4,161 件 ・ 関連度順
概要と使いどころ
Python library for biology: sequence manipulation (DNA/RNA/protein), pairwise/multiple alignment, phylogenetic trees (NJ, UPGMA), diversity (Shannon, Faith PD, Bray-Curtis, UniFrac), ordination (PCoA, CCA, RDA), stats (PERMANOVA, ANOSIM, Mantel), file I/O (FASTA, FASTQ, Newick, BIOM). Use for microbiome, community ecology, or phylogenetics.
日本語の概要は準備中です。原文の説明を表示しています。
Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.
日本語の概要は準備中です。原文の説明を表示しています。
Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.
日本語の概要は準備中です。原文の説明を表示しています。
MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
日本語の概要は準備中です。原文の説明を表示しています。
Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.
日本語の概要は準備中です。原文の説明を表示しています。
Protocols and best practices for western blot quantification and analysis including band detection, normalization, and statistical methods.
日本語の概要は準備中です。原文の説明を表示しています。
Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib for publication figures.
日本語の概要は準備中です。原文の説明を表示しています。
protocols.io REST API: search and fetch wet-lab, bioinformatics, and clinical protocols by keyword, DOI, or category, with steps, reagents, materials, equipment, timing. Public access free; auth needed for private or publishing. Pair with opentrons-protocol-api or benchling-integration to execute.
日本語の概要は準備中です。原文の説明を表示しています。
Benchling R&D Python SDK: CRUD on registry entities (DNA, RNA, proteins, custom), inventory, ELN, workflow automation. Needs Benchling account and API key. Use biopython for local sequence analysis; pubchem for chemical DBs.
日本語の概要は準備中です。原文の説明を表示しています。
Three-tiered sgRNA design guide using validated Addgene sequences, CRISPick pre-computed datasets, or de novo design rules for CRISPR experiments
日本語の概要は準備中です。原文の説明を表示しています。
Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
日本語の概要は準備中です。原文の説明を表示しています。
Hardware-agnostic Python liquid-handler library: portable scripts run on Hamilton STAR, Tecan Freedom EVO, Opentrons OT-2, or a simulator without vendor lock-in. For protocol automation, method dev, plate reformatting, serial dilutions, and Python lab workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
日本語の概要は準備中です。原文の説明を表示しています。
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
日本語の概要は準備中です。原文の説明を表示しています。
Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.
日本語の概要は準備中です。原文の説明を表示しています。
Open-source bio-image data management. Use the omero-py client to connect to an OMERO server, retrieve images as numpy arrays, annotate with tags and key-value pairs, manage ROIs, and feed image data into Python analysis pipelines — programmatically, no GUI.
日本語の概要は準備中です。原文の説明を表示しています。
Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.
日本語の概要は準備中です。原文の説明を表示しています。
3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.
日本語の概要は準備中です。原文の説明を表示しています。
Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.
日本語の概要は準備中です。原文の説明を表示しています。
Best practices, design patterns, and common pitfalls for ROS1 (Robot Operating System 1) development. Use this skill when building ROS1 nodes, packages, launch files, or debugging ROS1 systems. Trigger whenever the user mentions ROS1, catkin, rospy, roscpp, roslaunch, roscore, rostopic, tf, actionlib, message types, services, or any ROS1-era robotics middleware. Also trigger for migrating ROS1 code to ROS2, maintaining legacy ROS1 systems, or building ROS1-ROS2 bridges. Covers catkin workspaces, nodelets, dynamic reconfigure, pluginlib, and the full ROS1 ecosystem.
日本語の概要は準備中です。原文の説明を表示しています。
Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for perception, and dev-vs-deploy container patterns. Use this skill when containerizing ROS2 workspaces, setting up docker-compose for robot software stacks, debugging DDS communication between containers, configuring NVIDIA Container Toolkit for GPU workloads, forwarding X11/Wayland for rviz2 and GUI tools, or managing USB device passthrough for cameras and serial devices. Trigger whenever the user mentions Docker with ROS2, docker-compose for robots, Dockerfile for colcon workspaces, container networking for DDS, devcontainer for ROS2, multi-stage builds for ROS2, or deploying ROS2 in containers. Also trigger for CI/CD with Docker-based ROS2 builds, CycloneDDS or FastDDS configuration in containers, or shared memory in Docker. Covers Humble, Jazzy, and Rolling distributions across Ubuntu 22.04 and 24.04 base images.
日本語の概要は準備中です。原文の説明を表示しています。
Bringing up a complete ROS2 system on a robot's onboard computer: systemd services, launch file composition, ordered startup, and production monitoring. Use this skill when configuring a robot to start ROS2 nodes on boot, writing systemd unit files for ROS2 launch, composing layered launch files for full robot stacks, setting up watchdog monitoring, configuring udev rules for deterministic device naming, or debugging boot-time race conditions. Trigger whenever the user mentions robot bringup, robot startup, systemd for ROS2, ROS2 on boot, launch file composition, robot boot sequence, udev rules for cameras or serial ports, automatic restart for ROS2 nodes, network configuration for multi-machine ROS2, log rotation for robots, graceful shutdown of robot stacks, or SSH-based remote debugging. Also trigger for sourcing workspaces in systemd, ordered startup with health checks, or running ROS2 systems as long-running production services. Covers systemd on Ubuntu 22.04/24.04 with ROS2 Humble, Iron, and Jazzy.
日本語の概要は準備中です。原文の説明を表示しています。
Architecture patterns, design principles, and proven recipes for building robust robotics software. Use this skill when designing robot software architectures, choosing between behavioral frameworks, structuring perception-planning-control pipelines, implementing state machines, designing safety systems, or architecting multi-robot systems. Trigger whenever the user mentions behavior trees, finite state machines, subsumption architecture, sensor fusion, robot safety, watchdogs, heartbeats, graceful degradation, hardware abstraction layers, real-time constraints, or software architecture for robots. Also applies to sim-to-real transfer, digital twins, and robot fleet management.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and best practices for integrating ROS2 systems with web technologies including REST APIs, WebSocket bridges, and browser-based robot interfaces. Use this skill when building web dashboards for robots, streaming camera feeds to browsers, exposing ROS2 services as REST endpoints, or implementing bidirectional WebSocket communication between web UIs and ROS2 nodes. Trigger whenever the user mentions rosbridge, rosbridge_suite, roslibjs, FastAPI with ROS2, Flask with rclpy, WebSocket for robot telemetry, MJPEG streaming, WebRTC for robots, REST API wrapping ROS2 services, web-based robot control, browser robot interface, robot dashboard, CORS configuration for robots, or any web-to-ROS2 bridge pattern. Also trigger for authentication on robot web interfaces, rate limiting sensor streams, video streaming from robot cameras to browsers, or running async web frameworks alongside the ROS2 executor. Covers rosbridge_suite, FastAPI, Flask, WebSocket, and WebRTC approaches.
日本語の概要は準備中です。原文の説明を表示しています。