'discovery' 검색 결과

KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compound-target-disease network construction and analysis for drug repurposing, polypharmacology discovery, and multi-target drug design. Uses STRING, BioGRID, ChEMBL, DGIdb, OMIM, OpenTargets. Use for off-target effect prediction, network-based drug repurposing, and identifying molecules with desired multi-target profile.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization/drift correction see metabolomics/normalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning/biomarker-discovery and machine-learning/model-validation; for pathway interpretation see metabolomics/pathway-mapping; for design/power/multiplicity regime see experimental-design/multiple-testing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

De novo molecule generation for drug discovery. Scaffold-based analog enumeration, fragment growing/linking, structure-based design, multi-objective optimization, and drug-likeness filtering.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Multi-method binding pocket detection and druggability assessment. Grid-based, fpocket, and P2Rank detection with druggability scoring, visualization, and cross-structure comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Product manager for PRD creation and requirements discovery. Use when the user asks to talk to Nicole or requests the product manager.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Map owned assets, attack surface, and inventory for systems the requester is authorized to assess. Use for asset discovery, service catalogs, and exposure reviews — never unauthorized scanning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Work on the Android app — the thin WebView wrapper in `android/`. Use this whenever a task touches the Kotlin sources, the Gradle build, the WebView shell, the native settings screen, LMS server auto-discovery, the JS↔native bridge, app strings/i18n, or the Android CI/lint. Covers the "shell only, UI lives on the web" principle, the debug/release signing split, the static versioning F-Droid needs, and the min/target SDKs, so changes fit the wrapper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predict enhancer-gene regulatory connections from ATAC-seq using ABC, ENCODE-rE2G, HiChIP, or Cicero. Use when linking distal enhancers to target genes, choosing between contact-aware (ABC, ENCODE-rE2G), accessibility-only (Cicero), and orthogonal (HiChIP H3K27ac, EpiMap) approaches, validating predictions against CRISPRi-FlowFISH gold-standard, or building cell-type-specific regulatory maps for fine-mapping or therapeutic target discovery.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.