|
Date
Members |
| 24 | 강성훈 | Review of LLM-Assisted OMOP CDM(Common Data Model) ETL process |
| 10 | 유준기 | muscat: tool for subpopulation-specific snRNA-seq analysis |
| 13 | 유준기 | OPLL research: figures and tables | |
| 27 | 강성훈 | Paper2Agent: Reimagining research papers as interactive and reliable AI agents |
| 15 | 문성희 | WES data analysis Discussion | |
| 22 | 강성훈 | MedAgentBench: Benchmark for medical LLM agents |
| 13 | 유준기 | CellChat for cell-cell communication analysis: comparative analysis | |
| 20 | 문성희 | Clinical data application in deep learning model | |
| 27 | 강성훈 | DNABERT replication |
| 02 | 윤미선 | Knowledge Structure of Pharmacogenomics | |
| 09 | 유준기 | CellChat for cell-cell communication analysis: preprocessing and visualization | |
| 14 | LMM process | ||
| 30 | 권호식 | Ph.D. Defense Process |
| 04 | 유준기 | CS-CORE: Cell type specific co-expression inference in scRNA-seq data | |
| 18 | 권호식 | R packages for enrichment analysis | |
| 25 | 차재현 | CPIC guideline |
| 14 | 문성희 | Pyhealth: Overview and examples | |
| 14 | 권호식 | UK Biobank Biomarker Panel | |
| 21 | 차재현 | Candidate genes evaluation stategies | |
| 28 | 윤미선 | Processing alphanumeric words(Introduction) |
| 19 | 윤미선 | neo4j tutorial (knowledge graph) | |
| 26 | 유준기 | sc2GWAS: A comprehensive platform linking single cell and GWAS traits of human |
| 05 | 유준기 | OPLL analysis result | |
| 19 | 문성희 | NHANES recent update | |
| 26 | 권호식 | Database commons |
| 08 | 최선 | llm experience | |
| 15 | 차재현 | Review) Candidate biomarkers in psychiatric disorders: state of the field | |
| 22 | 윤미선 | Knowledge graph |
| 03 | 최선 | test | |
| 11 | 유준기 | OPLL analysis | |
| 18 | 권호식 | InterVar | |
| 24 | 최선 | test2 |
| 06 | 권호식 | Databases for journal lists | |
| 27 | 차재현 | Preprocessing issue |
| 16 | 차재현 | GWAS of escitalopram treatment outcomes in patients with MDD | |
| 23 | 윤미선 | Instruction fine tuning | |
| 30 | 유준기 | The complex genetic architecture of Alzheimer disease novel insights and future directions |
| 04 | 윤미선 | (In review) Pharmacogenomic Profiling of the South Korean Population: Insights and Implications for Personalized Medicine | |
| 11 | 유준기 | AD knowledge portal data download | |
| 25 | 권호식 | Nonlinear model |
| 07 | 권호식 | [Review] Machine Learning to Advance Human Genome-Wide Association Studies | |
| 14 | 최선 | Documents loaders for LLM | |
| 21 | 차재현 | Causal Gene discovery in mendelian disorders |
| 05 | 최선 | Review)Identifcation of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis | |
| 12 | 차재현 | Review) Tumor mutational and indel burden: a systematic pan-cancer evaluation as prognostic biomarkers | |
| 19 | 윤미선 | LLaMA: Open and Efficient Foundation Language Models | |
| 26 | 유준기 | Polygenic resilience scores capture protective effects for Alzheimer disease |
| 01 | 차재현 | 16S rRNA : OTU vs ASV | |
| 21 | 윤미선 | A list of Large Language Model | |
| 21 | 윤미선 | A list of Large Language Model | |
| 29 | 권호식 | dbGaP Data Download |
| 03 | 최선 | gene | |
| 03 | 유준기 | ssGSEA: single sample Gene Set Enrichment Analysis | |
| 17 | 권호식 | dbGaP Data Access |
| 06 | 권호식 | SNU OA Ұ | |
| 20 | 차재현 | single cell analysis | |
| 27 | 윤미선 | 3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints | |
| 1 | 최선 | UKB processing |
| 07 | 차재현 | analyzing TCGA variants | |
| 21 | 윤미선 | An Evaluation of large language models in bioinformatics research | |
| 28 | 유준기 | CoxPH model for cross-sectional genomic data |
| 17 | 배소정 | Heritability Estimation Approaches Utilizing Genome-Wide Data | |
| 24 | 권호식 | Example data and vignette in R package | |
| 31 | 최선 | multiplex network | |
| 06 | 차재현 | Review: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition | |
| 06 | 차재현 | TCGA_study | |
| 27 | 유준기 | External DBs for filtering Synthetic Rescue Pairs |
| 08 | 유준기 | Idea for identifying synthetic rescue pair | |
| 15 | 배소정 | Post-GWAS analysis | |
| 22 | 권호식 | R packages to develop R package |
| 06 | 유준기 | TCGA PanCanAtlas data | |
| 13 | 배소정 | Phylogenetic analysis | |
| 20 | 권호식 | (Review) Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods |
| 02 | 배소정 | One-stage object detectors | |
| 16 | 권호식 | AnnotSV: Annotation of Human Structural Variations | |
| 23 | 차재현 | genomic studies of ASD | |
| 30 | 윤미선 | Visualizing genomic information across chromosomes with PhenoGram |
| 05 | 차재현 | Network characteristics and clustering | |
| 12 | 윤미선 | EPO resistance index design & Mixed Effects Model | |
| 26 | 유준기 | Positive selection scoring method |
| 03 | 배소정 | Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies | |
| 10 | 권호식 | Busulfan and Adverse Drug Events | |
| 24 | 차재현 | GWA Studies with UKBB data |
| 05 | 최선 | GWAS | |
| 26 | 유준기 | Single-cell whole genome sequencing (scWGS) in degenrative diseases |
| 02 | 최선 | RD1 | |
| 09 | 윤미선 | Diffusion model | |
| 09 | 윤미선 | Diffusion model | |
| 15 | 유준기 | LRT-q: Rare variant association test for gene expression in multiple tissues | |
| 29 | 권호식 | Cell types and Functions |
| 09 | 권호식 | Cell markers and Stem cells |
| 19 | 유준기 | Histological category of cancer and classification of Cancer predisposition genes |
| 08 | 배소정 | Rare-Variant Association Analysis: Study Designs and Statistical Tests | |
| 22 | 최선 | Time series in healthcare_1 |
| 03 | 최선 | Data valuation | |
| 10 | 윤미선 | A gene set-integrated approach for predicting disease-associated genes | |
| 17 | 유준기 | Disease-gene Database: DisGeNET |
| 27 | 권호식 | NGS-based HLA genotyping tools |
| 08 | 윤미선 | CLIP for Medical Image | |
| 08 | 윤미선 | CLIP for Medical Image | |
| 15 | 유준기 | Review of recurrent event models | |
| 22 | 배소정 | MIMIC-IV database |
| 03 | 최선 | ||
| 04 | ̿ | Data of Signatures of copy number alterations from TCGA | |
| 11 | 권호식 | Mutect2(Mitochondria mode) | |
| 16 | 최선 | RNA seq research | |
| 25 | 최선 | RNA seq research |
| 16 | 최선 | Reinforcement learning | |
| 23 | ̿ | Standard Preprocessing and Analysis of Single Cell Gene Expression Data | |
| 30 | 권호식 | Association test for variants on X chromosome |
| 12 | 최선 | shap value | |
| 26 | 유준기 | Neutropenia criteria in Infliximab-treated IBD patients |
| 10 | 최선 | SAS command for HIRA data analysis | |
| 24 | 유준기 | Study design - Infliximab response in IBD patients |
| 06 | 유준기 | Accessing JGA (Japanese Genotype-Phenotype Archive) data | |
| 20 | ̿ | Mutational analysis of triple-negative breast cancer using targeted kinome sequencing | |
| 27 | 권호식 | Human CNV and complex genetic disease |
| 25 | 유준기 | Variant calling from transcriptome data |
| 07 | ӿ | Cancer register in UKB | |
| 14 | ̿ | Aggregate Trend of LOH by eQTL method | |
| 21 | 권호식 | Sequencing and Imputation | |
| 28 | 최선 | Morphological Filtering& segmentation metrics |
| 09 | 최선 | Research idea challenge using cancer big data | |
| 15 | 최선 | Public Cancer Bigdata | |
| 24 | 최선 | Cancer idea challenge_0924 | |
| 30 | 유준기 | Comparing Rare Variant Association Tests |
| 02 | ӿ | Molecular Features for subgrouping cancer patients | |
| 09 | ̿ | AlphaFold : Highly Accurate Protein Structure Prediction | |
| 30 | 권호식 | Genebass |
| 21 | ̿ | GTEX eQTL data | |
| 28 | 권호식 | Filtering out false positive variants |
| 01 | ̿ | LOH of Korean TNBC | |
| 08 | 권호식 | review : Contrast media | |
| 15 | ӿ | MSI analysis in Cancer. | |
| 22 | ̿ | Review of Prognosis Potential Loss of Heterozygosity in TNBC | |
| 29 | 권호식 | Adverse Effects of Contrast Media |
| 04 | ̿ | OncoKB Annotator | |
| 11 | 권호식 | Charlson comorbidity index |
| 25 | ӿ | metmap |
| 28 | ̿ | TNBC Survival Analysis |
| 03 | ̿ | Korean Uveal Melanoma Exome Analysis Figure | |
| 17 | 최선 | Training Keras Models Using the Genetic Algorithm with PyGAD | |
| 24 | ӿ | Comparison deleteriousness between germline and somatic variants in cancer |
| 05 | ̿ | Recent LOH algorithm review | |
| 26 | ӿ | pancreatic cancer WES analysis |
| 22 | 최선 | Pytorch tutorial | |
| 29 | ӿ | Summary of Pancreatic cancer data analysis |
| 10 | 최선 | propensity score matching | |
| 17 | ӿ | Identifying genetic variant associated FOLFIRINOX regimen | |
| 24 | ̿ | BLAST Result of UVM for Sanger Validation |
| 06 | 권호식 | Sex chromosomes and genetic association studies | |
| 13 | ӿ | Affecting FOLFIRINOX sensitivity(CAP grade) variants | |
| 27 | ̿ | CNV Validation Candidate Selection |
| 04 | 최선 | Sklearn.preprocessing package for preprocessing data | |
| 09 | ӿ | CAP grade analysis | |
| 15 | ̿ | UVM Copy Number Analysis |
| 02 | 최선 | Rethinking drug design in the artificial intelligence era | |
| 09 | ӿ | Comparison variants between TSVC and GATK pipeline | |
| 23 | ̿ | TNBC survival analysis |
| 06 | 유승원 | mercaptopurine study - discussion issues | |
| 20 | 권호식 | BiobankRead | |
| 27 | 최선 | Advanced SQL query using sub query, CTE |
| 07 | 권호식 | Analysis of HD-MTX induced renal toxicity In Pediatric Patients with ALL | |
| 15 | 최선 | ġĢ | |
| 28 | Genomic analysis of L-asparaginase-induced pancreatitis in 25 Korean pediatric ALL patients. |
| 05 | ӿ | Networks of Cancer Gene 6.0 | |
| 19 | 최선 | google cloud platform | |
| 26 | Characteristics of candidate genes that cause side effects of L-asparaginase |
| 01 | 권호식 | Rare-Variant Association | |
| 08 | 유승원 | Whole Exome Sequencing : germline variant caller comparison |
| 14 | 국수경 | Advantages and disadvantages of the Foundation-Medicine cancer panel in head and neck adenocarcinoma | |
| 21 | ӿ | Installation and Run of Neopepsee | |
| 28 | Settling the score |
| 07 | 권호식 | star allele assignment and annotation | |
| 14 | 김재환 | Development of knowledge base (RarePedia) and software platform (VAAT & PMAT) for NGS study result based genetic counseling | |
| 28 | 채정환 | WES seq analysis |
| 04 | 최선 | Introduction to clinicaltrials.gov DB | |
| 11 | ӿ | Variants calling workflow with GATK4 | |
| 18 | Analysis of onset age in depression | ||
| 25 | ̿ | TNBC Analysis of LOH Affecting Cancer Survival |
| 13 | 유승원 | study : exploration of non-linear method(Random Forest) for 6MP dose prediction( | |
| 20 | 권호식 | Study : Leukemia and Anticancer drugs | |
| 27 | 채정환 | Myocardial infarction based study |
| 06 | 김재환 | ü ǷḦ 帧 Ʈ | |
| 20 | [Review]Variant caller |
| 02 | 유승원 | circRNA : potential therapeutic target | |
| 09 | ̿ | Copy Number Alteration Visualization | |
| 16 | 권호식 | Mutational Signatures of COSMIC | |
| 23 | 채정환 | breast cancer patient RNA-seq data analysis |
| 06 | ȣ | CPTAC and TCGA Breast Cancer samples, data | |
| 13 | ̿ | TNBC and Non-TNBC Kinome NGS data | |
| 20 | Analysis of depression with cut-off in age of onset | ||
| 27 | ӿ | Network of Cancer genes 5.0 |
| 11 | Research plan for follicular thyroid neoplasm | ||
| 18 | ȣ | Depression phenotype and gwas studys | |
| 25 | ̿ | Somatic Mutation Analysis with WGS of Small Round Cell Tumor in Brain |
| 06 | Analysis of microarray using the GEO data | ||
| 13 | ӿ | Public Cell lines databases | |
| 19 | Visualization web application for interaction | ||
| 27 | Significantly regulated multi-isoform genes |
| 09 | The effect of BRAF and RAS mRNA expression on posttranscriptional regulation | ||
| 16 | ȣ | 1000 Depression data and non-baseline clinical variants | |
| 30 | ̿ | Package of Cancer Panel for Pathology Department |
| 05 | Isoform fraction analysis methodology | ||
| 12 | Further AS and APA analysis in TCGA THCA data | ||
| 19 | ȣ | NCBI EST ABCB1 information summary | |
| 26 | ̿ | How to get controlled access data from ICGC data portal. |
| 04 | Cancer genome database: COSMIC | ||
| 11 | MISO review | ||
| 18 | Alternative polyadenylation by mutation type of papillary thyroid cancer | ||
| 25 | ȣ | TCGA data and perturbation sensitivity |
| 18 | ̰ȭ | List of Drugs for Pharmsafe Report | |
| 25 | ̰ȭ | BRONJ variants analysis |
| 13 | ̰ȭ | Statistical analysis of variants in COPD target sequencing study. |
| 07 | ̿ | Refractory Acute Leukemia(RAL) data analysis result | |
| 15 | ̰ȭ | COPD targeted sequencing data: variants analysis | |
| 21 | ̰ȭ | Risk prediction of SNUBI Genome using GWAS catalog |
| 11 | ̰ȭ | 2015 ACMG guideline review | |
| 17 | Alternative Polyadenylation in heart development and disease | ||
| 24 | ̿ | Kataegis and Rainfall plot | |
| 24 | ̿ | Kataegis and Rainfall plot | |
| 30 | ̰ȭ | COPD data analysis results |
| 01 | ̰ȭ | Cancer genomics review | |
| 15 | ̰ȭ | Review of method: Burden test | |
| 15 | ̰ȭ | Review of the method :burden test | |
| 22 | Review: splicing analysis of cancer data | ||
| 29 | ̿ | Reversal Loss of Heterozygosity |
| 20 | ̰ȭ | Review: A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. |
| 06 | HHA data analysis : protein domain annotation results | ||
| 13 | ̰ȭ | Review of public genome data: 1KP and HapMap project |
| 10 | SG02 Genome Interpretation processing | ||
| 17 | ̿ | Comprehensive analysis of DNA data | |
| 31 | Sequenza | ||
| 31 | Sequenza | ||
| 31 | Sequenza | ||
| 31 | Sequenza | ||
| 31 | ̰ȭ | Unmatched results of genome annotation by multiple tools |
| 12 | ̰ȭ | Genomic data privacy and security | |
| 12 | ̰ȭ | Genomic data privacy and security | |
| 26 | Phenotype-Genotype correlations | ||
| 26 | phenotype | ||
| 26 | Phenotype-Genotype correlations | ||
| 30 | edgeR review |
| 03 | Exome Sequencing pipeline and analysis results | ||
| 11 | ̿ | G.S.E.A for data analysis | |
| 18 | LOH of tumor suppressor genes and chemotherapy response using TCGA data |
| 12 | ̰ȭ | Exome sequencing annotation pipeline | |
| 19 | ̰ȭ | News | |
| 26 | ȼ | Experimental Design & Statistical Issues in RNA-seq |
| 01 | ȼ | Thyroid cancer RNA-seq analysis | |
| 01 | ̰ȭ | News about Genomics | |
| 08 | ̰ȭ | Exome-Sequencing annotation strategy | |
| 22 | Variant Annotation Tool(VAT) Review | ||
| 29 | ̿ | TCGA ovarian cancer patient data analysis | |
| 29 | ̿ | TCGA ovarian cancer patient data analysis |
| 03 | Variant calling pipeline for P0 genome | ||
| 03 | ̰ȭ | Genomic news | |
| 10 | ̰ȭ | Genome News | |
| 10 | ȼ | RNA-seq pipeline review | |
| 24 | ̰ȭ | Genome news | |
| 24 | ̿ | Network based survival | |
| 16 | Prediction algorithms used in SIFT | ||
| 22 | ӿ | DNMT3A in OMIM |
| 04 | ӿ | Characteristics of DNA methylation depending on cancer types | |
| 11 | ̿ | Algorithms for finding mutated driver pathways | |
| 18 | GWAS of cancer |
| 20 | Identify high coverage and mutual exclusive mutations | ||
| 27 | RNA isoforms of cancer genes |
| 22 | ̿ | Summary of GICD Team | |
| 22 | ̿ | Summary of GICD Team | |
| 28 | Current Trends in Proteomics | ||
| 28 | Current Trends in Proteomics | ||
| 28 | Current Trends in Proteomics |
| 15 | network based pathway analysis for COPD data | ||
| 15 | network based pathway analysis for COPD data |
| 11 | Network analysis for ChIPseq data | ||
| 20 | Summary of MedCassandra |
| 27 | Comparison of transcriptome via Drosophilia RNA-seq data |
| 13 | Drug integration for drug repositioning using ADR |
| 14 | Bipartite Network Analysis for TCGA Kidney clear cell carcinoma dataset |
| 19 | Breast cancer transcriptome analysis using weighted PPI Network | ||
| 28 | data extraction for construction of ADR-drug-target relationship |
| 01 | Breast cancer transcriptome analysis using weighted PPI Network | ||
| 03 | ȿ | Consensus clustering of GBM data from TCGA | |
| 29 | Building And Visualizing Breast Cancer Co-expression Network |
| 15 | 赵 | Summary on TCGA data | |
| 28 | ȭ | Core pathways in ovarian carcinomas driven by somatic mutations | |
| 07 | Predicting the disease potential of personal genome based on disease hierarchy |
| 02 | Rocky | Database schema for miRNA variant annotation | |
| 13 | ̼S | Finding RNA-editing site |
| 03 | introduction of metagenomics | ||
| 17 | Introduction of Epigenetics - DNA methylation and analysis tool | ||
| 24 | Sequence Informatics |
| 08 | Identifying cancer driver genes in tumor genome sequencing studies | ||
| 15 | ̼ | Gene ontology analysis for RNA-seq accounting for selection bias | |
| 07 | MagicViewer: integrated solution for next-generation sequencing data visualization and genetic variation detection and annotation | |
|
| 14 | Problems in manipulating large scale NGS data |
| 31 | Brief description of team | ||
| 31 | ̼ | converting RNA-seq into tagCount compare with traditional extracting expression profile |
| 07 | Specific dose responsiveness in B10 to ionizing radiation | ||
| 14 | ¼ | On parkinson-mouse data [parkinson] |
| 02 | Data Analysis of Methotrexate in Rheumatoid Arthritis | |
|
| 09 | ¼ | Discovering differential co-expression patterns between normal and tumor samples [DiffCoexpression] | |
| 16 | Interpretation of Mantel test | ||
| 23 | ѹ̷ | Meta analysis of AB chip & cDNA chip | |
| 30 | Gene Set Association Test (for GAW15 data) |
| 12 | Mantel test with pathway gene set II | ||
| 19 | Finding gene modulator with latent variable model | ||
| 26 | ѹ̷ | MCF-7 cell line data analysis | |
| 07 | ѹ̷ | cDNA microarray | |
| 14 | Analysis plan for "Genetic Analysis Workshop" data. [genetical genomics] | ||
| 28 | ¼ | Differential co-expression patterns between normal and tumor samples [DiffCoexpression] |
| 10 | Chicken PGC data analysis | ||
| 17 | Meta Analysis of Microarray | ||
| 24 | ¼ | Prediction of microbial infection [Myco] | |
| 31 | Selection of gene subset with MDR algorithm in Mantel test |
| 14 | Data Analysis - Radiation Data | ||
| 14 | Bioconductor Basics | ||
| 21 | microarray data preprocessing | ||
| 21 | ¼ | PathPlus |
| 07 | ¼ | PathPlus | |
| 14 | Analysis of responsive genes after Low-Dose Ionizing Radiation Using Evolutionary Algorithm | ||
| 14 | Identifying Biological themes Within lists of genes |
| 12 | Analysis of variance in thyroid microarray data | ||
| 19 | Simulation study to measure cyclicity in microarray expression profile | ||
| 24 | ¼ | Monthly report | |
| 26 | (H) | ES Cell Differentiation |
| 27 | Monthly report | ||
| 29 | Preprocessing cell-cycle microarray data using PERL |
| 04 | What should I make bed? T.T |