Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments
Institute for Biomedical Informatics（生物医学信息学研究所）；University of Pennsylvania（宾夕法尼亚大学）；Biostatistics and Bioinformatics Facility（生物统计学与生物信息学中心）；Fox Chase Cancer Center（福克斯蔡斯癌症中心）；Division of Cancer Epidemiology and Genetics（癌症流行病学与遗传学部门）；National Cancer Institute（美国国家癌症研究所）；Cancer Prevention and Control（癌症预防与控制部门）
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Saarland University（萨尔大学）；Yale Center for Medical Informatics（耶鲁医学信息学中心）；UCL Centre for Artificial Intelligence（伦敦大学学院人工智能中心）
LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification
Institute for Biomedical Informatics（生物医学信息学研究所）；University of Pennsylvania（宾夕法尼亚大学）；National Cancer Institute（国家癌症研究所）；Fox Chase Cancer Center（福克斯蔡斯癌症中心）
Engineering Signals of Human-AI Collaboration in the Agentic Coding Era: A Longitudinal Analysis of 33,228 Pull Requests from vLLM and SGLang with Implications for Biomedical AI Agents and Bioinformatics Pipeline Developmen
Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
TH OWL University of Applied Sciences and Arts（TH OWL应用科学与艺术大学）；Fraunhofer IOSB-INA（弗劳恩霍夫IOSB-INA研究所）；Institute for Industrial Information Technology (inIT)（工业信息技术研究所(inIT)）
