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机器学习作为人工智能的一个分支，为分析大规模组学数据集提供了强大的工具。通过利用这些算法，研究人员可以揭示组学数据中隐藏的模式，促进个性化医疗、药物发现以及我们对基本生物过程理解的进步。
Machine learning, as a branch of artificial intelligence, provides robust tools for analyzing large-scale omics datasets. By leveraging these algorithms, researchers can uncover hidden patterns within omics data, facilitating advancements in personalized medicine, drug discovery, and our comprehension of fundamental biological processes.
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定义：通过使用带标签的数据集训练模型，使其学习输入特征与输出标签之间的映射关系，从而能够对新的未标记数据进行准确的预测或分类，常应用于分类和回归问题。
Supervised learning, a key AI concept, involves training models on labeled data to learn input-output relationships. This enables accurate predictions on new data, widely used for classification and regression.
定义：利用未标记的数据集让模型自主探索和发现数据中的模式或结构，如将数据进行分组或简化数据的维度，常用于聚类分析和降维。
Unsupervised learning, a key AI concept, trains models on unlabeled data to find patterns autonomously. It includes cluster analysis for grouping similar data and dimensionality reduction to simplify complex datasets.
定义： 结合了少量标记数据和大量未标记数据进行训练，通常在标记数据难以获得但未标记数据丰富的情况下使用。
应用： 图像分类 (用少量标记图像和大量未标记图像提高模型性能)。
Semi-supervised learning is an AI technique that combines a small amount of labeled data with a large volume of unlabeled data during model training. This approach is particularly effective when labeled data is scarce but unlabeled data is plentiful. Example: In image classification tasks, semi-supervised learning can significantly enhance model performance by utilizing a limited set of labeled images alongside a vast collection of unlabeled images.
定义：模型通过与环境交互来学习策略。它通过奖惩机制不断优化决策，以实现最大化长期收益。
应用：如国际象棋（AlphaGo）、自动驾驶、机器人控制。
Reinforcement learning is a machine learning method where models learn optimal strategies through environment interactions, using rewards and penalties to improve decision-making. This technique has proven effective in diverse fields, including game AI (e.g., AlphaGo), self-driving cars, adaptive robotics.
定义：通过从数据中生成部分标签来进行自我监督，常用于自然语言处理和计算机视觉任务。
应用：自然语言处理 (如GPT模型)、图像生成和理解。
Self-supervised learning is an advanced AI technique that allows models to learn from unlabeled data without human annotation. By utilizing inherent dataset structures, it generates supervised learning signals across various domains. This approach powers large language models in NLP, enhances image understanding in Computer Vision, improves Speech Recognition, and boosts machine adaptability in Robotics.
早期检测和筛查
分类
诊断
预后预测
治疗反应预测
机器学习分析图例
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