A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
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1 | DATE | SPEAKER | TITLE | Etc. | ||||||||||||||||||||||
2 | 2019.01.04 | 조수복 | Identification of methylation sites and signature genes with prognostic value for luminal breast cancer | Paper | ||||||||||||||||||||||
3 | 김기법 | PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction | Paper | |||||||||||||||||||||||
4 | 2019.01.08 | 이송연 | 1H NMR based serum metabolic profiling reveals differentiating biomarkers in patients with diabetes and diabetes-related complication | Personal study | ||||||||||||||||||||||
5 | 2019.01.11 | 김현호 | Learning Deep Features for Discriminative Localization | Paper | ||||||||||||||||||||||
6 | 이인구 | A new view of transcriptome complexity and regulation through the lens of local splicing variations | Paper | |||||||||||||||||||||||
7 | 2019.01.15 | 진일중 | Cancer-Drug response prediction | Personal study | ||||||||||||||||||||||
8 | 2019.01.18 | 김은영 | Prediction of Effective Drug Combinations by an Improved Naive Bayesian Algorithm | Paper | ||||||||||||||||||||||
9 | 이송연 | MVP_an open-source preprocessor for cleaning duplicate records and missing values in mass spectrometry data | Paper | |||||||||||||||||||||||
10 | 2019.01.22 | 김현호 | Predicting Drug Safety and Communicating Risk_Benefits of a Bayesian Approach | Personal study | ||||||||||||||||||||||
11 | 2019.01.25 | 이인구 | You only look once: unified, real-time obejct detection | Paper | ||||||||||||||||||||||
12 | 진일중 | PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks | Paper | |||||||||||||||||||||||
13 | 2019.01.29 | 2019 Timeline 점검 및 워크샵 | ||||||||||||||||||||||||
14 | 2019.02.08 | 조수복 | Methylome sequencing in triple-negative breast cancer reveals distinct methylation clusters with prognostic value | Paper | ||||||||||||||||||||||
15 | 김기법 | Representation Learning on Graphs_ Methods and Applications | Paper | |||||||||||||||||||||||
16 | 2019.02.12 | 이인구 | HoTS: Hightlight on Target Sequence and prediction of Drug-target interaction | Personal study | ||||||||||||||||||||||
17 | 2019.02.15 | 김도현 | ADAGE signature analysis: differential expression analysis with data-defined gene sets | Paper | ||||||||||||||||||||||
18 | 김현호 | The Catch-22 of Predicting hERG Blockade Using Publicly Accessible | Paper | |||||||||||||||||||||||
19 | 2019.02.19 | 천세종 교수님 | Genetic predisposition of pediatric B-lymphoblastic leukemia/lymphoma | Personal study | ||||||||||||||||||||||
20 | 2019.02.22 | 진일중 | Predict drug sensitivity of cancer cells with pathway activity inference | Paper | ||||||||||||||||||||||
21 | 이송연 | Missing value imputation approach for mass spectrometry-based metabolomics data | Paper | |||||||||||||||||||||||
22 | 2019.02.26 | 김도현 | Human Breast Cancer Subtyping with meaningful representation | Personal study | ||||||||||||||||||||||
23 | 2019.03.05 | 김기법 | Attention is all you need | Personal study | ||||||||||||||||||||||
24 | 2019.03.08 | 김은영 | Predicting combinative drug pairs via multiple classifier system with positive samples only | Paper | ||||||||||||||||||||||
25 | 이인구 | DeepSite: protein-binding site predictor using 3D-convolutional neural network | Paper | |||||||||||||||||||||||
26 | 2019.03.12 | 조수복 | Network-based integration of multi-omics data for predicting cancer prognosis | Personal study | ||||||||||||||||||||||
27 | 2019.03.18 | 조수복 | Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers, 2017 | Paper | ||||||||||||||||||||||
28 | 2019.03.19 | 김은영 | Drug-drug interaction prediction using gene expression data | Personal study | ||||||||||||||||||||||
29 | 2019.03.22 | 김도현 | Statistical significance of variables driving systematic variation in high-dimensional data | Paper | ||||||||||||||||||||||
30 | 이송연 | GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies | Paper | |||||||||||||||||||||||
31 | 2019.03.26 | 진일중 | Interpretable Cancer-Drug response prediction with gene set activity | Personal study | ||||||||||||||||||||||
32 | 2019.03.29 | 김현호 | Deep learning-Based Prediction of Drug-Induced Cardiotoxicity | Paper | ||||||||||||||||||||||
33 | 진일중 | Hierarchical Attention Networks for Document Classification | Paper | |||||||||||||||||||||||
34 | 2019.04.02 | 이인구 | HoTS: Hightlight on Target Sequence and prediction of Drug-target interaction | Personal study | ||||||||||||||||||||||
35 | 2019.04.06 | 김은영 | Drug and disease signature integration identifies synergistic combinations in glioblastoma | Paper | ||||||||||||||||||||||
36 | 이인구 | Interpretable Drug Target Prediction Using Deep Neural Represtionation | Paper | |||||||||||||||||||||||
37 | 2019.04.09 | 이송연 | Breast cancer biomarker prediction via metabolomics data analysis | Personal study | ||||||||||||||||||||||
38 | 2019.04.12 | 조수복 | A gene expression-based risk model reveals prognosis of gastric cancer, 2018 | Paper | ||||||||||||||||||||||
39 | 2019.04.23 | 김현호 | Predicting hERG blockers by CNN based Deep learning | Personal study | ||||||||||||||||||||||
40 | 김은영, 이인구 | Drug Discovery Chemistry 2019 학회 보고 | ||||||||||||||||||||||||
41 | 2019.04.26 | 김현호 | Deeply learning molecular structure-property relationships using attention and gate augmented graph convolutional network | Paper | ||||||||||||||||||||||
42 | 이송연 | NMR-based metabolomics study of canine bladder cancer | Paper | |||||||||||||||||||||||
43 | 2019.04.30 | 조수복, 진일중, 김도현 | HGM2019 후기 | |||||||||||||||||||||||
44 | 김도현 | Identification of molecular subtypes of human breast cancer through gene expression and prognostic outcome | Personal study | |||||||||||||||||||||||
45 | 2019.05.03 | 진일중 | A novel heterogeneous network-based method for drug response prediction in cancer cell lines | Paper | ||||||||||||||||||||||
46 | 김도현 | A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data | Paper | |||||||||||||||||||||||
47 | 2019.05.07 | 김기법 | Learned protein embedding for machine learning | Personal study | ||||||||||||||||||||||
48 | 2019.05.10 | 김은영 | Network-based prediction of drug combinations | Paper | ||||||||||||||||||||||
49 | 2019.05.14 | 김기법 | Learned protein embedding for machine learning (보충) | Personal study | ||||||||||||||||||||||
50 | 조수복 | Improving prognostic prediction through the predicting clinical status | Personal study | |||||||||||||||||||||||
51 | 2019.05.17 | 조수복 | DNA methylation markers for diagnosis and prognosis of common cancers | Paper | ||||||||||||||||||||||
52 | 이인구 | Linguistic measures of chemical diversity and the keywords of molecular collections | Paper | |||||||||||||||||||||||
53 | 2019.05.21 | 김은영 | DDI prediction | Personal study | ||||||||||||||||||||||
54 | 2019.05.24 | 김도현 | Learning Latent Representations in Neural Networks for Clustering Through Pseudo Supervision and Graph-based Activity Regularization | Paper | ||||||||||||||||||||||
55 | 김현호 | FP2VEC | Paper | |||||||||||||||||||||||
56 | 2019.05.28 | 진일중 | Interpretable Cancer-Drug response prediction with gene set activity | Personal study | ||||||||||||||||||||||
57 | 2019.05.31 | 진일중 | Predicting drug response of tumors from integrated genomic profiles by deep neural networks | Paper | ||||||||||||||||||||||
58 | 이송연 | Serum Metabolomics to Identify the Liver Disease-Specific Biomarkers for the Progression of Hepatitis to Hepatocellular Carcinoma | Paper | |||||||||||||||||||||||
59 | 2019.06.11 | 이인구 | HoTS: Highlights on Target Sequence and Prediction of Drug-Target Interaction | Personal study | ||||||||||||||||||||||
60 | 2019.06.14 | 김은영 | Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures | Paper | ||||||||||||||||||||||
61 | 이인구 | Predicting protein-ligand binding residues with deep convolutional neural network | Paper | |||||||||||||||||||||||
62 | 2019.06.18 | 이송연 | Breast cancer biomarker prediction via metabolomics data analysis | Personal study | ||||||||||||||||||||||
63 | 2019.06.21 | 조수복 | Reduced DNA methylation patterning and transcriptional connectivity define human skin aging, 2016 | Paper | ||||||||||||||||||||||
64 | 2019.06.25 | 김현호 | Predicting hERG blockers by Interpretable Deep learning | Personal study | ||||||||||||||||||||||
65 | 2019.06.28 | 김도현 | Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data | Paper | ||||||||||||||||||||||
66 | 김현호 | Identifying Structure-Property Relationships through SMILES syntax Analysis with Self-Attention Mechanism | Paper | |||||||||||||||||||||||
67 | 2019.07.02 | 김도현 | Identification of molecular subtypes of human breast cancer using VAE | Personal study | ||||||||||||||||||||||
68 | 2019.07.05 | 이송연 | Untargeted metabolomic profiling of urine from healthy dogs and dogs with chronic hepatic disease | Paper | ||||||||||||||||||||||
69 | 진일중 | DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines | Paper | |||||||||||||||||||||||
70 | 2019.07.09 | 김기법 | XLNet: Generalized Autoregressive Pretraining for Language Understanding | Personal study | ||||||||||||||||||||||
71 | 2019.07.12 | 김은영 | Drug-Drug adverse effect prediction with graph co-attention | Paper | ||||||||||||||||||||||
72 | 이인구 | Deep Learning Enables High-quality and High-throughput prediction of enzyme commission numbers | Paper | |||||||||||||||||||||||
73 | 2019.07.16 | 김은영 | Drug-drug interaction prediction using gene expression data | Personal study | ||||||||||||||||||||||
74 | 2019.07.19 | 2019 하계 Timeline 점검 및 워크샵 | ||||||||||||||||||||||||
75 | 2019.07.23 | 조수복 | DNA methylation dynamics during epigenetic modification in mammalian life (PhD Proposal) | Personal study | ||||||||||||||||||||||
76 | 2019.07.26 | 조수복 | Epigenetic Biomarker to Support Classification into Pluripotent and Non-Pluripotent Cells | Paper | ||||||||||||||||||||||
77 | 김기법 | A High Efficient Biological Language Model for Predicting Protein–Protein Interactions | Paper | |||||||||||||||||||||||
78 | 2019.07.30 | 진일중 | Interpretable Cancer-Drug response prediction with gene set activity | Personal study | ||||||||||||||||||||||
79 | 2019.08.02 | 이한솔 | Computational prediction of inter-species relationships through omics data analysis and machine learning | Paper | ||||||||||||||||||||||
80 | 배봉성 | SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines | Paper | |||||||||||||||||||||||
81 | 2019.08.06 | 이인구 | HoTS: Highlights on Target Sequence and Prediction of Drug-Target Interaction | Personal study | ||||||||||||||||||||||
82 | 2019.08.09 | 김도현 | Pathway-based deep clustering for molecular subtyping of cancer | Paper | ||||||||||||||||||||||
83 | 김현호 | Path Augmented Graph Transformer Network | Paper | |||||||||||||||||||||||
84 | 2019.08.12 | 이송연 | Breast cancer biomarker prediction via metabolomics data analysis | Personal study | ||||||||||||||||||||||
85 | 2019.08.16 | 이송연 | Bladder Cancer Biomarker Discovery Using Global Metabolomic Profiling of Urine | Paper | ||||||||||||||||||||||
86 | 김은영 | Drug Response Similarity Prediction using Siamese Neural Networks | Paper | |||||||||||||||||||||||
87 | 2019.08.23 | 이인구 | Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Network | Paper | ||||||||||||||||||||||
88 | 진일중 | Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders | Paper | |||||||||||||||||||||||
89 | 2019.08.26 | 김은영 | Machine learning and multi-omics based drug candidates prediction modeling (PhD Proposal) | Personal study | ||||||||||||||||||||||
90 | 2019.08.30 | 조수복 | DNA methylation and hormone receptor status in breast cancer | Paper | ||||||||||||||||||||||
91 | 김기법 | BERT_Pre-training of Deep Bidirectional Transformers for Language Understanding | Paper | |||||||||||||||||||||||
92 | 2019.09.03 | 배봉성 | iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting | Personal study | ||||||||||||||||||||||
93 | 2019.09.06 | 배봉성 | iDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE | Paper | ||||||||||||||||||||||
94 | 김도현 | DeepCC: a novel deep learning-based framework for cancer molecular subtype classification | Paper | |||||||||||||||||||||||
95 | 2019.09.10 | 이한솔 | Taxonomy-aware feature engineering for microbiome classification | Personal study | ||||||||||||||||||||||
96 | 2019.09.17 | 김현호 | Predicting hERG blockers by Deep Convolutional neural network | Personal study | ||||||||||||||||||||||
97 | 2019.09.20 | 김현호 | A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification | Paper | ||||||||||||||||||||||
98 | 이한솔 | A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains | Paper | |||||||||||||||||||||||
99 | 2019.09.24 | 김도현 | Identification of molecular subtypes of human breast cancer using VAE | Personal study | ||||||||||||||||||||||
100 | 2019.09.27 | 이송연 | Application of ensemble deep neural network to metabolomics studies | Paper |