-infrared) probe designed to detect cells bearing epidermal growth factor receptors (EGFR) that are overexpressed in 80% of urothelial carcinoma (UC) cases. The NIR imaging agent consisted of an elastin like polypeptide (ELP) fused with epidermal growth factor (EGF) and conjugated to Cy5.5 to give Cy5.5-N24-EGF as a NIR contrast agent. In addition to evaluation in human cells and tissues, the agent was tested in canine cell lines and tissue samples with naturally occurring invasive UC. Flow cytometry and confocal microscopy were used to test cell-associated fluorescence of the probe in T24 human UC cells, and in K9TCC-SH (high EGFR expression) and K9TCC-Original (low EGF expression) canine cell lines. The probe specifically engages these cells through EGFR within 15 min of incubation and reached saturation within a clinically relevant 1 h timeframe. Furthermore, ex vivo studies with resected canine and human bladder tissues showed minimal signal from normal adjacent tissue and significant NIR fluorescence labeling of tumor tissue, in good agreement with our in vitro findings. Differential expression of EGFR ex vivo was revealed by our probe and confirmed by anti-EGFR immunohistochemical staining. Taken together, our data suggests Cy5.5-ELP-EGF is a NIR probe with improved sensitivity and selectivity towards BC that shows excellent potential for clinical translation.