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3D Deep Learning for Biological Function Prediction from Physical Fields [article]

Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler, Daniel Cremers
2017 arXiv   pre-print
In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.  ...  Protein function based on EC numbers is predicted from the approximated electron density field.  ...  We postulate therefore that deep learning methods of inferring functional information from raw spatial 3D data will increase in importance, with the growing amounts of spatial biological information and  ... 
arXiv:1704.04039v1 fatcat:2oq3eaivu5g2vfimoyekp66aze

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  ...  further development in this evolving field.  ...  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

Single Cell Biological Microlasers Powered by Deep Learning [article]

Zhen Qiao, Wen Sun, Na Zhang, Randall Ang Jie, Sing Yian CHEW, Yu-Cheng Chen
2021 bioRxiv   pre-print
In the second part, deep learning was employed to study laser modes generated from biological cells.  ...  In this study, deep learning technique was applied to analyze laser modes generated from single-cell lasers, in which a correlation between laser modes and physical properties of cells was built.  ...  We would like to thank the lab support from Centre of Bio-Devices and Bioinformatics and Internal Grant NAP SUG from Nanyang Technological University.  ... 
doi:10.1101/2021.01.21.427584 fatcat:pze6nubn5nhxzic6hcbzxds4la

Deep-learning enhancement of large scale numerical simulations [article]

Caspar van Leeuwen, Damian Podareanu, Valeriu Codreanu, Maxwell X. Cai, Axel Berg, Simon Portegies Zwart, Robin Stoffer, Menno Veerman, Chiel van Heerwaarden, Sydney Otten, Sascha Caron, Cunliang Geng (+2 others)
2020 arXiv   pre-print
This type of application, deep learning for high-performance computing, is the theme of this whitepaper.  ...  Deep learning appears to be a promising way to achieve this.  ...  Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," NIPS, 2012.  ... 
arXiv:2004.03454v1 fatcat:l4vs2ham6ngdjdvio66uieb5my

Quantitative Phase Imaging and Artificial Intelligence: A Review [article]

YoungJu Jo, Hyungjoo Cho, Sang Yun Lee, Gunho Choi, Geon Kim, Hyun-seok Min, YongKeun Park
2018 arXiv   pre-print
Herein, we review the synergy between QPI and machine learning with a particular focus on deep learning. Further, we provide practical guidelines and perspectives for further development.  ...  Subsequently, the AI-assisted interrogation of QPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself.  ...  Loss function: In deep learning, the loss functions are essentially similar to those used in conventional parametric models.  ... 
arXiv:1806.03982v2 fatcat:bt2h4c63gnalhhsbm4c6zyuhtu

Deep Learning in Protein Structural Modeling and Design

Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
2020 Patterns  
meaningful problems that may benefit from deep learning techniques.  ...  Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior  ...  functionality of proteins responsible for most biological activities.  ... 
doi:10.1016/j.patter.2020.100142 pmid:33336200 pmcid:PMC7733882 fatcat:qhjfidyusbfothv4kxdthodv6q

A Review of Protein Structure Prediction using Deep Learning

Meredita Susanty, Tati Erawati Rajab, Rukman Hertadi, Gunadi, T. Yamada, A.A.C. Pramana, Y. Ophinni, A. Gusnanto, W.A. Kusuma, J. Yunus, Afiahayati, R. Dharmastiti (+3 others)
2021 BIO Web of Conferences  
Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying  ...  We discuss various deep learning approaches used to predict protein structure and future achievements and challenges.  ...  This work was supported by Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia.  ... 
doi:10.1051/bioconf/20214104003 fatcat:cwab4ukudvgx5dnny3dk64g3uq

Deep Learning in Protein Structural Modeling and Design [article]

Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
2020 arXiv   pre-print
meaningful problems that may benefit from deep learning techniques.  ...  Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior  ...  Deep learning architectures In conventional computational approaches, predictions from data are made by means of physical equations and modeling.  ... 
arXiv:2007.08383v1 fatcat:ynpdumcqnbel7duwffbork6s2u

A Short Review on Data Modelling for Vector Fields [article]

Jun Li, Wanrong Hong, Yusheng Xiang
2020 arXiv   pre-print
, the modelling of the fluid flow in earth science, and the modelling of physical fields.  ...  On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e.g. non-parametric transformations of 3D point clouds using 3D vector fields  ...  The recent success of using deep neural networks-based modelling for image and multimedia data has been introduced to and shown promise in physical field modelling.  ... 
arXiv:2009.00577v1 fatcat:c7y5aacx2fde3nph3qux2qtste

Protein sequence-to-structure learning: Is this the end(-to-end revolution)? [article]

Elodie Laine, Stephan Eismann, Arne Elofsson, Sergei Grudinin
2021 arXiv   pre-print
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13.  ...  In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy.  ...  The authors thank Kliment Olechnovič from Vilnius University for his help with illustrating Voronoi cells and proof-reading the manuscript, and Bowen Jing for his feedback on the manuscript.  ... 
arXiv:2105.07407v2 fatcat:6szubg7q2rajlj3l4vyzqri3nm

Machine Learning for Next-generation Printed Technologies

Litty Varghese Thekkekara, Shamini P. Baby, Jeffery Chan, Ivan Cole
2021 Advanced Materials Science and Technology  
In this review, we discuss the use of machine learning prediction algorithms for technologies using 3D printing.  ...  It has a broader opportunity to support 3D printing to develop the potentials and efficiency through effective prediction methods for printing methods and design aspects.  ...  HML algorithm predicts the problematic physical system behaviours using sparse data sets through the integration of physical modeling using statistical learning.  ... 
doi:10.37155/2717-526x-0301-5 fatcat:r2ol74suyvacdozry54q7gbiaa

Physics-informed neural networks for diffraction tomography [article]

Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis
2022 arXiv   pre-print
We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately.  ...  We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples.  ...  Joowon Lim for their useful comments about the implementation of MaxwellNet and tomographic optimization in Tensorflow.  ... 
arXiv:2207.14230v1 fatcat:rk7pwrj66bfv7a5x2hea2iymeu

Artificial Intelligence-Based Drug Design and Discovery [chapter]

Yu-Chen Lo, Gui Ren, Hiroshi Honda, Kara L. Davis
2019 Cheminformatics and its Applications [Working Title]  
The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure-Activity  ...  Finally, we will discuss limitations and future direction to guide this rapidly evolving field.  ...  while others are purely theoretical, such as chemical descriptors and molecular fields derived from the chemical graph or 3D structure data.  ... 
doi:10.5772/intechopen.89012 fatcat:327njwv46rc2hi32nwx3nbkqkq

Machine learning in protein structure prediction

Mohammed AlQuraishi
2021 Current Opinion in Chemical Biology  
Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases.  ...  The extraction of physical contacts from the evolutionary record; the distillation of sequence-structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank  ...  Trunks Current PSP trunks largely borrow from advances in the broader deep learning field, including CNNs, ResNets, and most recently Transformers.  ... 
doi:10.1016/j.cbpa.2021.04.005 pmid:34015749 fatcat:v6jgfwns25dajfoiayjqexbe5a

Continuous Molecular Fields Approach Applied to Structure-Activity Modeling [article]

Igor I. Baskin, Nelly I. Zhokhova
2013 arXiv   pre-print
The Method of Continuous Molecular Fields is a universal approach to predict various properties of chemical compounds, in which molecules are represented by means of continuous fields (such as electrostatic  ...  The essence of the proposed approach consists in performing statistical analysis of functional molecular data by means of joint application of kernel machine learning methods and special kernels which  ...  Yu.A.Ustynyuk for stimulating discussion and advice. The authors also thank Prof. A.Varnek and Dr. G.Marcou for valuable comments regarding the developed approach.  ... 
arXiv:1311.1495v1 fatcat:wnoyw7t7mzhlfcfa63kv4mrdz4
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