Results for “filtering”

Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

The English summary is being prepared. Showing the original description.

Review Cisco IOS and IOS-XE configurations, choose read-only troubleshooting commands, and plan checks before and after network changes. Examine traffic filtering rules, interface settings, and configuration saving to avoid overlooked operational risks.

• Review router and switch configurations before a change
• Choose read-only commands for troubleshooting
• Check traffic filtering masks and interface direction

Review JavaScript loops and replace suitable ones with standard array and object methods. Make transformations, filtering, and aggregation easier to understand, with practical explanations and browser checks to confirm that changes preserve the intended behavior.

• Refactoring manual loops into array methods
• Clarifying data transformation, filtering, and aggregation
• Reviewing object transformations and deep copying

Manage AdGuard Home network filtering from the command line. Update blocklists, register devices, configure DNS rules that map names to addresses, and inspect query logs and statistics through the AdGuard Home REST API.

• Adding and refreshing ad-blocking filter lists
• Mapping local domain names to IP addresses
• Registering devices and managing fixed address assignments

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Screen compound libraries for drug discovery using drug-likeness guidelines, structural alerts that flag patterns for review, and molecular complexity measures. Combine criteria to shortlist candidates and retain filtering results with reasons for further assessment.

• Screen compound libraries with drug-likeness rules
• Flag PAINS and other structures for review
• Combine property, structure, and complexity filters

Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.

• Reviewing defenses against prompt injection in AI agents
• Filtering personal information from LLM responses
• Designing authentication and rate limits for LLM APIs

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Configure safety checks for LLM applications with NeMo Guardrails. Covers attempts to bypass safety rules, input and output validation, masking personal information, and checking factual claims through programmable rules and integrations.

• Detecting instructions that try to bypass safety rules
• Masking personal information before processing
• Adding factual checks to generated answers

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Build fuzzy search that filters candidates and sorts them by relevance. Use saved match scores, search across multiple object fields, adjust acceptance thresholds, and connect ranking results to TanStack Table filtering and sorting.

• Sort search results by relevance
• Search names and email addresses together
• Adjust which fuzzy matches are accepted

Add or update Handsontable performance tests for rendering and user interactions. The skill guides trace recording, measurement boundaries, baseline comparisons, and reports that help reviewers identify performance changes in pull requests.

• Add a performance test for a Handsontable feature
• Investigate filtering or sorting performance
• Compare performance changes in a pull request

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.

• Detect attempts to bypass an AI app's safety rules
• Mask personal information before processing
• Add fact-checking to generated answers

Use Meta's Prompt Guard to detect malicious instructions in user prompts and external data. This skill guides input filtering, batch checks for retrieved documents, detection threshold adjustments, and splitting long text for inspection.

• Detecting attempts to override an LLM's instructions
• Checking API responses for embedded malicious instructions
• Batch screening documents retrieved for RAG

Scan recent merges, pull requests, and commits for release note material. Produce structured entries and a summary with PR references, highlighting breaking changes and security items while filtering routine dependency and bot noise.

• Gathering release note material from recent changes
• Identifying breaking changes and security items
• Organizing changes with PR references

Turn “this year so far” into a concrete date range from January 1 through today. Get local calendar dates and precise UTC boundaries to use when filtering records or calculating year-to-date metrics.

• Setting the date range for year-to-date metrics
• Filtering records from January 1 through today
• Checking UTC boundaries for timestamp queries

Run TypeScript type checks when standard commands fail, then filter diagnostics to files involved in your task. Preserve the compiler’s exit status while reviewing relevant errors separately from existing project errors.

• Running type checks when npx tsc fails
• Reviewing diagnostics for newly created files
• Filtering errors to a feature directory

Plan or run DNA analysis to find small genetic changes in tumors, using tumor samples alone or paired with normal tissue. Check sample pairing, contamination, data quality, filtering, and annotations that add information to detected variants.

• Planning analysis of paired tumor and normal samples
• Reviewing filtering and limitations for tumor-only analysis
• Checking quality in whole-genome, exome, or cancer-panel data

Prepare detailed 3D materials that remain clear in close-up views. The skill guides texture resolution, physically based material maps, surface mapping, filtering, and progressive loading while checking download size and GPU memory use.

• Prepare wood, stone, and fabric materials for close-up views
• Fix texture seams, grain direction, and normal map orientation
• Preserve floor and foliage detail at oblique or distant views