NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Jan. 16, 2025 Qingqing Zhu
GPTRadScore and CT-Bench: Advancing Multimodal AI Evaluation and Benchmarking in CT Imaging -
Jan. 17, 2025 Xuegong Zhang
Using Large Cellular Models to Understand Cell Transcriptomics Language -
Jan. 21, 2025 Qiao Jin
Artificial Intelligence for Evidence-based Medicine -
Jan. 28, 2025 Kaleb Abram
TBD
RECENT SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Dec. 17, 2024 Joey Thole
Training set associations drive AlphaFold initial predictions of fold-switching proteins -
Dec. 10, 2024 Amr Elsawy
AI for Age-Related Macular Degeneration on Optical Coherence Tomography -
Dec. 3, 2024 Sarvesh Soni
Toward Relieving Clinician Burden by Automatically Generating Progress Notes -
Nov. 19, 2024 Benjamin Lee
Reiterative Translation in Stop-Free Circular RNAs
Scheduled Seminars on May 30, 2023
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers.
To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors.
The results allow us to identify markers (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation.
We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA).
Many markers identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred markers are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation.
This implies that the markers are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of markers identified by TranNet will provide a valuable resource for future investigations.