Data Analysis

k-dense-ai Bundle

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

k-dense-ai Bundle

Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.

k-dense-ai Bundle

Fit statistical models (OLS, GLM, ARIMA, mixed models) with detailed diagnostics, residuals, and inference for econometrics and time series analysis.

k-dense-ai Bundle

Store and process large N-dimensional arrays with chunking, compression, and parallel I/O, integrating with NumPy, Dask, and Xarray for cloud-native scientific computing.

k-dense-ai Bundle

Search the web, extract URL content, enrich datasets with web-sourced fields, and run deep research reports, prioritizing academic and scientific sources.

k-dense-ai Bundle

Build and analyze phylogenetic trees using MAFFT, IQ-TREE 2, and FastTree, with visualization via ETE3 or FigTree for evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies.

k-dense-ai Bundle

Query documented public database APIs with explicit endpoints, filters, pagination, and provenance for reproducible retrieval of scientific, regulatory, or financial facts.

k-dense-ai Bundle

Perform survival analysis and time-to-event modeling in Python using scikit-survival, including Cox models, random survival forests, gradient boosting, survival SVMs, and evaluation metrics like concordance index and Brier score.

k-dense-ai Bundle

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.

k-dense-ai Bundle

GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.

k-dense-ai Bundle

Access microscopy images and metadata via the OMERO Python API: retrieve datasets, analyze pixels, manage ROIs and annotations, and batch-process for high-content screening workflows.

k-dense-ai Bundle

Calculate sample sizes, minimum detectable effects, and power curves for study planning using closed-form formulas or Monte Carlo simulation.

k-dense-ai Bundle

Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).

k-dense-ai Bundle

Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Covers over-representation analysis (ORA), Gene Set Enrichment Analysis (GSEA), and single-sample scoring using gseapy, g:Profiler, and Enrichr libraries.

k-dense-ai Bundle

Forecast any univariate time series (sales, sensors, energy, vitals, weather) zero-shot using Google's TimesFM foundation model, with point forecasts and prediction intervals from CSV, DataFrame, or array inputs.

k-dense-ai Bundle

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface, covering loading, preprocessing, drift correction, spike sorting, quality metrics, and unit curation.

k-dense-ai Bundle

Guides statistical hypothesis testing with assumption checks, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting for research data.

k-dense-ai Bundle

Detects available CPU, GPU, memory, and disk resources and generates strategic recommendations for scientific computing tasks.

k-dense-ai Bundle

Generate comprehensive, professional-grade market research reports (50+ pages) with LaTeX formatting, strategic frameworks, and data-driven visualizations.

k-dense-ai Bundle

Generate professional clinical decision support documents for pharmaceutical and clinical research, including biomarker-stratified cohort analyses and evidence-based treatment recommendation reports with GRADE grading, statistical analysis, and publication-ready LaTeX/PDF output.

k-dense-ai Bundle

Automatically detect and analyze scientific data files across 200+ formats, generating detailed markdown reports with quality metrics and analysis recommendations.

mukul975 Bundle

Analyze proxy logs, DNS query logs, and netflow data to detect unauthorized SaaS and cloud service usage, classify domains, and flag high-risk services.

mukul975 Bundle

Examine Linux system artifacts including auth logs, cron jobs, shell history, and system configuration to uncover evidence of compromise or unauthorized activity.

mukul975 Bundle

Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.

mukul975 Bundle

Detect anomalies in DNP3 protocol communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

mukul975 Bundle

Analyzes mobile applications for malicious behavior through static analysis, runtime monitoring, and network traffic inspection to identify malware indicators.

mukul975 Bundle

Detect Kerberos Pass-the-Ticket attacks by analyzing Windows Event IDs 4768, 4769, and 4771 for anomalous ticket usage patterns in Splunk and Elastic SIEM.

mukul975 Bundle

Detect Cobalt Strike beacon network activity using TLS certificate signatures, JA3/JA3S/JARM fingerprints, HTTP C2 profile matching, beacon jitter analysis, and named pipe detection via Zeek, Suricata, and Python PCAP analysis.

mukul975 Bundle

Reduce SIEM alert fatigue by systematically tuning detection rules in Splunk and Elastic, using statistical baselines, whitelists, and precision/recall metrics.

mukul975 Bundle

Detects cyber attacks targeting SCADA systems, including man-in-the-middle, command injection, HMI compromise, historian manipulation, and DoS, using OT-specific intrusion detection and protocol anomaly analysis.

mukul975 Bundle

Detect insider threats by modeling normal user and entity behavior with Elasticsearch, computing anomaly scores, and correlating low-confidence indicators into high-confidence alerts.

mukul975 Bundle

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

mukul975 Bundle

Detect and extract hidden data embedded in images, audio, and other media files using steganalysis tools to uncover covert communication channels.

mukul975 Bundle

Detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis.

mukul975 Bundle

Build forensic super timelines from disk images using Plaso (log2timeline) and triage them in Timesketch.

mukul975 Bundle

Detects anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems using Zeek, Suricata, and custom Python analysis.