DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction
Aalto University（阿尔托大学）；University of Manchester（曼彻斯特大学）；Kyoto University（京都大学）
Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka
发表信息：
Guvencpaltun, B., Kaski, S., & Mamitsuka, H. (2021). DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, (01), 1-1
Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substantial experimental efforts that are made difficult by the heterogeneity of cell lines and huge cost. An effective solution is to build a computational model over the diverse omics data, including genomic, molecular, and environmental information. However, choosing informative and reliable data sources from among the different types of data is a challenging problem. We propose DIVERSE, a framework of Bayesian importance-weighted tri- and bi-matrix factorization(DIVERSE3 or DIVERSE2) to predict drug responses from data of cell lines, drugs, and gene interactions. DIVERSE integrates the data sources systematically, in a step-wise manner, examining the importance of each added data set in turn. More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses. Empirical experiments show that DIVERSE clearly outperformed five other methods including three state-of-the-art approaches, under cross-validation, particularly in out-of-matrix prediction, which is closer to the setting of real use cases and more challenging than simpler in-matrix prediction. Additionally, case studies for discovering new drugs further confirmed the performance advantage of DIVERSE.
University of Cambridge（剑桥大学）；University of Liverpool（利物浦大学）
Classifying chemicals according to putative modes of action (MOAs) is of paramount importance in the context of risk assessment. However, current methods are only able to handle a very small proportion of the existing chemicals. We address this issue by proposing an integrative deep learning architecture that learns a joint representation from molecular structures of drugs and their effects on human cells. Our choice of architecture is motivated by the significant influence of a drug's chemical structure on its MOA. We improve on the strong ability of a unimodal architecture (F1 score of 0.803…
With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms competing methods and can iden…
The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of validated drug-disease associations is scarce compared to the number of drugs and diseases in the real world. Too few labeled samples will make the classification model unable to learn effective latent factors of drugs, resulting in poor generalization performance. In this work, we propose a multi-task self-supervised learning framework for computational drug repositioning. T…
Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-novo evaluations. We propose a framework called Functional Integrative Bayesian Analysis of High-dimensio…
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…
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Institute for AI Industry Research, Tsinghua University, Beijing, China（人工智能产业研究院，清华大学，北京，中国）；Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China（北京生物结构前沿研究中心，清华大学，北京，中国）；Beijing Academy of Artificial Intelligence (BAAI), Beijing, China（北京人工智能研究院（BAAI），北京，中国）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（计算机科学与技术系，清华大学，北京，中国）；University of Electronic Science and Technology of China, Chengdu, China（电子科技大学，成都，中国）
The Chinese University of Hong Kong（香港中文大学）；BioMap (Beijing) Intelligence Technology Limited（百图生科（北京）智能技术有限公司）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The CUHK Shenzhen Research Institute（香港中文大学深圳研究院）
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we c…
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）
In this study, we developed and tested machine learning models to predict epilepsy surgical outcome using noninvasive clinical and demographic data from patients. Methods: Seven dif-ferent categorization algorithms were used to analyze the data. The techniques are also evaluated using the Leave-One-Out method. For precise evaluation of the results, the parameters accuracy, precision, recall and, F1-score are calculated. Results: Our findings revealed that a machine learning-based presurgical model of patients' clinical features may accurately predict the outcome of epilepsy surgery in patients…
The liver performs critical physiological functions, including metabolizing and removing substances, such as toxins and drugs, from the bloodstream. Hepatotoxicity itself is intimately linked to abnormal hepatic transport and hepatotoxicity remains the primary reason drugs in development fail and approved drugs are withdrawn from the market. For this reason, we propose to analyze, across liver compartments, the transport kinetics of fluorescein-a fluorescent marker used as a proxy for drug molecules-using intravital microscopy data. To resolve the transport kinetics quantitatively from fluores…
Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose-regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) information to estimate the maximum tolerated dose-regimen (MTD-regimen), at the end of the dose-escalati…
East China University of Science and Technology（华东理工大学）；Jacobs Technion-Cornell Institute（雅各布布·泰克尼翁-康奈尔研究所）；Cornell Tech（康奈尔科技校区）；Cornell University（康奈尔大学）
Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtype-Former, a deep learning method based on MLP and Transformer Block, to extract the low-dimensional representation of the multi-omics data. K-means and Consensus Clustering are also used to achieve ac…
