'prediction' 검색 결과

Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage.

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Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".

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Audit legal case prediction systems for bias, fairness, accuracy, and ethical guardrails. Use when: 'check my prediction model for bias', 'audit case outcome fairness', 'evaluate legal ML model', 'review sentencing prediction ethics', 'analyze bail risk algorithm', 'fairness metrics for justice system AI'.

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Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer.

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Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.

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Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.

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Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.

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공통 과제로 대규모 언어 모델의 지식·추론·코드 생성을 평가합니다. 모델 비교표와 학습 단계별 성적 추이를 만들고 평가 결과와 개별 답변을 저장합니다.

• MMLU와 GSM8K로 모델 평가
• 여러 모델의 성적 비교표 작성
• 학습 중 모델의 성적 추적

클라우드 GPU로 머신러닝 학습과 추론을 실행하고 모델을 API로 공개하도록 돕습니다. Python으로 실행 환경을 정의하고 병렬 처리와 정기 실행도 설정합니다.

• GPU 서버 관리 없이 모델 학습
• 자동 확장되는 API로 모델 공개
• 대량 데이터 병렬 처리

Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.

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• Designing models for classification or time series
• Coordinating training across cloud environments
• Comparing model performance and tuning training

Scan selected market symbols for unusual price movements and rank potential trading signals. Combine anomaly scores, measures of departure from normal behavior, with strategy filters, historical pattern matches, and predictions about changing market conditions.

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• Scan selected symbols for unusual price movements
• Filter trading signals by a saved strategy
• Compare detected anomalies with historical patterns

Explain a trading model's prediction by ranking the input features that contributed to its signal. Returns a Markdown table and saves an attribution record with calculation settings, adding a digital signature when a signing key is available.

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• Reviewing which inputs drove a trading signal
• Saving signed records of model explanations
• Reproducing rankings with the same signal and seed

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

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• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Simulate lithium-ion battery charging, discharging, and rest with PyBaMM. Track voltage and capacity, record parameter sources, check how numerical settings affect predictions, and compare simulated curves with measured cycling data.

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• Predicting voltage changes during charge, discharge, and rest
• Comparing simulation results with measured cycling data
• Checking how numerical settings affect predictions

Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.

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• Predicting properties across a compound library
• Exploring molecular shapes and tautomeric forms
• Estimating how compounds bind to proteins

Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.

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• Estimate regression effects with uncertainty
• Model differences across patients or groups
• Inspect sampling diagnostics and predictive checks

Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.

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• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

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.

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

Plan and audit HypoGeniC workflows that use language models to propose patterns from labeled text. Check configurations, dataset splits, cost limits, and saved predictions while keeping candidate hypotheses separate from scientific confirmation.

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• Reviewing task settings before a HypoGeniC run
• Checking duplicates across training and test data
• Estimating token and cost limits before model calls

Predict spatial gene expression from H&E-stained tissue images using DeepSpot-M. Query a released panel of about 19,000 genes and combine predictions from small image tiles into slide-wide maps for research analysis.

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• Mapping predicted marker-gene expression across a tumor section
• Predicting expression across slides without matching gene-expression assays
• Adding predicted expression to a histology image analysis pipeline

Develop AxonX plugins and manage quantitative finance research through the command line or MCP, an agent connection interface. Track data preparation, training, prediction, and historical strategy tests using execution status, logs, artifacts, and dependencies.

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• Develop research plugins with defined inputs and outputs
• Run data preparation, training, prediction, and backtests
• Investigate failed tasks through status and logs

Design subject lessons that also teach students how to learn. Add recall practice, explanations in their own words, predictions, and reflection to help learners check their understanding and plan later review while studying the main concepts.

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• Adding recall practice to subject lessons
• Having students explain concepts in their own words
• Designing feedback and retries after mistakes

Design and implement machine learning systems for production use. Cover data preparation, training, prediction services, model comparisons, monitoring, and retraining, with attention to reliability, response time, resource costs, and both technical and business evaluation metrics.

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• Designing a real-time recommendation service
• Comparing model versions with A/B testing
• Detecting changes in data and model performance

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

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• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions