Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in preventing obesity and numerous sub-sequent diseases associated with obesity. In this work, we use a rule discovery method to study this problem, by presenting an approach that offers genuine interpretability and concurrently optimizes the accuracy(being correct often) and support (applying to many samples) of the identified patterns. Specifically, we extend an established subgroup-discovery method to generate the desired rules of type X -> Y and show how top features can be extracted from the X side, functioning as the best predictors of Y. In our obesity problem, X refers to the extracted features from very large and multi-site EHR data, and Y indicates significant weight gains. Using our method, we also extensively compare the differences and inequities in patterns across 22 strata determined by the individual's gender, age, race, insurance type, neighborhood type, and income level. Through extensive series of experiments, we show new and complementary findings regarding the predictors of future dangerous weight gains.
A new algorithm for Subgroup Set Discovery based on Information Gain
Centro de Tecnología Biomédica（生物医学技术中心）；Escuela Técnica Superior de Ingenieros Informáticos（高等信息工程学院）；Hospital Universitario Puerta de Hierro Majadahonda（马哈拉翁达铁之门大学医院）
Identifying the Key Attributes in an Unlabeled Event Log for Automated Process Discovery
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所（IHPC））；Nanyang Technological University (NTU)（南洋理工大学）；Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局新加坡制造技术研究院（SIMTech））
University of Quebec in Montreal（魁北克大学蒙特利尔分校）；Concordia University（康考迪亚大学）
Fungal Biosynthetic Gene Clusters (BGCs) of secondary metabolites are clusters of genes capable of producing natural products, compounds that play an important role in the production of a wide variety of bioactive compounds, including antibiotics and pharmaceuticals. Identifying BGCs can lead to the discovery of novel natural products to benefit human health. Previous work has been focused on developing automatic tools to support BGC discovery in plants, fungi, and bacteria. Data-driven methods, as well as probabilistic and supervised learning methods have been explored in identifying BGCs. Mo…
Stanford University（斯坦福大学）；The Harker School（哈克学校）；Technical University of Munich（慕尼黑工业大学）
TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence data. This technical note focuses on version v0.5.6.5. The implementation is available at https://github.com/kundajelab/tfmodisco/tree/v0.5.6.5
Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs
Data Science Center ScaDS.AI, Universität Leipzig（ScaDS.AI数据科学中心，莱比锡大学）；Department of Artificial Intelligence, Fraunhofer Heinrich-Hertz-Institute（人工智能系，弗劳恩霍夫 Heinrich-Hertz 研究所）；Department of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学系，柏林技术大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Centre of eXplainable Artificial Intelligence, Technological University Dublin（可解释人工智能中心，都柏林技术大学）
Stanford University（斯坦福大学）；Cornell University（康奈尔大学）；Joint Center for Artificial Photosynthesis（人工光合作用联合中心）；California Institute of Technology（加州理工学院）
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. In addition, we propose a new pattern decomposition algorithm, called AMIQO, based on solving a sequence of (mixed-integer) quad…
Identifying high-dimensional data patterns without a priori knowledge is an important task of data science. This paper proposes a simple and efficient noparametric algorithm: Data Convert to Sequence Analysis, DCSA, which dynamically explore each point in the feature space without repetition, and a Directed Hamilton Path will be found. Based on the change point analysis theory, The sequence corresponding to the path is cut into several fragments to achieve clustering. The experiments on real-world datasets from different fields with dimensions ranging from 4 to 20531 confirm that the method in…
Local Causal Discovery for Structural Evidence of Direct Discrimination
Cornell Tech（康奈尔理工学院）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Oxford（牛津大学）；Amazon AWS AI/ML（亚马逊云科技人工智能/机器学习部门）；Weill Cornell Medicine（威尔康奈尔医学院）
Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Dhaka, Bangladesh（信息与通信技术系，穆拉纳巴沙尼科学与技术大学）；Department of Computer Science, Tulane University, New Orleans, LA, USA（计算机科学系，杜兰大学）；Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 138632, Singapore（生物信息学研究所（BII），科技研究局（A*STAR））；Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA（杜兰大学生物医学信息学与基因组学中心，医学部）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（Ochsner研究中心，Ochsner研究）
Score-Based Causal Discovery of Latent Variable Causal Models
Carnegie Mellon University（卡内基梅隆大学）；University of California, San Diego（加州大学圣地亚哥分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Columbia University（哥伦比亚大学）；National University of Singapore（新加坡国立大学）；University of Maryland（马里兰大学）
Identifying hidden dynamics from observed data is a significant and challenging task in a wide range of applications. Recently, the combination of linear multistep methods (LMMs) and deep learning has been successfully employed to discover dynamics, whereas a complete convergence analysis of this approach is still under development. In this work, we consider the deep network-based LMMs for the discovery of dynamics. We put forward error estimates for these methods using the approximation property of deep networks. It indicates, for certain families of LMMs, that the $\ell^2$ grid error is boun…
