리서치·학습 스킬

Turns a biomedical study objective into operational primary, secondary, and exploratory endpoints. It specifies event rules, assessment timing, composite logic, plausible data capture sources, and feasibility or interpretation concerns, while asking for clarification when the study context is too vague.

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• Defining protocol-ready endpoints from a study aim
• Clarifying event criteria and follow-up timing
• Reviewing a proposed composite endpoint

Creates a structured comparative network toxicology study plan for two related exposures and one shared toxic outcome. It maps shared and exposure-specific targets, proposes enrichment and network analyses, and lays out four workload levels, validation options, figures, and a cautious evidence-based interpretation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing a parent compound and its metabolite
• Planning shared and specific target analysis for two chemicals
• Designing a comparative toxicology study with validation options

Plan a biomedical study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related data. Define the use case, align data layers, choose feature reduction and modeling routes, and set validation steps across four workload levels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a clinical and multi-omics biomarker study
• Choosing how to align clinical records with molecular data
• Comparing four levels of analysis and validation effort

Plan a bulk-transcriptome tumor biomarker study guided by immune infiltration. The skill structures analysis from differential expression and immune estimates through feature selection and diagnostic modeling, with four workload levels, validation options, and a figure plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a bulk-expression diagnostic biomarker study for a cancer type
• Linking immune infiltration estimates to candidate biomarkers
• Scoping machine-learning feature selection and diagnostic modeling

Refine a broad biomedical research aim into a bounded, measurable objective that can guide study design. Identify the objective type, missing details, and exploratory versus confirmatory intent, then provide versions suited to plain-language, protocol, and downstream planning needs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Narrow a broad biomedical research aim before choosing a study design
• Identify missing populations, outcomes, comparisons, or time frames
• Separate exploratory questions from confirmatory objectives

Plan an observational study of disease and biomarker associations using NHANES-style survey data. Get four workload options, a recommended plan, analysis steps, figures, validation boundaries, and a verified-reference search strategy tailored to the research question.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a survey-based biomarker association study
• Comparing a minimal study plan with publication-focused options
• Adding a retrospective hospital cohort to validate an association

Find recent algorithms that fit a biomedical research question, its data type, goals, and resource limits. The skill prioritizes recent literature, verifies primary method papers, checks published uses when available, and separates practical recommendations from baselines and unverified names.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding recent methods for a disease-specific research project
• Matching algorithms to omics data and a study aim
• Checking whether a named biomedical algorithm is real and relevant

Turn a biomedical research direction into a structured plan combining Mendelian randomization, a method for testing causal links, with single-cell RNA sequencing. Compare four workload levels, choose a primary plan, and outline analyses, figures, validation, and verified references.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan an MR and single-cell study from a disease or mechanism theme
• Compare feasible study plans for different time and resource limits
• Map analysis steps to figures and validation strategies

Plan a computational study that compares gene expression across two diseases to identify shared biomarkers or hub genes. It lays out four workload options, recommends a primary workflow, and organizes analysis, validation, figures, and literature support.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a shared biomarker study for two diseases
• Comparing differential expression and shared gene signals
• Designing hub-gene prioritization and diagnostic evaluation

Shape a biomedical study idea into a realistic plan based on available samples, data, time, budget, lab capacity, and team skills. The skill identifies the main constraints, narrows the study route, defines a minimum executable version, and flags dependencies and assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Scale a biomedical project to fit a limited timeline or budget
• Choose a feasible route when lab or validation resources are uncertain
• Narrow a study idea to the minimum version that can answer its core question

Compare plausible study routes for a defined biomedical research question and recommend one primary plan. Weigh question fit, evidence value, feasibility, dependencies, and tradeoffs, then explain which alternatives belong as secondary, later, or excluded options without drafting a full protocol.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a primary route for a biomedical research question
• Comparing cohort, bioinformatics, and wet-lab approaches
• Explaining why alternative study routes should be secondary

Turn a biomedical association or mechanism finding into an ordered validation plan. The skill identifies what current evidence supports, highlights the weakest link, and separates the next essential steps from stronger upgrades and optional extensions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan the next validation steps for a pathway finding
• Decide which experiments are essential and which can wait
• Identify the weakest link in a biomarker evidence chain

Plan a single-drug, single-adverse-effect network pharmacology study. The skill organizes target collection, overlap analysis, PPI hub prioritization, enrichment, and optional docking or validation into four workload levels with evidence limits and references.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a network pharmacology study for one drug and adverse effect
• Compare Lite, Standard, Advanced, and Publication+ workloads
• Map dependencies, figures, and validation steps for a study

Plan a biomedical study to predict response or resistance to a defined treatment. Clarify the population, response endpoint, baseline predictors, model development, and validation approach while surfacing bias, data leakage, feasibility limits, and assumptions that still need confirmation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a treatment response prediction study
• Defining responder and non-responder groups
• Designing a resistance prediction study

Generate anatomy quizzes by body region, topic, and difficulty, including neuroanatomy and clinical scenarios. The described tool also supports image identification, timed exam practice, adaptive question selection, and progress tracking for medical education.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a focused quiz for a body region and difficulty
• Practice neuroanatomy pathways and lesion effects
• Generate clinically framed anatomy questions

Plan conventional diagnostic machine learning studies for non-oncology diseases using transcriptome data. It produces four workload options, a recommended design, stepwise methods, figures, validation tiers, a minimal version, upgrade path, and verified literature or a search strategy when browsing is unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a disease versus control transcriptome study
• Design mechanism-guided biomarker feature selection
• Compare study plans at different workload levels

Pressure-test a biomedical study design before data collection by classifying variable roles, identifying plausible confounders, and choosing how to control them. Get a reasoned adjustment strategy, likely bias risks, and mitigation steps, with uncertainty and residual bias made explicit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning confounder control for an observational study
• Deciding which variables to adjust for
• Comparing matching, stratification, and weighting

Turn medical notes, textbook excerpts, lecture summaries, or Q&A material into focused Anki cards. The workflow produces an importable TSV or card blueprint, states assumptions, and flags source material that is incomplete or too ambiguous to convert safely.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert lecture notes into focused recall cards
• Create drug mechanism and side-effect cards
• Build anatomy cards covering location and function

Create an executable Mendelian randomization study plan from a proposed exposure and outcome. It selects a suitable design, outlines genetic instrument choices and analyses, compares four workload levels, and sets validation steps and boundaries for interpreting causal claims.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an MR study for an exposure and outcome
• Choosing between one-way and bidirectional MR
• Designing sensitivity checks for genetic instruments

Turn a cancer context, fixed target gene, and validation goal into a structured single-gene oncology study plan. It compares four workload levels, recommends a primary approach, maps analysis dependencies, and retrieves verified literature to support design choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a single-gene cancer biomarker study
• Compare lightweight and publication-focused study designs
• Build a gene study plan with a defined validation goal

Design phenotype-scoring bioinformatics studies around a disease and a user-defined pathway, process, or molecular signature. Get four workload options, a recommended workflow, validation plan, figures, and literature references that are verified when search is available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a disease signature discovery study
• Designing phenotype scoring and feature selection
• Adding immune or single-cell interpretation

Turn adverse event case details into structured safety narratives for regulatory reporting. The skill checks required fields, organizes the event timeline, supports MedDRA coding and causality assessment, and formats drafts for CIOMS I, ICH E2B R3, or FDA MedWatch.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting an ICSR narrative from an adverse event case
• Checking a case for missing CIOMS I fields
• Organizing drug and event dates into a timeline

Creates structured bulk transcriptome research plans for cancer studies focused on programmed or regulated cell death. It compares four workload levels and recommends a primary design, with analysis steps, figures, validation, a minimal plan, an upgrade path, and verified literature or a transparent search strategy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a ferroptosis study in a cancer type
• Designing a prognostic signature and immune analysis
• Comparing Lite through Publication+ study workloads

Build a structured bioinformatics study plan for non-oncology diseases using bulk transcriptome data and a process-related gene theme. Compare four workload levels and define analysis steps, validation logic, figures, a minimal plan, and a publication upgrade path.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a non-oncology biomarker study from public expression data
• Combining differential expression with a biological gene set
• Designing hub-gene, ROC, or immune analysis workflows