「perf」の検索結果

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 2024 bioRxiv; 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.

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Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.

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Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

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Performs subgroup and heterogeneous treatment effect (HTE) analyses for clinical trials. Covers Mantel-Haenszel pooling, Breslow-Day, interaction tests in regression, RERI for additive interaction, modern data-adaptive HTE methods (STEPP, SIDES, causal forests, X/R-learners), Bayesian shrinkage (Dixon-Simon, MAP, EXNEX), graphical multiplicity (Bretz-Maurer), and credibility frameworks (Sun BMJ, EMA 2019). Use when analyzing treatment effects across patient subgroups for regulatory submissions or precision-medicine claims.

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Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, MHFP6 LSH forest, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

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Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

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Performs retrosynthetic planning using AiZynthFinder (template-based MCTS), maintained or version-pinned template-free models, ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, configurable MCTS rewards, building-block availability, and forward-prediction checks. Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.

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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

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Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.

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Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peaksets, integrating with paired scRNA-seq, doublet detection (AMULET vs ArchR vs scDblFinder), or running pseudobulk differential accessibility per cluster.

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Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.

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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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Use when building, fixing, or improving ANY SwiftUI UI — views, navigation, layout, animations, gestures, debugging, iOS 26 features, iPhone Duo, view-level performance, feature architecture.

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Use when the user mentions SwiftData review, @Model issues, SwiftData migration safety, or SwiftData performance checking.

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Use when working with ANY GPU rendering, Metal, OpenGL migration, shaders, 3D content, RealityKit, AR, USD/USDZ files, or display performance. Covers Metal migration, shader conversion, RealityKit ECS, RealityView, USDKit, ProMotion.

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Use when the user mentions slow builds, build performance, or build time optimization.

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Builds and reviews SwiftUI views with modern MV architecture, state, composition, isolated previews, and migration guidance. Covers @Observable ownership, @State/@Bindable/@Environment wiring, view decomposition, ViewModifiers, environment values, .task loading, iOS 26+ handoffs, Writing Tools, clipboard availability, and performance. Use when structuring SwiftUI state, managing @Observable, composing views, previewing meaningful UI states, or correcting SwiftUI patterns.

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Writes and migrates Swift Testing framework tests with @Test, @Suite, #expect, #require, confirmation, traits, withKnownIssue, Attachment.record, processExitsWith exit tests and capture lists, Test.cancel, Issue.record warnings/manual failures, XCTest-to-Swift Testing migration, Xcode 27 interoperability modes, XCUITest UI-test boundaries, performance/snapshot boundaries, mocking, async patterns, and test organization. Use when writing tests, converting XCTest assertions such as XCTUnwrap or XCTFail, reviewing advanced Swift Testing API availability, or deciding when to keep XCTest/XCUITest.

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Use Apple CryptoKit for Swift cryptographic primitives. Use when hashing with SHA-2 or SHA-3, generating HMACs, encrypting with AES-GCM or ChaChaPoly, signing with P256/P384/P521/Curve25519 or ML-DSA keys, performing ECDH, HPKE, ML-KEM, or X-Wing key exchange, using Secure Enclave CryptoKit keys, or migrating CommonCrypto code to CryptoKit.

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Manages iOS Simulator devices and app tests with xcrun simctl: lifecycle, install/launch, push and location simulation, privacy permissions, deep links, status-bar overrides, screenshots/video, log streaming, app containers, and targetEnvironment(simulator). Use when scripting Simulator workflows, debugging CoreSimulator boot failures, or deciding which hardware, performance, networking, memory-pressure, and security behaviors require a physical device.

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Use when collecting or analyzing production iOS or iPadOS performance telemetry with MetricKit, including iOS 27 MetricManager async metric or diagnostic reports, hang or crash triage, custom signposts, extended launch measurement, durable export, or iOS 26 MXMetricManager compatibility.

日本語の概要は準備中です。原文の説明を表示しています。