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Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning [article]

Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge
2018 arXiv   pre-print
In this work, we propose a data fusion framework that uses Independent Vector Analysis to exploit underlying complementary information contained in different molecular featurization methods, bringing us  ...  Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention  ...  Bill Wilson from the Energetics Technology Center for their encouragement, useful thoughts, and for proofreading the manuscript.  ... 
arXiv:1811.00628v1 fatcat:avw6mrjqyvbfletoi4n7djnxwe

Machine learning in chemoinformatics and drug discovery

Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman
2018 Drug Discovery Today  
With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound  ...  databases to design drugs with important biological properties.  ...  Acknowledgments We thank all members of the Helix group at Stanford University for their helpful feedback and suggestions.  ... 
doi:10.1016/j.drudis.2018.05.010 pmid:29750902 pmcid:PMC6078794 fatcat:ckxznjxuujajle6iqycgi74d7i

Information Fusion for Multi-Source Material Data: Progress and Challenges

Zhou, Hong, Jin
2019 Applied Sciences  
First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data.  ...  This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion.  ...  Acknowledgments: We would like to thank the editors and anonymous reviewers for their suggestions and comments to improve the quality of the paper.  ... 
doi:10.3390/app9173473 fatcat:yq25vikkqfdflp36bqx5o3l6wu

Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

Samuel Egieyeh, James Syce, Sarel F. Malan, Alan Christoffels, Dinesh Gupta
2018 PLoS ONE  
This study set out to harness antimalarial bioactivity data of natural products to build accurate predictive models, utilizing classical machine learning approaches, which can find potential antimalarial  ...  Classical machine learning approaches were used to build four classifier models (Naïve Bayesian, Voted Perceptron, Random Forest and Sequence Minimization Optimization of Support Vector Machines) from  ...  Acknowledgments The authors wish to acknowledge William Jose, Godinez Navarro and Azzaoui Kamal all of Novartis Institute for Biomedical Research (NIBR), Basel Switzerland for reading this paper and making  ... 
doi:10.1371/journal.pone.0204644 fatcat:l7iaewiy2vafzk5dm4iniw7fwi

Identification of Cyclin Protein Using Gradient Boost Decision Tree Algorithm

Hasan Zulfiqar, Shi-Shi Yuan, Qin-Lai Huang, Zi-Jie Sun, Fu-Ying Dao, Xiao-Long Yu, Hao Lin
2021 Computational and Structural Biotechnology Journal  
Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins.  ...  However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods.  ...  ., A novel molecular representation with BiGRU neural networks for learning atom. Briefings in Bioinformatics, 2020. 21(6): p. 2099-2111. 44.  ... 
doi:10.1016/j.csbj.2021.07.013 pmid:34527186 pmcid:PMC8346528 fatcat:sgyuzvadifcpbemeu3qibg6hzu

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
2019 Information Fusion  
No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease.  ...  An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation.  ...  These methods use molecular, drug, and patient data to predict side effects associated with pairs of drugs.  ... 
doi:10.1016/j.inffus.2018.09.012 pmid:30467459 pmcid:PMC6242341 fatcat:mjhnzxxv4fbrlgufb7vkg3pz5u


2006 Journal of Bioinformatics and Computational Biology  
Machine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions.  ...  For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes.  ...  Shang-Hung Lai of the Princeton University, for invaluable insights.  ... 
doi:10.1142/s0219720006002065 pmid:16819784 fatcat:vjjxjpjxrvfobdx5dw47g744z4

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment  ...  Support vector machines Support vector machines (SVMs) are the most used algorithms for hyperspectral data analysis [227] .  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Machine Learning and Integrative Analysis of Biomedical Big Data

Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, Peipei Ping
2019 Genes  
Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods.  ...  In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing  ...  MI for nonlinear analysis can be performed using random forest (RF) Figure 3 . 3 Machine learning with missing data.  ... 
doi:10.3390/genes10020087 pmid:30696086 pmcid:PMC6410075 fatcat:vopnjgke4fculmr7t3n43ewfiy

Generative chemistry: drug discovery with deep learning generative models [article]

Yuemin Bian, Xiang-Qun Xie
2020 arXiv   pre-print
Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry.  ...  From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine  ...  ACKNOWLEDGEMENTS Authors would like to acknowledge the funding support to the Xie laboratory from the NIH NIDA (P30 DA035778A1) and DOD (W81XWH-16-1-0490).  ... 
arXiv:2008.09000v1 fatcat:ivznoc4bsbfoderwr2ted76fiq

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity

Ying Hong Li, Jing Yu Xu, Lin Tao, Xiao Feng Li, Shuang Li, Xian Zeng, Shang Ying Chen, Peng Zhang, Chu Qin, Cheng Zhang, Zhe Chen, Feng Zhu (+2 others)
2016 PLoS ONE  
predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted  ...  Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other  ...  Acknowledgments Analyzed the data: YHL JYX LT XFL. Contributed reagents/materials/analysis tools: YHL JYX LT XFL SL XZ SYC PZ CQ CZ ZC. Wrote the paper: FZ YZC. Design the web interface: YHL LT XFL.  ... 
doi:10.1371/journal.pone.0155290 pmid:27525735 pmcid:PMC4985167 fatcat:mmu6i6whljckdmv35xh6esh6b4

Application of Support Vector Machines in Viral Biology [chapter]

Sonal Modak, Swati Mehta, Deepak Sehgal, Jayaraman Valadi
2019 Global Virology III: Virology in the 21st Century  
Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases.  ...  Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems.  ...  Support Vector Machines for Classification Support Vector Machines can be used both for supervised and unsupervised learning tasks. In viral biology, SVM is used mainly for supervised learning.  ... 
doi:10.1007/978-3-030-29022-1_12 fatcat:leaxfnxiuze2jbuyenwps7qcve

Recent Advances in Variational Autoen-coders with Representation Learning for Biomedical Informatics: A Survey

Ruoqi Wei, Ausif Mahmood
2020 IEEE Access  
This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning.  ...  The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution.  ...  [48] found that VAE with a VAMP prior is capable of learning biologically informative embeddings without compromising on generative properties.  ... 
doi:10.1109/access.2020.3048309 fatcat:ka5k7nfia5c5ra4npsvuxp6h3q

Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture

Fei He, Rui Wang, Jiagen Li, Lingling Bao, Dong Xu, Xiaowei Zhao
2018 BMC Systems Biology  
While deep learning is able to excavate underlying characteristics from large-scale training data via multiple-layer networks and non-linear mapping operations.  ...  properties and sequence profiles, and designed different deep network layers to extract the hidden representations from them.  ...  Acknowledgements The authors would like to thank Dong Xu and Duolin Wang for their pioneering work and helpful suggestions concerning the work.  ... 
doi:10.1186/s12918-018-0628-0 pmid:30463553 pmcid:PMC6249717 fatcat:mydfsfamjjhqjolvqed7iusrhy

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

Haneen Banjar, David Adelson, Fred Brown, Naeem Chaudhri
2017 BioMed Research International  
Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making  ...  This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient.  ...  Acknowledgments Financial support for this study was provided in part by a grant from King Abdulaziz University.  ... 
doi:10.1155/2017/3587309 pmid:28812013 pmcid:PMC5547708 fatcat:ylwvfw74bfhetayzundiylofg4
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