CODE-AE: A Coherent De-confounding Autoencoder for Predicting Patient-Specific Drug Response From Cell Line Transcriptomics
Graduate Center, City University of New York（纽约城市大学研究生中心）；Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University（康奈尔大学威尔康奈尔医学院菲尔家族脑与心智研究所）；Hunter College, City University of New York（纽约城市大学亨特学院）
Accurate and robust prediction of patient's response to drug treatments is critical for developing precision medicine. However, it is often difficult to obtain a sufficient amount of coherent drug response data from patients directly for training a generalized machine learning model. Although the utilization of rich cell line data provides an alternative solution, it is challenging to transfer the knowledge obtained from cell lines to patients due to various confounding factors. Few existing transfer learning methods can reliably disentangle common intrinsic biological signals from confounding factors in the cell line and patient data. In this paper, we develop a Coherent Deconfounding Autoencoder (CODE-AE) that can extract both common biological signals shared by incoherent samples and private representations unique to each data set, transfer knowledge learned from cell line data to tissue data, and separate confounding factors from them. Extensive studies on multiple data sets demonstrate that CODE-AE significantly improves the accuracy and robustness over state-of-the-art methods in both predicting patient drug response and de-confounding biological signals. Thus, CODE-AE provides a useful framework to take advantage of in vitro omics data for developing generalized patient predictive models. The source code is available at https://github.com/XieResearchGroup/CODE-AE.
In this work we present a deep learning approach to conduct hypothesis-free, transcriptomics-based matching of drugs for diseases. Our proposed neural network architecture is trained on approved drug-disease indications, taking as input the relevant disease and drug differential gene expression profiles, and learns to identify novel indications. We assemble an evaluation dataset of disease-drug indications spanning 68 diseases and evaluate in silico our approach against the most widely used transcriptomics-based matching baselines, CMap and the Characteristic Direction. Our results show a more…
Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations
Interdisciplinary Program in Bioinformatics, Seoul National University（首尔国立大学生物信息学跨学科项目）；AIGENDRUG Co., Ltd.（爱真药物有限公司）；BK21 FOUR Intelligence Computing, Seoul National University（首尔国立大学BK21四号智能计算）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Department of Artificial Intelligence, Inha University（仁荷大学人工智能系）
Helmholtz Center for Computational Health（亥姆霍兹计算健康中心）；Technical University of Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Wellcome Sanger Institute（威康桑格研究所）
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully. We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model …
IBM Research Zurich（IBM苏黎世研究中心）；ETH Zurich（苏黎世联邦理工学院）
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and properties of the target disease. Here, we introduce the first generative model capable of tailoring anticancer compounds for a specific biomolecular profile. Using a RL framework, the transcriptomic profiles of cancer cells are used as a context for the generation of candidate molec…
Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学先进交叉学科学院）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学人工智能学院）；National University of Singapore, Singapore（新加坡国立大学）
Georgia Institute of Technology（佐治亚理工学院）；IQVIA（艾昆纬）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Counterfactual prediction is about predicting outcome of the unobserved situation from the data. For example, given patient is on drug A, what would be the outcome if she switch to drug B. Most of existing works focus on modeling counterfactual outcome based on static data. However, many applications have time-varying confounding effects such as multiple treatments over time. How to model such time-varying effects from longitudinal observational data? How to model complex high-dimensional dependency in the data? To address these challenges, we propose Deep Recurrent Inverse TreatmEnt weighting…
Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. when an association between two variables is identified due to them both being associated to a third variable). In this paper we propose a proof of concept method that learns common associations and uses this knowledge to automatically refine side effect signals (i.e. exposure-outcome associations) by removing instances of the exposure-outcome associations that are caused b…
Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data
Centre for Molecular Medicine and Biobanking, University of Malta（马耳他大学分子医学与生物银行中心）；Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta（马耳他大学应用生物医学科学系）；Central European Institute of Technology, Masaryk University（马萨里克大学中欧技术研究所）；National Centre for Biomolecular Research, Faculty of Science, Masaryk University（马萨里克大学生物分子研究中心）
MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Warshel Institute for Computational Biology（瓦谢尔计算生物研究院）；Guangdong Provincial Key Laboratory of Digital Biology and Drug Development（广东省数字生物与药物开发重点实验室）；Peking Union Medical College Hospital（北京协和医院）；Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院 北京协和医学院）
School of Computer Science and Engineering, Hunan University（湖南大学计算机科学与工程学院）；AWS Shanghai AI Lab（亚马逊云科技上海人工智能实验室）；Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic（克利夫兰诊所勒纳研究所基因组医学研究所）；New York University Shanghai（上海纽约大学）；AWS AI（亚马逊云科技人工智能部门）；University of Minnesota（明尼苏达大学）；Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University（凯斯西储大学克利夫兰诊所勒纳医学院分子医学系）；Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine（凯斯西储大学医学院凯斯综合癌症中心）
There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehe…
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Developing new drugs for target diseases is a time-consuming and expensive task, drug repurposing has become a popular topic in the drug development field. As much health claim data become available, many studies have been conducted on the data. The real-world data is noisy, sparse, and has many confounding factors. In addition, many studies have shown that drugs effects are heterogeneous among the population. Lots of advanced machine learning models about estimating heterogeneous treatment effects (HTE) have emerged in recent years, and have been applied to in econometrics and machine learnin…
Multimodal AI predicts clinical outcomes of drug combinations from preclinical data
Harvard Medical School（哈佛医学院）；Harvard College（哈佛学院）；AstraZeneca（阿斯利康）；Carnegie Mellon University（卡内基梅隆大学）；Dana-Farber Cancer Institute and Harvard Medical School（达纳-法伯癌症研究所和哈佛医学院）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（MIT和哈佛大学 Broad研究所）；Harvard Data Science Initiative（哈佛数据科学计划）
Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering（化学与生物传感国家重点实验室，计算机科学与电子工程学院；教育部长江超级计算与人工智能融合计算重点实验室）；The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence（湖南大学）；Hunan University（人工智能与计算机科学学院）；School of Artificial Intelligence and Computer Science（江南大学）；Jiangnan University（计算机科学系）；Department of Computer Science（筑波大学）；University of Tsukuba（中医学学院）；School of Chinese Medicine（香港 Baptist 大学）；Hong Kong Baptist University（信息学院，国家健康与医学数据科学研究院）；School of Informatics, National Institute for Data Science in Health and Medicine
