Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '20), September 21-24, 2020, Virtual Event, USA
We present an end-to-end, interpretable, deep-learning architecture to learn a graph kernel that predicts the outcome of chronic disease drug prescription. This is achieved through a deep metric learning collaborative with a Support Vector Machine objective using a graphical representation of Electronic Health Records. We formulate the predictive model as a binary graph classification problem with an adaptive learned graph kernel through novel cross-global attention node matching between patient graphs, simultaneously computing on multiple graphs without training pair or triplet generation. Results using the Taiwanese National Health Insurance Research Database demonstrate that our approach outperforms current start-of-the-art models both in terms of accuracy and interpretability.
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所）；Aalto University（阿尔托大学）；Institute for Molecular Medicine Finland FIMM（芬兰分子医学研究所）；University of Helsinki（赫尔辛基大学）；Science for Life Laboratory（生命科学实验室）；Karolinska Institutet（卡罗林斯卡学院）；University of Turku（图尔库大学）
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学国家健康医疗大数据研究院）；School of Computer Science, Nanjing Normal University（南京师范大学计算机科学与技术学院）；Department of Radiology and BRIC, UNC Chapel Hill（北卡罗来纳大学教堂山分校放射学系与生物医学研究成像中心）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Nanjing Medical University（南京医科大学）；Nanjing Brain Hospital（南京脑科医院）；Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司研发部）；Department of Brain and Cognitive Engineering, Korea University（高丽大学脑与认知工程系）
An Offline Smartphone Check for Drug Interactions and Past Reactions in Handwritten Prescriptions: A Technical Feasibility Study in Rural Bangladeshi Outpatient Care
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Tulane Center for Biomedical Informatics and Genomics（杜兰大学生物医学信息学与基因组学中心）；Division of Biomedical Informatics and Genomics（生物医学信息学与基因组学部门）；Deming Department of Medicine（戴明医学系）；Tulane University（杜兰大学）
Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
Qilu University of Technology（齐鲁工业大学）；University of Nottingham（诺丁汉大学）
药品不良反应（Adverse Drug Reaction，ADR）是一个备受关注的公共卫生问题。药品不良反应是导致药品退出市场的最常见原因之一。处方事件监测是发现药品不良反应的重要手段。该方法的主要问题是如何从临床实践中收集的高通量医疗事件中自动提取医疗事件或副作用。在这项研究中，我们提出了一个新的概念的特征矩阵检测的ADR。从THIN数据库的大数据中提取特征矩阵，用于描述吸毒患者的医疗事件。特征矩阵为不规则、大规模的医学数据奠定了基础。然后对特征矩阵进行特征选择，检测出显著特征。最后，根据显著特征对不良反应进行定位。实验在三种药物上进行：阿托伐他汀、阿仑膦酸钠和甲氧氯普胺。每种药物的主要副作用被检测到，并且与其他计算机化方法相比，实现了更好的性能。检测到的不良反应是基于计算机方法，需要进一步调查。
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）
Adaptive kernel predictors from feature-learning infinite limits of neural networks
John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保罗森工程与应用科学学校）；Center for Brain Sciences（脑科学中心）；Kempner Institute（凯普纳研究所）
Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy
Kashani Comprehensive Epilepsy Center, Kashani Hospital（卡沙尼综合癫痫中心，卡沙尼医院）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Staffordshire University（斯塔福德郡大学）
Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
