2026-1학기 전산전자공학대학원 첫 번째 세미나 강의를 소개합니다. We are pleased to announce the commencement of the 2026 Spring Semester Seminar Series for the Graduate School of Computer Science and Electrical Engineering. This weekly forum serves as a cornerstone for academic exchange, bringing together students, faculty, and industry experts to explore the frontiers of modern technology.
강사(Speaker): 박현정 교수 (University of Pittsburgh)
제목(Title): Deep neural network learning biological condition information refines gene-expression-based cell subtypes
초록(Abstract): With the recent advent of single-cell level biological understanding, a growing interest is in identifying cell states or subtypes that are homogeneous in terms of gene expression and are also enriched in certain biological conditions, including disease samples versus normal samples (condition-specific cell subtype). Despite the importance of identifying condition-specific cell subtypes, existing methods have the following limitations: since they train models separately between gene expression and the biological condition information, (1) they do not consider potential interactions between them, and (2) the weights from both types of information are not properly controlled. Also, (3) they do not consider non-linear relationships in the gene expression and the biological condition. To address the limitations and accurately identify such condition-specific cell subtypes, we develop scDeepJointClust, the first method that jointly trains both types of information via a deep neural network. scDeepJointClust incorporates results from the power of state-of-the-art gene-expression-based clustering methods as an input, incorporating their sophistication and accuracy. We evaluated scDeepJointClust on both simulation data in diverse scenarios and biological data of different diseases (melanoma and non-small-cell lung cancer) and showed that scDeepJointClust outperforms existing methods in terms of sensitivity and specificity. scDeepJointClust exhibits significant promise in advancing our understanding of cellular states and their implications in complex biological systems.
본 세미나는 온라인으로 진행됩니다. 학부생, 대학원생, 교직원 누구나 참여 가능하니 많은 관심과 참여 바랍니다. This seminar will be conducted online. We welcome all undergraduate students, graduate students, faculty, and staff to join us. We look forward to your interest and active participation.
2026-1학기 전산전자공학대학원 첫 번째 세미나 강의를 소개합니다. We are pleased to announce the commencement of the 2026 Spring Semester Seminar Series for the Graduate School of Computer Science and Electrical Engineering. This weekly forum serves as a cornerstone for academic exchange, bringing together students, faculty, and industry experts to explore the frontiers of modern technology.
With the recent advent of single-cell level biological understanding, a growing interest is in identifying cell states or subtypes that are homogeneous in terms of gene expression and are also enriched in certain biological conditions, including disease samples versus normal samples (condition-specific cell subtype). Despite the importance of identifying condition-specific cell subtypes, existing methods have the following limitations: since they train models separately between gene expression and the biological condition information, (1) they do not consider potential interactions between them, and (2) the weights from both types of information are not properly controlled. Also, (3) they do not consider non-linear relationships in the gene expression and the biological condition.
To address the limitations and accurately identify such condition-specific cell subtypes, we develop scDeepJointClust, the first method that jointly trains both types of information via a deep neural network. scDeepJointClust incorporates results from the power of state-of-the-art gene-expression-based clustering methods as an input, incorporating their sophistication and accuracy. We evaluated scDeepJointClust on both simulation data in diverse scenarios and biological data of different diseases (melanoma and non-small-cell lung cancer) and showed that scDeepJointClust outperforms existing methods in terms of sensitivity and specificity. scDeepJointClust exhibits significant promise in advancing our understanding of cellular states and their implications in complex biological systems.
본 세미나는 온라인으로 진행됩니다. 학부생, 대학원생, 교직원 누구나 참여 가능하니 많은 관심과 참여 바랍니다. This seminar will be conducted online. We welcome all undergraduate students, graduate students, faculty, and staff to join us. We look forward to your interest and active participation.