BIOVIA Pipeline Pilot 2026 是一款面向科学与工程行业的强大数据科学平台，通过模块化的数据管道设计，实现端到端的数据驱动与人工智能（AI）工作流程管理。它支持用户将企业中的异构数据统一整合、清洗、建模与部署，借助丰富的内置组件与扩展能力，帮助研发团队更高效地利用数据资产，提升模型可信度与协作效率。特别是其 Machine Learning & Analytics Collection，无需编程即可使用 R、Python 等主流算法库，构建机器学习模型并实现可视化评估与部署。支持多种数据类型（化学、生物、图像、文本等）与第三方工具集成（如 Jupyter、SAS、JMP），为科研人员与工程师提供简洁而专业的 AI 工作流支持。
该平台还配备专为领域用户量身打造的 Collection 工具组，涵盖从化学信息学、序列分析、图像分析到文本挖掘等多场景需求。用户可在无需编码的前提下，通过拖拽方式快速创建流程，实时训练与评估模型，进行交互式多维数据分析（如 PCA、聚类、ROC 图、富集分析等），并通过内置的建模框架进行交叉验证、变量选择与超参数优化。同时，它提供模型适用性分析（MAD）、误差评估与多目标优化支持，适合高质量预测任务。BIOVIA Pipeline Pilot 不仅简化了复杂的数据科学流程，更将 AI 深度集成至整个科研开发生命周期，是企业推进智能化、自动化科学探索的重要平台。
主要优势：
- 普及数据 – 为所有人最大限度地发挥人工智能和机器学习的价值。
- 利用科学知识和专业技术 – 捕获最佳实践标准实践并将其转化为可分发、模块化、可共享的协议。
- 部署数据驱动型研发操作 – 帮助您的团队更智能地工作，而不是更辛苦地工作。
- 支持端到端数据科学工作流程 – 随时随地部署服务。所有这些都在一个工作流程中完成。
- BIOVIA Pipeline Pilot（特别是其系列产品）提供了开箱即用的纵向和横向特定领域功能，支持用户解决从化学信息学到序列分析、从图像分析到文档和文本搜索、从实验室信息学到机器学习和分析等各方面的挑战。
BIOVIA Pipeline Pilot 2026 v26.1.0.1865 x64 激活版下载
x64 | File Size: 10.03 GB
Leveraging BIOVIA Pipeline Pilot and its Collections, users author data pipelines, or protocols in Pipeline Pilot, to deliver integrated, data-driven solutions, and potentially combine them with other BIOVIA applications to augment out-of-the-box capabilities.
Operationalize Data-driven and AI-based Workflows
Data has become ubiquitous. However, many scientific and engineering-based organizations still struggle to effectively utilize the data at their disposal. Teams use different tools and processes to access data, clean it, model it, and deliver results, but these results often lack the domain depth needed to drive innovation. This disjointed, and often too generic approach, to analyze scientific and engineering data and deliver insights lowers trust in results, obstructs progress, and stifles collaboration. To fully benefit from the potential data science offers, organizations need an end-to-end approach to leverage their data across the science and engineering enterprise.
Key Benefits
-Democratize Data – Maximize the value of AI and machine learning for everyone.
-Capitalize Scientific Knowledge and Know-How – Capture best practices standard practices as distributable, modular, shareable protocols.
-Deploy Data-driven R&D Operations – Help your teams work smarter, not harder.
-Support End-to-End Data Science Workflows – Deploy services where they are needed, when they are needed. All in a single workflow.
Purpose-built Solutions for Science and Engineering
Scientists and engineers face different challenges. BIOVIA Pipeline Pilot, and specifically its Collections, offer vertical and horizontal domain-specific capabilities out-of-the-box, supporting users to solve their challenges from cheminformatics to sequence analysis, from image analytics to document and text searching, from lab informatics to machine learning and analytics. Explore our Collections below.
Simplify Your Data Science Workflow
Data comes in all shapes and sizes, yet unlocking actionable insight efficiently requires deep knowledge of data science techniques. BIOVIA Pipeline Pilot Machine Learning and Analytics Collection provides a comprehensive set of machine learning and data modeling capabilities to streamline your data science initiatives.
Analyze data, train and retrain models, and deploy your automated solution to useful enterprise applications.
Developing machine learning solutions often requires complex software architectures and deep statistical knowledge. With BIOVIA Pipeline Pilot Analytics and Machine Learning Collection, developers and end users alike can incorporate the latest machine learning techniques to their workflows with just a few clicks. No coding required.
Key capabilities
-Merge, join, characterize, and clean your data sets
-Apply any of 15+ machine learning (ML) methods to your scientific and engineering data
-Use R-based ML methods such as support vector machines, neural networks, and XGBoost without writing R scripts
-Use Python ML libraries including scikit-learn and TensorFlow
-Rapidly apply statistical analyses
-Use regression and classification model evaluation viewers to assess and compare model test set performance
-Build fast, scalable Bayesian classification models
-Use the GFA method’s genetic algorithm for variable selection and building regression ensemble models
-Build accurate, easy-to-use RP Forest regression and classification models
-Curate model performance
-Deploy model applicability domain (MAD) methods and cross-validation
-Employ the ML framework for cross-validation, hyperparameter tuning, and variable importance assessment for any type of model
-Work flexibly
-Support for 3rd party statistic platforms and tools such as Jupyter Notebook, R, JMP and SAS
-Read in discipline-specific data
-Purpose-built to support various numerical, chemical, biological, textual, and image data types
-Use built-in applicability domain measures and error models to assess sample-specific prediction confidence
-Optimize predictions
-Train multiple trial models in parallel to identify top performers or combine multiple models into a single ensemble model
-Simplify multi-objective optimization
-Employ methods such as Pareto optimization to multi-objective optimization problems
-Visualize results in workflow
-Generate interactive reports with ROC plots, enrichment plots and other visualization techniques
-Perform exploratory analysis, including PCA, clustering, and multi-dimensional data visualization
System Requirements
OS:Windows 10/Windows Server 2019/Windows Server 2016
CPU:Intel-compatible x86_64 architecture
RAM:4 GB per core
Space:70 to 80 GB
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