Doheon Lee

Bio: Doheon Lee is an academic researcher from KAIST. The author has contributed to research in topics: Fuzzy logic & Cancer. The author has an hindex of 40, co-authored 273 publications receiving 7536 citations. Previous affiliations of Doheon Lee include Chonnam National University & Ajou University.

Network-based classification of breast cancer metastasis.

Abstract: Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

Inferring pathway activity toward precise disease classification.

Abstract: The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.

Phytochromes promote seedling light responses by inhibiting four negatively-acting phytochrome-interacting factors

Abstract: PIF3 is a phytochrome-interacting basic helix–loop–helix transcription factor that negatively regulates light responses, including hypocotyl elongation, cotyledon opening, and hypocotyl negative gravitropism. However, the role of PIF3 in chlorophyll biosynthesis has not been clearly defined. Here, we show that PIF3 also negatively regulates chlorophyll biosynthesis by repressing biosynthetic genes in the dark. Consistent with the gene expression patterns, the etiolated pif3 mutant accumulated a higher amount of protochlorophyllide and was bleached severely when transferred into light. The photobleaching phenotype of pif3 could be suppressed by the gun5 mutation and mimicked by overexpression of GUN5. When 4 negative phytochrome-interacting protein genes (PIF1, PIF3, PIF4, and PIF5) were mutated, the resulting quadruple mutant seedlings displayed constitutive photomorphogenic phenotypes, including short hypocotyls, open cotyledons, and disrupted hypocotyl gravitropism in the dark. Microarray analysis further confirmed that the dark-grown quadruple mutant has a gene expression pattern similar to that of red light-grown WT. Together, our data indicate that 4 phytochrome-interacting proteins are required for skotomorphogenesis and phytochromes activate photomorphogenesis by inhibiting these factors.

Genome-Wide Analysis of Genes Targeted by PHYTOCHROME INTERACTING FACTOR 3-LIKE5 during Seed Germination in Arabidopsis

Abstract: PHYTOCHROME INTERACTING FACTOR 3-LIKE5 (PIL5) is a basic helix-loop-helix transcription factor that inhibits seed germination by regulating the expression of gibberellin (GA)- and abscisic acid (ABA)-related genes either directly or indirectly. It is not yet known, however, whether PIL5 regulates seed germination solely through GA and ABA. Here, we used Chromatin immunoprecipitation-chip (ChIP-chip) analysis to identify 748 novel PIL5 binding sites in the Arabidopsis thaliana genome. Consistent with the molecular function of PIL5 as a transcription regulator, most of the identified binding sites are located in gene promoter regions. Binding site analysis shows that PIL5 binds to its target sites mainly through the G-box motif in vivo. Microarray analysis reveals that phytochromes regulate a large number of genes mainly through PIL5 during seed germination. Comparison between the ChIP-chip and microarray data indicates that PIL5 regulates 166 genes by directly binding to their promoters. Many of the identified genes encode transcription regulators involved in hormone signaling, while some encode enzymes involved in cell wall modification. Interestingly, PIL5 directly regulates many transcription regulators of hormone signaling and indirectly regulates many genes involved in hormone metabolism. Taken together, our data indicate that PIL5 inhibits seed germination not just through GA and ABA, but also by coordinating hormone signals and modulating cell wall properties in imbibed seeds.

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

Abstract: Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.