arXiv subjects

Seyoung Lee

Publications and source records attributed to Seyoung Lee.

3 recordsLinked to original sources

arXiv2026

Eigen for Statistical and Machine Learning Computing: A Lightweight C++ Tutorial with Python Bindings

This note provides a lightweight tutorial on using Eigen, a C++ template library for linear algebra, to implement statistical and machine learning algorithms. The emphasis is practical rather than methodological: we show how common matrix operations, decomposition-based solvers, and vectorized updates can be written in readable C++ and then connected to Python through pybind11. Two examples are used throughout the tutorial: kernel ridge regression and matrix factorization with stochastic gradient descent. The examples are intentionally small enough to be studied as code, but they contain many operations that appear in larger research software projects, including kernel matrix construction, regularized linear system solving, row-wise updates, and NumPy--Eigen data conversion. The goal is to provide a reproducible starting point for researchers who want to move from mathematical formulas to efficient C++ implementations while retaining a convenient Python workflow.

stat.CO↗

arXiv2024

Learning a Patent-Informed Biomedical Knowledge Graph Reveals Technological Potential of Drug Repositioning Candidates

Drug repositioning-a promising strategy for discovering new therapeutic uses for existing drugs-has been increasingly explored in the computational science literature using biomedical databases. However, the technological potential of drug repositioning candidates has often been overlooked. This study presents a novel protocol to comprehensively analyse various sources such as pharmaceutical patents and biomedical databases, and identify drug repositioning candidates with both technological potential and scientific evidence. To this end, first, we constructed a scientific biomedical knowledge graph (s-BKG) comprising relationships between drugs, diseases, and genes derived from biomedical databases. Our protocol involves identifying drugs that exhibit limited association with the target disease but are closely located in the s-BKG, as potential drug candidates. We constructed a patent-informed biomedical knowledge graph (p-BKG) by adding pharmaceutical patent information. Finally, we developed a graph embedding protocol to ascertain the structure of the p-BKG, thereby calculating the relevance scores of those candidates with target disease-related patents to evaluate their technological potential. Our case study on Alzheimer's disease demonstrates its efficacy and feasibility, while the quantitative outcomes and systematic methods are expected to bridge the gap between computational discoveries and successful market applications in drug repositioning research.

cs.AI↗

arXiv2019

Anatomy of the vertebral column lymphatic network in mice

Cranial lymphatic vessels (LVs) are involved in transport of fluids, macromolecules and CNS immune responses. Little information about spinal LVs is available, because these delicate structures are embedded within vertebral tissues and difficult to visualize using traditional histology. Here we reveal an extended vertebral column LV network using three-dimensional imaging of decalcified iDISCO-clarified spine segments. Spinal LVs are metameric circuits exiting along spinal nerve roots and connecting to lymph nodes and the thoracic duct. They navigate in the epidural space and the dura mater around the spinal cord, and associate with leukocytes, peripheral dorsal root and sympathetic ganglia. Spinal LVs are VEGF-C-dependent and remodel extensively after spinal cord injury. They constitute an extension to cranial circuits for meningeal fluids, but also a route for perineural fluids and a link with peripheral immune and nervous circuits. Vertebral column LVs may be potential targets to improve the maintenance and repair of 32 spinal tissues as well as gatekeepers of CNS immunity.

q-bio.TO↗