「prediction」の検索結果

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.

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Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.

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Use when designing ground station networks, scheduling satellite contacts, processing telemetry, commanding spacecraft, or analyzing link budgets. Use when "ground station, satellite contact, pass prediction, AOS, LOS, telemetry, commanding, uplink, downlink, antenna tracking, link budget, G/T, EIRP, " mentioned.

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Patterns for computer-aided drug discovery including virtual screening, molecular docking, ADMET prediction, lead optimization, and integration with AI/ML methods. Covers both structure-based and ligand-based approaches. Use when ", " mentioned.

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Expertise in real-time multiplayer networking, lag compensation, and authoritative server architectureUse when "multiplayer game, netcode, client-server game, P2P networking, lag compensation, rollback networking, game server, matchmaking, lobby system, player synchronization, state replication, dedicated server, authoritative server, tick rate, network prediction, interpolation, extrapolation, GGPO, lockstep, real-time multiplayer, networking, multiplayer, gamedev, realtime, client-server, p2p, netcode, synchronization, lag-compensation, matchmaking" mentioned.

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Build and maintain digital twins - virtual representations of physical systems that synchronize with real-world counterparts for monitoring, prediction, and optimization. Use when "digital twin, virtual model, real-time synchronization, physical-virtual coupling, predictive maintenance, asset modeling, system replica, live simulation, " mentioned.

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Customer retention analysis, churn prediction, cohort analysis, win-back campaigns, and loyalty program design. Use when the user asks about churn, retention, customer lifetime value, cohort analysis, or win-back strategies.

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Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.

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Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

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Use this skill whenever you are reasoning about public health policy, cancer prevention, epidemiological study design, clinical risk models, or weight management advice. This skill channels the thinking of Graham A. Colditz, epidemiologist at Washington University in St. Louis. Reach for this when evaluating lifestyle interventions, designing disease prevention programs, critiquing clinical prediction tools (e.g., AUC vs. clinical utility), or analyzing life-course risk accumulation. Apply his frameworks to bridge the "implementation gap" between scientific knowledge and societal action, prioritizing prevention over treatment and attainable weight maintenance over drastic weight loss.

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Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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Selecting MPC prediction horizon and cost matrices for web handling.

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Use when working with Unreal's Gameplay Ability System - when GAS is worth it, where the AbilitySystemComponent lives and how it is initialised on server and client, AttributeSets with correct clamping and replication, GameplayEffects and execution calculations as data, abilities with commit/end, gameplay tags, prediction, and testing.

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Use when touching multiplayer code - authoritative server, client prediction and reconciliation, interpolation, lockstep and rollback, server-side validation of every RPC per engine (Netcode, Unreal replication, Godot multiplayer, Colyseus, Bevy crates), bandwidth and versioned messages.

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Use when a model is served online (FastAPI, BentoML) or scored in batch, when changing how a model artifact is loaded or versioned, or when adding drift, quality or prediction monitoring and a rollout plan for a new model

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Used to invoke the PXMeter tool for rigorous quality assessment of biomolecular structure prediction models (e.g., proteins, nucleic acids, small molecules). This skill covers single-sample CIF evaluations and large-scale dataset benchmarks, providing rich result parsing support. Trigger this skill when the user asks to "run PXMeter", "compare PDB/CIF evaluation results of different tools", "check benchmark scores", or asks "how accurate is the structure generated by the model".

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Superforecasting with calibrated reasoning, Brier score tracking, and prediction ledger management

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Builds and backtests demand and revenue forecasts: rolling-origin backtests, prediction intervals, reconciliation, zero-shot foundation models. Use when forecasting time series.

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Submit protein folding tasks to the AlphaFold3 C550 inference platform. Use when the user needs to run AlphaFold3 predictions with pre-prepared JSON input files on the AI4S Kubernetes cluster.

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Build a forecasting model for a time series — demand, revenue, or usage prediction. Use when asked to "forecast demand", "predict next quarter revenue", or "build a time series model".

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Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".

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Cheminformatics toolkit catalog for small molecules. Covers RDKit/OpenChemLib calculations, descriptors, fingerprints, similarity/substructure search, R-group, MMP, scaffold tree, chemical space, MPO, reactions, and ADMET predictions. Open this skill when the user asks about computing molecular properties, comparing/searching molecules, or predicting ADMET/tox.

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MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.

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。