Predicting disease phenotypes based on the molecular networks with condition-responsive correlation.

Lee, Sejoon,Lee, Eunjung,Lee, Kwang H,Lee, Doheon Inderscience 2011 International journal of data mining and bioinform Vol.5 No.2

<P>Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.</P>

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

Lee, Sejoon,Lee, Kwang H,Song, Min,Lee, Doheon BioMed Central 2011 BMC bioinformatics Vol.12 No.suppl2

<P><B>Background</B></P><P>Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched.</P><P><B>Methods</B></P><P>We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone.</P><P><B>Results</B></P><P>The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner.</P><P><B>Conclusions</B></P><P>We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.</P>

Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis

Lee, Sunjae,Mardinoglu, Adil,Zhang, Cheng,Lee, Doheon,Nielsen, Jens Oxford University Press 2016 Nucleic acids research Vol.44 No.12

<P>Hepatocellular carcinoma (HCC) has a high mortality rate and early detection of HCC is crucial for the application of effective treatment strategies. HCC is typically caused by either viral hepatitis infection or by fatty liver disease. To diagnose and treat HCC it is necessary to elucidate the underlying molecular mechanisms. As a major cause for development of HCC is fatty liver disease, we here investigated anomalies in regulation of lipid metabolism in the liver. We applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of metabolism. Using transcriptomics data of HCC patients, we identified significant dysregulated expressions of lipid-regulated genes, across many different lipid metabolic pathways. Our findings, however, show that viral hepatitis causes HCC by a distinct mechanism, less likely involving lipid anomalies. Based on our analysis we suggest signaling hub genes governing overall catabolic or anabolic pathways, as novel drug targets for treatment of HCC that involves lipid anomalies.</P>

Protein comparison at the domain architecture level

Lee, Byungwook,Lee, Doheon BioMed Central 2009 BMC bioinformatics Vol.10 No.suppl15

<P><B>Background</B></P><P>The general method used to determine the function of newly discovered proteins is to transfer annotations from well-characterized homologous proteins. The process of selecting homologous proteins can largely be classified into sequence-based and domain-based approaches. Domain-based methods have several advantages for identifying distant homology and homology among proteins with multiple domains, as compared to sequence-based methods. However, these methods are challenged by large families defined by 'promiscuous' (or 'mobile') domains.</P><P><B>Results</B></P><P>Here we present a measure, called Weighed Domain Architecture Comparison (WDAC), of domain architecture similarity, which can be used to identify homolog of multidomain proteins. To distinguish these promiscuous domains from conventional protein domains, we assigned a weight score to Pfam domain extracted from RefSeq proteins, based on its abundance and versatility. To measure the similarity of two domain architectures, cosine similarity (a similarity measure used in information retrieval) is used. We combined sequence similarity with domain architecture comparisons to identify proteins belonging to the same domain architecture. Using human and nematode proteomes, we compared WDAC with an unweighted domain architecture method (DAC) to evaluate the effectiveness of domain weight scores. We found that WDAC is better at identifying homology among multidomain proteins.</P><P><B>Conclusion</B></P><P>Our analysis indicates that considering domain weight scores in domain architecture comparisons improves protein homology identification. We developed a web-based server to allow users to compare their proteins with protein domain architectures.</P>

Association analysis of the perturbation of interactions in biological pathways and anticancer drug activity

Lee, Junehawk,Lee, Doheon Elsevier 2016 Biochemical and biophysical research communication Vol.470 No.1

<P><B>Abstract</B></P> <P>Understanding how different genomic mutational landscapes in patients with cancer lead to different responses to anticancer drugs is an important challenge for realizing precision medicine for cancer. Many studies have analyzed the comprehensive anticancer drug-response profiles and genomic profiles of cancer cell lines to identify the relationship between the anticancer drug response and genomic alternations. However, few studies have focused on interpreting these profiles with a network perspective. In this work, we analyzed genomic alterations in cancer cell lines by considering which interactions in the signaling pathway were perturbed by mutations. With our interaction-centric approach, we identified novel interaction/drug response associations for two drugs (afatinib and ixabepilone) for which no gene-centric association could be found. When we compared the performance of classifiers for predicting the responses to 164 drugs, the classifiers trained with interaction-centric features outperformed the classifiers trained with gene-centric features, despite the smaller number of features (<I>p</I>-value = 2.0 × 10<SUP>−3</SUP>). By incorporating the interaction information from signaling pathways, we revealed associations between genomic alterations and drug responses that could be missed when using a gene-centric approach.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We analyzed genomic mutations in cancer cells using an interaction-centric approach. </LI> <LI> We identified perturbed interactions using domain–domain interaction data. </LI> <LI> We identified novel interaction/drug-response associations for 80 drugs. </LI> <LI> Drug response classifiers trained with interaction-centric features were superior. </LI> </UL> </P>

Analysis of AML genes in dysregulated molecular networks

Lee, Eunjung,Jung, Hyunchul,Radivojac, Predrag,Kim, Jong-Won,Lee, Doheon BioMed Central 2009 BMC bioinformatics Vol.10 No.suppl9

<P><B>Background</B></P><P>Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.</P><P><B>Results</B></P><P>Through the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation.</P><P><B>Conclusion</B></P><P>We showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.</P>

Density-Induced Support Vector Data Description

Lee, KiYoung,Kim, Dae-Won,Lee, Kwang H.,Lee, Doheon IEEE 2007 IEEE transactions on neural networks Vol.18 No.1

<P>The purpose of data description is to give a compact description of the target data that represents most of its characteristics. In a support vector data description (SVDD), the compact description of target data is given in a hyperspherical model, which is determined by a small portion of data called support vectors. Despite the usefulness of the conventional SVDD, however, it may not identify the optimal solution of target description especially when the support vectors do not have the overall characteristics of the target data. To address the issue in SVDD methodology, we propose a new SVDD by introducing new distance measurements based on the notion of a relative density degree for each data point in order to reflect the distribution of a given data set. Moreover, for a real application, we extend the proposed method for the protein localization prediction problem which is a multiclass and multilabel problem. Experiments with various real data sets show promising results</P>

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications

Lee, Byungwook,Kim, Taehyung,Kim, Seon-Kyu,Lee, Kwang H.,Lee, Doheon Oxford University Press 2007 Nucleic acids research Vol.35 No.suppl1

<P>With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene–patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene–patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at ; the information is updated bimonthly.</P>

Bio-Synergy Analysis with a Virtual Human System CODA

Doheon Lee 한국식품영양과학회 2016 한국식품영양과학회 학술대회발표집 Vol.2016 No.10

Accelerating Drug and Food Development with an AI-Based Virtual Human System CODA

Doheon Lee 한국식품영양과학회 2022 한국식품영양과학회 학술대회발표집 Vol.2022 No.10