“biology”的搜索结果

Expert-level computational biology covering molecular dynamics simulation, protein structure prediction, systems biology, network analysis, and machine learning in biology.

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Expert-level microbiology covering bacterial and viral biology, microbial physiology, pathogenesis, antimicrobial resistance, microbiome science, and industrial microbiology.

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Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.

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Comprehensive biology expert from molecular biology to ecology, covering cell biology, genetics, evolution, and physiology

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Expert-level cell biology knowledge. Use when working with cell structure, organelles, cell signaling, cell cycle, cell division, cytoskeleton, membrane biology, vesicular trafficking, apoptosis, or cell biology techniques. Also use when the user mentions 'organelle', 'mitochondria', 'endoplasmic reticulum', 'Golgi', 'cytoskeleton', 'cell cycle', 'mitosis', 'meiosis', 'apoptosis', 'signal transduction', 'membrane potential', 'endocytosis', or 'cell migration'.

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Expert-level marine biology covering ocean ecosystems, marine organisms, coral reef ecology, deep sea biology, fisheries science, ocean chemistry, and marine conservation.

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Use when targeting Global Change Biology or deciding whether a biology/ecology manuscript fits this venue. Encodes the journal's fit, the global-change-driver framing and biological-evidence bar, scale and mechanism expectations, house style, official-submission re-check, and desk-reject heuristics.

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Use when targeting Annual Review of Plant Biology or deciding whether a plant-biology review proposal fits this invited-review venue. Encodes the journal's fit, the authoritative-synthesis and invited-article bar, evidence and balance expectations, house style, official-submission re-check, and desk-reject heuristics.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Expert-level developmental biology covering embryogenesis, cell fate determination, morphogen gradients, organogenesis, stem cells, and regenerative medicine.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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从按领域整理的400多项技能索引中，查找适合基因组、基因表达等分析的专业指南与可执行流程，获取所需资料，并参考代码示例和分析管线。

• 查找序列质量检查步骤
• 选择基因表达分析流程
• 了解单细胞分析方法

Build a structured research dossier for a defined patient population, covering epidemiology, disease biology, current treatment, regulatory precedents, and major clinical trials. Receive a cited report and structured data to inform therapeutic development and trial planning.

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• Characterizing a target patient population
• Reviewing current treatments and unmet needs
• Researching regulatory precedents and landmark trials

Use when targeting Soil Biology & Biochemistry or deciding whether a soil-science manuscript fits this venue. Encodes the journal's fit, the soil-process-mechanism bar, evidence and data-reporting expectations, house style, official-submission re-check, and desk-reject heuristics.

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Simulate SimBiology models — ODE, stochastic (SSA), scenarios, and sensitivity analysis. Use when asked to run, simulate, predict, explore what-if, or identify influential parameters.

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Fit SimBiology model parameters to data — fitproblem, population NLME, virtual patients, and NCA. Use when asked to fit, estimate, calibrate, or compute PK metrics.

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Build, modify, and diagram SimBiology models — API reference, helper functions, and layout patterns. Use when constructing or editing models programmatically or visually.

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Molecular biology toolkit: sequence manipulation, FASTA/GenBank/PDB I/O, NCBI Entrez, BLAST automation, pairwise/MSA alignment, Bio.PDB, phylogenetic trees. Use for batch processing, custom pipelines, format conversion, PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.

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Create crafted, accurate visualizations of anatomy, neurobiology, pharmacology, drugs, cells, and molecular biology. Show real biological structures and make their mechanisms visible, using literal anatomy or a clearly identified mechanical metaphor. Build interactive isometric SVG figures with sourced numbers, accessible motion, optional WebGL, a framed card and live readout. Use for biological explainers, educational illustrations, documentation and interactive figures.

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Use when evaluating biology grant ideas in the Chinese funding context (NSFC, MOST, etc.) — diagnosing project legitimacy, mechanism-centered scientific questions, reviewer-aware logic, innovation discipline, feasibility, and scope control across funding levels (youth, general, key).

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Use when a task needs the judgment of a Biology Professor at a research university — writing or defending an NIH/NSF grant budget, deciding authorship order on a paper, navigating an IACUC or biosafety protocol, mentoring a PhD student through a stalled project, or converting a course when active-learning data doesn't match student pushback.

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