NLM DIR Seminar Schedule
UPCOMING SEMINARS
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May 12, 2026 John Bridgers
A bi-partition function algorithm to evaluate inferred subclonal structures in single-cell sequencing data -
May 14, 2026 Brandon Colelough
TBD -
May 19, 2026 Leann Lindsey
Are Genomic Language Models Learning? Insights from Tokenization Analysis and Prophage Detection in Bacterial Genomes -
May 26, 2026 Harutyun Saakyan
TBD -
May 27, 2026 Brian Abraham
Cis-Regulatory Organization and Transcription Factor Control of Cell Identity and Disease
RECENT SEMINARS
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May 5, 2026 Benjamin Hou
Machine Learning for Craniofacial Malocclusion Prediction -
April 28, 2026 Niccolo Marini
From Unimodal Datasets to Multimodal Foundation Models: Synthetic Clinical Notes for Dermatology AI -
April 21, 2026 Yoshitaka Inoue
Drug Response Prediction: Generalization using Graph Neural Networks & Reasoning over Predictions using LLMs -
April 16, 2026 Matthew Diller
Analyzing Similarity in Common Data Elements in the NIH CDE Repository via Semantic Clustering -
April 7, 2026 Henry Secaira Morocho
Toward a systematic method of database enrichment for reference-based metagenomics
Scheduled Seminars on May 5, 2026
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Craniofacial malocclusion assessment is traditionally based on cephalometric analysis, in which anatomical landmarks are identified and used to characterize skeletal relationships between the cranial base, maxilla, and mandible. Although this process is clinically well established, manually annotating landmarks is time-consuming, observer-dependent, and difficult to scale in routine clinical practice. This is especially limiting for opportunistic screening, where it is unrealistic to expect clinicians to annotate landmarks for every patient. In this talk, I will discuss how machine learning, particularly deep learning for medical imaging, can support automated craniofacial analysis and malocclusion prediction from 3D imaging.