Ki Wook Lee

Poster Presentation in 2026 AACR (#3594)

Mon, 20 Apr 2026 09:00:00 +0000

🏆 Achievement & Recognition I am deeply honored to announce that I have been selected as a recipient of the AACR-Margaret Foti Foundation Scholar-in-Training Award for the AACR Annual Meeting 2026. This prestigious award recognizes highly meritorious abstracts submitted by early-career scientists.

Poster Presentation Details

Abstract Title: Expression-based immune-phenotyping ML model predict ICI response and long-term clinic benefit in lung adenocarcinoma
Abstract Number: #3594
Session: Integrative Computational Approaches 1
Date & Time: Monday, April 20, 2026 | 9:00 AM – 12:00 PM
Location: Poster Section 5 - Board Number 4, San Diego Convention Center

Research Overview In this study, I present a machine learning-based framework that leverages transcriptomic immune-phenotyping to predict responses to Immune Checkpoint Inhibitors (ICI). Our model not only predicts immediate response but also identifies patients likely to experience long-term clinical benefits, providing a robust tool for precision oncology in lung adenocarcinoma.

Acknowledgements This research and my participation in the AACR Annual Meeting 2026 are generously supported by:

AACR-Margaret Foti Foundation (Scholar-in-Training Award)
National Research Foundation of Korea (NRF)
Hyundai Motor Chung Mong-Koo Foundation (On-dream Future Industrial Talent Scholarship)

I look forward to engaging with fellow researchers in San Diego. Please feel free to stop by my poster to discuss bioinformatics, AI in oncology, or potential collaborations! ✈️🇺🇸

Selected Oral Presentation (Young Scientist Session)

Thu, 05 Feb 2026 09:00:00 +0000

🏆 Recognition

Young Scientist Presentation Award, The Korean Genome Organization (KOGO), Feb 2026.
Selected as an Oral Presenter for the Young Scientist Session.

**Presentation Title** > **Transcriptome-driven Immune Phenotyping AI Framework Predict ICI Response and Long-term Benefit in Lung Adenocarcinoma**

**Highlights** As a selected presenter, I presented my research at the KOGO Winter Symposium.

Objective: To improve the accuracy of predicting clinical responses to Immune Checkpoint Inhibitors (ICI).
Methodology: Integration of RNA-seq data preprocessing optimization with advanced deep learning models.
Key Finding: Established a robust immune phenotyping framework that significantly correlates with long-term therapeutic benefits in lung adenocarcinoma cohorts.

Oral Presentation in UKC 2025, Atlanta

Sat, 09 Aug 2025 08:30:00 +0000

Title
Transcriptomic AI-based classification of immune phenotypes for predicting immunotherapy response in lung cancer

Main
With full support from the Hyundai Motor Chung Mong-Koo Foundation, I will attend to UKC 2025 as a presenter at the Medical and Pharmaceutical Science symposium.
Additionally, to gain the opportunity to engage with senior experts and receive valuable advice for establishing myself as an independent researcher, I will participate in the SCIENTISTS AND ENGINEERS EARLY CAREER DEVELOPMENT (SEED) 2025 program.

💬 Oral Presentation on Minisymposium of The Genetics Society of Korea

Fri, 27 Jun 2025 00:00:00 +0000

For academic exchange between scholarship students of Hyundai Motor Chung Mong-Koo Foundation, I presented my research topic and publication experience.

Poster Presentation in 2025 AACR (#7263)

Wed, 30 Apr 2025 09:00:00 +0000

Background
Tumor immune microenvironment composition significantly influences immunotherapy outcomes in lung cancer, with immune-inflamed tumors demonstrating higher response rates compared to immune-non-inflamed tumors. This study aimed to develop a machine learning framework to classify immune phenotypes in lung cancer using TCGA data and validate its utility in predicting immunotherapy response with real-world patient samples. Existing approaches, such as the Lunit SCOPE technique, have been employed for immune phenotype characterization but face limitations in handling tumor heterogeneity and adapting to diverse datasets, underscoring the need for more robust tools.

Methods
A baseline machine learning model was constructed using six tree-based classifiers: Random Forest, Extremely Randomized Trees, AdaBoost, Gradient Boosting, XGBoost, and LightGBM. Feature importance scoring identified critical immune-related gene expression profiles, including markers from interferon signaling, T-cell activation, and cytokine regulation pathways. Model optimization incorporated 15 classifiers, leveraging advanced hyperparameter tuning and ensemble learning strategies to improve accuracy, Matthew’s correlation coefficient (MCC), and area under the ROC curve (AUROC). Classification training and validation were performed on 447 TCGA lung cancer samples, divided into 80% training and 20% validation subsets, stratified into immune-inflamed and immune-non-inflamed phenotypes based on bulk RNA-seq gene expression datasets.
The final model demonstrated robust adaptability to tumor heterogeneity and was independently tested on 87 patient samples labeled through the Lunit SCOPE framework, stratified into immunotherapy responders and non-responders. Correlation analysis assessed the relationship between immune phenotypes and therapeutic outcomes. The optimized model exceeded the performance of Lunit SCOPE, achieving an AUROC >0.90 in classifying immune-inflamed versus immune-non inflamed samples in the TCGA dataset. Testing on real-world patient samples further confirmed its ability to accurately predict immunotherapy response, supporting the hypothesis that immune-inflamed tumors derive greater benefit from treatment. This machine learning framework demonstrates superior accuracy and adaptability compared to existing techniques, offering significant potential to improve patient stratification, reduce ineffective treatments, and guide precision immunotherapy in lung cancer.

Conclusion
Future directions include integrating multi-omics datasets to further refine predictive capabilities and assess its generalizability across other cancer types.

Poster Presentation in 2025 AACR (#2427)

Mon, 28 Apr 2025 09:00:00 +0000

Background
Current research in medicine and biology predominantly focuses on applying artificial intelligence (AI) to analyze patient-specific imaging data, such as CT and MRI scans. However, the application of AI to next-generation sequencing (NGS) data, particularly transcriptomics, remains underutilized. RNA sequencing data is prone to biological and technical variability during generation, and the choice of error-correction and preprocessing methods can significantly influence downstream analyses. These challenges underscore the need for clear and systematic guidelines to optimize transcriptome data for AI-based analyses. In this study, we aim to identify optimal preprocessing strategies for developing AI models leveraging transcriptomic data.

Methods
We utilized RNA sequencing datasets from 25 independent cohorts, comprising over 5,800 patients with conditions such as lung adenocarcinoma, colorectal cancer, diabetes, and other diseases. A total of 18 combinatorial preprocessing pipelines were systematically assessed, encompassing 6 normalization methods (Raw, CPMTMM, RLE, UQ, RPKM, TPM) and 3 scaling methods (None, MinMax, Z-score). Over 20,000 transcripts were processed using each combination of normalization and scaling techniques. We then developed disease diagnosis models using 13 machine learning and deep learning algorithms. The performance of these models was evaluated to identify the most effective preprocessing strategies for transcriptomic data.

Conclusion
We identified algorithm-specific optimal preprocessing strategies to develop robust AI models utilizing transcriptomic data. Performance variations driven by normalization and scaling methods were observed for each algorithm, with these differences being particularly pronounced in datasets characterized by inherently lower model performance (difficult tasks). Our findings underscore the critical role of preprocessing in shaping model outcomes and provide a foundation for the development of tailored AI frameworks for disease diagnosis. This study offers a systematic approach to optimize transcriptomics-based predictive modeling, advancing the integration of AI into transcriptomic data analysis for clinical applications.

💬 Poster presentation on AACR 2025

Mon, 28 Apr 2025 00:00:00 +0000

During AACR 2025, I presented two posters about AI platforms utilizing whole transcriptomics (4/28 - 4/30).

👩🏼‍🏫 Teach 5 weeks course of functional transcriptomics

Fri, 18 Apr 2025 00:00:00 +0000

For undergraduate course, I taught a 5-week course on functional transcriptomics (3/14 - 4/18).

This course covered the fundamentals of R and Linux for bioinformatics, as well as RNA sequencing analysis and data visualization.

Class Syllabus

Week 1: Introduction to R
Week 2: Identification of Differentially Expressed Genes from RNA-seq
Week 3: Graphic Visualization & Application of R
Week 4: Data Mining & Preprocessing Using Linux
Week 5: Genome Alignment & Quantification of RNA-seq Data

💬 Poster presentation on Department BK21 Conference 2025

Wed, 26 Mar 2025 00:00:00 +0000

During Gene camp 2025, I presented two posters about AI platforms utilizing whole transcriptomics (3/26 - 3/28).

DOGpred: A Novel Deep Learning Framework for Accurate Identification of Human O-linked Threonine Glycosylation Sites

Sat, 15 Mar 2025 00:00:00 +0000

💬 Presentation on academic exchange program of Hyundai Motor Chung Mong-Koo Foundation

Thu, 13 Mar 2025 00:00:00 +0000

For academic exchange between scholarship students of Hyundai Motor Chung Mong-Koo Foundation, I presented my research topic and publication experience.

Projects

Sun, 19 May 2024 00:00:00 +0000

3-Fucosyllactose-mediated modulation of immune response against virus infection

Mon, 01 Jan 2024 00:00:00 +0000

MiR-29 and MiR-140 regulate TRAIL-induced drug tolerance in lung cancer

Mon, 01 Jan 2024 00:00:00 +0000

Experience

Tue, 24 Oct 2023 00:00:00 +0000

Learn JavaScript

Tue, 24 Oct 2023 00:00:00 +0000

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Learn Python

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
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👉 Click to view the solution

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Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

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{{< math >}}
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Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

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Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

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data = pd.read_csv("data.csv")
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TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization

Sat, 01 Jan 2022 00:00:00 +0000

Ki Wook Lee

Poster Presentation in 2026 AACR (#3594)

Selected Oral Presentation (Young Scientist Session)

Oral Presentation in UKC 2025, Atlanta

💬 Oral Presentation on Minisymposium of The Genetics Society of Korea

Poster Presentation in 2025 AACR (#7263)

Poster Presentation in 2025 AACR (#2427)

💬 Poster presentation on AACR 2025

👩🏼‍🏫 Teach 5 weeks course of functional transcriptomics

Class Syllabus

💬 Poster presentation on Department BK21 Conference 2025

DOGpred: A Novel Deep Learning Framework for Accurate Identification of Human O-linked Threonine Glycosylation Sites

💬 Presentation on academic exchange program of Hyundai Motor Chung Mong-Koo Foundation

Projects

3-Fucosyllactose-mediated modulation of immune response against virus infection

MiR-29 and MiR-140 regulate TRAIL-induced drug tolerance in lung cancer

Experience

Learn JavaScript

Video

Podcast

Test students

Math

Code

Inline Images

Did you find this page helpful? Consider sharing it 🙌

Learn Python

Video

Podcast

Test students

Math

Code

Inline Images

Did you find this page helpful? Consider sharing it 🙌

TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization