Collaborative Research: FET: Small: Machine Learning Models for Function-on-Function Regression

大型异质特征集在生物学研究中非常常见，例如遗传、转录组、蛋白质组和代谢组信息以及电子健康记录。个性化医学的目标通常是将这些信息与治疗反应联系起来。更高的准确性预测可以帮助为每个患者选择最理想的治疗方法。一些最新的机器学习工具，如基于卷积神经网络的深度学习，在基于图像的预测建模的各个领域显示出了巨大的前景，但往往不适合于涉及生物场景中经常出现的非基于图像的大特征集的场景。该项目开发了一种新的框架，称为RELEARED(将特征表示为具有邻域相关性的图像)，以将高维向量表示为紧凑的图像，从而提高了在此类数据集上训练的机器学习模型的精度，并能够处理异质特征集。这项创新的成功实施将有助于实现从生物数据集进行更高精度的预测建模的目标。开发的算法将以用户友好的方式在网上提供。研究人员深入参与了对下一代各级学生的教育和培训，关注少数群体和代表性不足的群体。该项目涉及设计一种新的回归框架，可以将标量和函数预测器转换为数学上合理的图像对象，可以通过基于卷积网络的深度学习方法进行处理。在生物数据集上的初步结果表明，与现有方法相比，该框架具有更高的预测精度，同时在偏差方面保持了理想的特性。具体的项目贡献涉及(A)用于将高维标量特征表示为具有邻域依赖关系的图像的创新设计，这导致使用基于卷积神经网络的深度学习的高精度预测建模(B)扩展基于图像的表示以将函数变化纳入预测器和输出。该项目还探索了这一新的生物学情景预测建模框架的理论基础。该框架可以应用于任何预测器具有标量、函数和/或图像属性的生物预测问题。该项目的成功完成将为特征表示和函数-函数回归带来新的有效工具，并将成为执行对象回归的重要方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Large heterogeneous feature sets are quite common in biological studies such as genetic, transcriptomic, proteomic and metabolomic information and in electronic health records. The goal of personalized medicine is often to link this information to therapeutic responses. Higher accuracy prediction can assist in selecting the most desirable therapy for each individual patient. Some of the latest machine-learning tools, such as deep learning based on convolutional neural networks, have shown great promise in various areas of image-based predictive modeling but are often unsuitable for scenarios involving non-image based large feature sets that appear quite frequently in biological scenarios. The project develops a novel framework termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to represent high-dimensional vectors as compact images that increases the accuracy of machine-learning models trained on such datasets and is able to handle heterogeneous feature set as well. Successful implementation of the innovation will assist in the goal of higher-accuracy predictive modeling from biological datasets. The developed algorithms will be made available online in a user-friendly manner. Investigators are deeply involved in educating and training the next generation of students at all levels with attention to minority and underrepresented groups.The project involves the design of a novel regression framework that can convert scalar and functional predictors into mathematically justifiable image objects that can be processed by convolutional networks based deep-learning methodologies. Preliminary results illustrated on biological datasets show the higher prediction accuracy of the framework as compared to existing methodologies while maintaining desirable properties in terms of bias. The specific project contributions involve (a) an innovative design for representation of high-dimensional scalar features as images with neighborhood dependencies that results in high accuracy predictive modeling using Convolutional Neural Network based deep learning (b) extension of the image-based representation to incorporate functional changes in predictors and outputs. The project also explores the theoretical underpinnings for this new predictive-modeling framework for biological scenarios. The framework can be applied to any biological-prediction problem where the predictors have scalar, functional and/or image attributes. The successful completion of this project will result in a new effective tool for feature representation and function-on-function regression and will be a significant methodology to perform object regression.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.