Results for “analyse”

Explore gene, drug, disease, and phenotype associations in a downloaded PrimeKG CSV. Retrieve direct relationships, disease summaries, and paths through one intermediate node while preserving relationship types and data provenance for reproducible research and hypothesis generation.

• Checking associations between genes, drugs, and diseases
• Summarizing genes, drugs, and phenotypes linked to a disease
• Exploring short paths between two entities

Process mass spectrometry data using scripts for format conversion, signal cleanup, peak detection, and quantification across samples. Prepare protein and metabolite analyses with identification filtering, candidate annotations, data tables, and plots.

• Inspect, convert, and preprocess mass spectrometry files
• Build quantitative tables across multiple samples
• Find metabolite candidates from accurate mass

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Analyze DepMap cancer model data to rank genes required for cell survival, compare groups defined by biological markers, and explore drug responses. Release tracking and statistical checks help organize evidence for research hypotheses.

• Ranking gene dependencies in cancer models
• Comparing groups defined by biological markers
• Finding genes with similar dependency patterns

Review and improve statistical reporting in manuscript methods, results, and figure captions. It checks sample definitions, repeated measurements, tests, and uncertainty, then provides revised wording and identifies missing information or issues reviewers may raise.

• Reviewing a manuscript’s statistical methods section
• Checking sample sizes and repeated measurements
• Aligning figure captions with reported statistics

Build Python text analysis with Azure AI Language to detect sentiment, names, key phrases, languages, and personal information. Examples also cover healthcare terminology extraction, combined analyses, authentication, and handling results for multiple documents.

• Analyzing sentiment about specific aspects of a review
• Detecting and redacting personal information
• Extracting names and key phrases from text

Build an Excel comparison of public companies using financial performance, valuation ratios, and statistical benchmarks. The spreadsheet includes formulas, source notes, medians, and quartiles to show how each company compares with its peers.

• Comparing public company valuations for M&A
• Benchmarking performance against industry peers
• Identifying unusually high or low valuation ratios

Organizes compliance work and audit preparation by tracking controls, responsible people, evidence, and deadlines. Produces status dashboards, gap analyses, audit checklists, and evidence collection plans for frameworks such as SOC 2 and ISO 27001.

• Preparing a checklist for an upcoming audit
• Comparing current practices with framework requirements
• Organizing evidence owners and collection dates

Ask data questions in everyday language, from checking a single metric to investigating trends or preparing a formal report. Receive findings supported by queries, tables, or charts, with data validation and clear limitations.

• Check new user signups for a specific month
• Investigate a decline in conversion rates
• Compare support tickets by category and priority

Explore a table or data file before starting analysis. Summarize its structure, missing values, duplicates, and distributions, flag potential quality issues, and suggest useful measures, grouping columns, and follow-up analyses.

• Understanding an unfamiliar table or data file
• Checking missing values, duplicates, and suspicious entries
• Choosing measures and grouping columns for analysis

Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.

• Forecasting sprint workload from past delivery data
• Assessing team health and prioritizing improvements
• Tracking retrospective actions and recurring themes

Research a listed company's business quality and valuation using official filings, comparisons with similar companies, and historical performance. Produce an evidence-backed report and scorecard, with separate modes for accounting concerns and companies preparing for an IPO.

• Compare a company's financial strength with sector peers
• Investigate gaps between reported profit and cash flow
• Assess assumptions built into a stock's current price

Turn company strategy into operational plans through process design, OKRs—objectives paired with measurable results—and regular reviews. Produce bottleneck analyses, efficiency scorecards, meeting schedules, and readiness reports for teams preparing to grow.

• Connecting company goals to department and team OKRs
• Finding process bottlenecks and planning improvements
• Assessing operational readiness for team growth

Analyze bug-fix commits in a Git repository to uncover recurring failure patterns. Produce individual bug analyses and a reusable pattern guide that removes project-specific details, helping inform future code reviews.

• Extract recurring bug patterns from past fixes
• Build a reusable guide for code reviews
• Analyze fixes within a chosen date range or path

Analyze a long novel through its opening chapters, chapter summaries, plot causality, reader emotions, pacing, relationships, settings, and prose style. Produce reusable writing references, continue partial analyses, or fill gaps in existing reports using available evidence.

• Studying how a novel's first three chapters engage readers
• Mapping plot causality, timelines, and character relationships
• Examining emotional effects, pacing, and prose style

Turn broad life sciences questions into focused research plans. Resolve names and identifiers for genes, diseases, or compounds, select relevant research skills, and combine evidence into a concise answer that explains uncertainty and useful follow-up analyses.

• Reviewing evidence linking a gene to a disease
• Combining evidence about a variant and its associated traits
• Researching protein structure, function, and drug interactions

Plan DNA variant detection workflows by identifying the assay and sample model, then routing to germline, cancer somatic, or molecular-barcode panel skills. It also supports focused local BAM/CRAM checks with variant files, depth summaries, and optional filters.

• Choosing a workflow for WGS, WES, or DNA panels
• Routing inherited, cancer, and molecular-barcode analyses
• Checking reference genomes and required input files

Plan or run bulk RNA-seq analysis to find genes whose expression differs between conditions. Check sample replication, study design, batch effects, and data quality, then produce comparison tables, diagnostic plots, and ranked gene lists.

• Comparing gene expression between experimental conditions
• Planning analyses with donor pairing or batch variables
• Checking sample quality with PCA and distance plots

Review reports, spreadsheets, dashboards, or other analyses before sharing them or making decisions. Check methods, calculations, charts, and conclusions, then receive a readiness assessment with prioritized fixes, supporting evidence, and limitations that readers need to know.

• Checking a report before sharing it
• Verifying key metrics and comparison periods
• Reviewing charts for misleading presentation

Create and maintain a reusable skill that records metric definitions, trusted data sources, and analytical caveats. Preserve supporting evidence and unresolved conflicts so future Data Analytics work can consult and cite the same documented context.

• Save shared metric definitions for future analyses
• Document trusted sources and conflicting definitions
• Update or repair an existing semantic layer

Learn from top Kaggle solutions across text, images, time series, tabular data, and multimodal tasks that combine data types. Explore technical analyses, model settings, reusable code templates, and practical lessons to inform machine learning projects.

• Study top solutions for a Kaggle competition
• Find proven methods for a machine learning task
• Explore reusable machine learning code templates

Edit English and Chinese prose to reduce formulaic AI writing patterns, including filler, inflated claims, and vague attribution. Preserves the original meaning and intended tone while improving sentence variety, directness, and the writer’s voice.

• Rewriting formulaic AI-generated prose
• Checking English or Chinese text before publication
• Polishing academic and professional writing

Analyze ML and AI experiment results with statistical checks, confidence intervals, and effect sizes, which describe the size of differences. Produce scientific figures and analysis reports while identifying missing evidence and limits on model comparison claims.

• Comparing model performance across repeated runs
• Checking significance and the size of performance differences
• Examining how removing model components affects results

Review a paper’s complete figure set against its manuscript and captions. Develop a supported central claim, propose figure ordering and content changes, and pass human-reviewed revisions to figure-composer while preserving figures that need no changes.

• Reviewing the narrative across a paper’s full figure set
• Strengthening Figure 1 within the available evidence
• Identifying panel moves, missing analyses, and supplement material