bio-immunoinformatics-tcr-epitope-binding：预测 TCR-epitope specificity ，使用 ERGO-II 、 深度学习 models ，用于 T-cell receptor antig…
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训练与评测
SC
scikit-survival
维护者 K-Dense Inc. · 最近更新 2026年4月1日
scikit-survival是一个Python 库 ，用于 survival analysis built on top of scikit-learn。 It provides speciali。
原始来源
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/scikit-survival
- 维护者
- K-Dense Inc.
- 许可
- GPL-3.0 license
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- scikit-survival是一个Python 库 ，用于 survival analysis built on top of scikit-learn. It provides specialized tools ，用于 time-to-event analysis，handling unique challenge of censored data where some observations are only partially known。
- Survival analysis aims to establish connections between covariates 、 time of event，accounting ，用于 censored records (particularly right-censored data ，面向 studies where participants don't experience events during observation periods)。
- y = Surv.from_arrays(event=event_array，time=time_array)。
原始文档
SKILL.md 摘录
When to Use This Skill
Use this skill when:
- Performing survival analysis or time-to-event modeling
- Working with censored data (right-censored, left-censored, or interval-censored)
- Fitting Cox proportional hazards models (standard or penalized)
- Building ensemble survival models (Random Survival Forests, Gradient Boosting)
- Training Survival Support Vector Machines
- Evaluating survival model performance (concordance index, Brier score, time-dependent AUC)
- Estimating Kaplan-Meier or Nelson-Aalen curves
- Analyzing competing risks
- Preprocessing survival data or handling missing values in survival datasets
- Conducting any analysis using the scikit-survival library
1. Model Types and Selection
scikit-survival provides multiple model families, each suited for different scenarios:
Cox Proportional Hazards Models
Use for: Standard survival analysis with interpretable coefficients
CoxPHSurvivalAnalysis: Basic Cox modelCoxnetSurvivalAnalysis: Penalized Cox with elastic net for high-dimensional dataIPCRidge: Ridge regression for accelerated failure time models
See: references/cox-models.md for detailed guidance on Cox models, regularization, and interpretation
适用场景
- Performing survival analysis 或 time-to-event modeling。
- Working ，支持 censored data (right-censored，left-censored，或 interval-censored)。
- Fitting Cox proportional ha。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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