Log in

Relevant bibliographies by topics / Neural networks; X-ray crystallography

Contents

Journal articles
Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'Neural networks; X-ray crystallography'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Neural networks; X-ray crystallography.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Neural networks; X-ray crystallography"

1

Sullivan, Brendan, Rick Archibald, Jahaun Azadmanesh, et al. "BraggNet: integrating Bragg peaks using neural networks." Journal of Applied Crystallography 52, no. 4 (2019): 854–63. http://dx.doi.org/10.1107/s1600576719008665.

Full text

Abstract:

Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U

APA, Harvard, Vancouver, ISO, and other styles

2

Ke, Tsung-Wei, Aaron S. Brewster, Stella X. Yu, Daniela Ushizima, Chao Yang, and Nicholas K. Sauter. "A convolutional neural network-based screening tool for X-ray serial crystallography." Journal of Synchrotron Radiation 25, no. 3 (2018): 655–70. http://dx.doi.org/10.1107/s1600577518004873.

Full text

Abstract:

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data f

APA, Harvard, Vancouver, ISO, and other styles

3

Ito, Sho, Go Ueno, and Masaki Yamamoto. "DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography." Journal of Synchrotron Radiation 26, no. 4 (2019): 1361–66. http://dx.doi.org/10.1107/s160057751900434x.

Full text

Abstract:

High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. Thi

APA, Harvard, Vancouver, ISO, and other styles

4

Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, et al. "Accurate prediction of protein structures and interactions using a three-track neural network." Science 373, no. 6557 (2021): 871–76. http://dx.doi.org/10.1126/science.abj8754.

Full text

Abstract:

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography an

APA, Harvard, Vancouver, ISO, and other styles

5

Xuan, Wenjing, Ning Liu, Neng Huang, Yaohang Li, and Jianxin Wang. "CLPred: a sequence-based protein crystallization predictor using BLSTM neural network." Bioinformatics 36, Supplement_2 (2020): i709—i717. http://dx.doi.org/10.1093/bioinformatics/btaa791.

Full text

Abstract:

Abstract Motivation Determining the structures of proteins is a critical step to understand their biological functions. Crystallography-based X-ray diffraction technique is the main method for experimental protein structure determination. However, the underlying crystallization process, which needs multiple time-consuming and costly experimental steps, has a high attrition rate. To overcome this issue, a series of in silico methods have been developed with the primary aim of selecting the protein sequences that are promising to be crystallized. However, the predictive performance of the curren

APA, Harvard, Vancouver, ISO, and other styles

6

Elbasir, Abdurrahman, Balasubramanian Moovarkumudalvan, Khalid Kunji, Prasanna R. Kolatkar, Raghvendra Mall, and Halima Bensmail. "DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction." Bioinformatics 35, no. 13 (2018): 2216–25. http://dx.doi.org/10.1093/bioinformatics/bty953.

Full text

Abstract:

Abstract Motivation Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, the majority of these methods build their predictors by extracting features from protein sequences, which is computationally expensive and can explode the feature space. We propose DeepCrystal, a deep learning framework for sequence-based protein crystallization predi

APA, Harvard, Vancouver, ISO, and other styles

7

Uddin, Mostofa Rafid, Sazan Mahbub, M. Saifur Rahman, and Md Shamsuzzoha Bayzid. "SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction." Bioinformatics 36, no. 17 (2020): 4599–608. http://dx.doi.org/10.1093/bioinformatics/btaa531.

Full text

Abstract:

Abstract Motivation Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8

APA, Harvard, Vancouver, ISO, and other styles

8

van den Bedem, Henry, Gira Bhabha, Kun Yang, Peter E. Wright, and James S. Fraser. "Automated identification of functional dynamic contact networks from X-ray crystallography." Nature Methods 10, no. 9 (2013): 896–902. http://dx.doi.org/10.1038/nmeth.2592.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Inokuma, Yasuhide, and Makoto Fujita. "Visualization of Solution Chemistry by X-ray Crystallography Using Porous Coordination Networks." Bulletin of the Chemical Society of Japan 87, no. 11 (2014): 1161–76. http://dx.doi.org/10.1246/bcsj.20140217.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Romo, T., K. Gopal, E. McKee, et al. "TEXTAL: AI-Based Structural Determination for X-ray Protein Crystallography." IEEE Intelligent Systems 20, no. 6 (2005): 59–63. http://dx.doi.org/10.1109/mis.2005.114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Neural networks; X-ray crystallography"

1

Kinna, David John. "Pattern recognition in chemical crystallography." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318724.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Abbott, Paul H. "Heuristically guided interpretation of X-ray fluorescence spectra." Thesis, University of Wolverhampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309784.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Poláková, Veronika. "Využití konvolučních neuronových sítí pro segmentaci chrupavčitých tkání myších embryí v mikro-CT datech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442503.

Full text

Abstract:

Automatická segmentace biologických struktur v mikro-CT datech je stále výzvou, protože často objekt zájmu (v našem případě obličejová chrupavka) není charakterizovaný unikátním jasem či ostrými hranicemi. V posledních letech se konvoluční neuronové sítě (CNNs) staly mimořádně populárními v mnoha oblastech počítačového vidění. Konkrétně pro segmentaci biomedicínských obrazů je široce používaná architektura U-Net. Nicméně v případě mikro-CT dat vyvstává otázka, zda by nebylo výhodnější použít 3D CNN. Diplomová práce navrhla CNN architekturu založenou na síti V-Net včetně metodologie pro předzpr

APA, Harvard, Vancouver, ISO, and other styles

4

Chen, Hsin-Jui, and 陳新叡. "Lung X-Ray Segmentation using Deep Convolutional Neural Networks on Contrast-enhanced Binarized Images." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r59pdv.

Full text

Abstract:

碩士<br>國立臺灣科技大學<br>電子工程系<br>107<br>Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is crucial in computer-aided diagnosis. In this paper, we propose a method to segment lungs from CXR images, which comprises of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Secondly, using adaptive binarization to preprocess CXR images to obtain foreground information and reduce storage space usage. Thirdly, the practicality of the proposed methodology is validated through various fully convolutional neur

APA, Harvard, Vancouver, ISO, and other styles

5

Norval, Michael John. "Detection of pulmonary tuberculosis using deep learning convolutional neural networks." Diss., 2019. http://hdl.handle.net/10500/26890.

Full text

Abstract:

If Pulmonary Tuberculosis (PTB) is detected early in a patient, the greater the chances of treating and curing the disease. Early detection of PTB could result in an overall lower mortality rate. Detection of PTB is achieved in many ways, for instance, by using tests like the sputum culture test. The problem is that conducting tests like these can be a lengthy process and takes up precious time. The best and quickest PTB detection method is viewing the chest X-Ray image (CXR) of the patient. To make an accurate diagnosis requires a qualified professional Radiologist. Neural Networks have

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Neural networks; X-ray crystallography"

1

Kao, Hsien-Pei, Tzu-Chia Tung, Hong-Yi Chen, Cheng-Shih Wong, and Chiou-Shann Fuh. "Pin Defect Inspection with X-ray Images." In Advances in Neural Networks - ISNN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_54.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Oliveira, Gabriel, Rafael Padilha, André Dorte, et al. "COVID-19 X-ray Image Diagnostic with Deep Neural Networks." In Advances in Bioinformatics and Computational Biology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65775-8_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Kim, Byungwhan, Sooyoun Kim, and Sang Jeen Hong. "Recognition of Plasma-Induced X-Ray Photoelectron Spectroscopy Fault Pattern Using Wavelet and Neural Network." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760191_151.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Kunapinun, Alisa, and Matthew N. Dailey. "COVID-19 X-ray Image Diagnosis Using Deep Convolutional Neural Networks." In Proceedings of Sixth International Congress on Information and Communication Technology. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2380-6_64.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Rustichelli, Franco. "Structural Properties of Monolayers and Langmuir-Blodgett Films by X-Ray Scattering Techniques." In From Neural Networks and Biomolecular Engineering to Bioelectronics. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-1088-2_16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Kong, Quan, Naoto Akira, Bin Tong, Yuki Watanabe, Daisuke Matsubara, and Tomokazu Murakami. "Multimodal Deep Neural Networks Based Ensemble Learning for X-Ray Object Recognition." In Computer Vision – ACCV 2018 Workshops. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21074-8_41.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Tsukada, Ryotaro, Lekang Zou, and Hitoshi Iba. "Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects." In Natural Computing Series. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3685-4_12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Kondo, Tadashi, and Abhijit S. Pandya. "Recognition of X-ray Images by Using Revised GMDH-type Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45226-3_116.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Karthik, K., and Sowmya Kamath S. "Automated View Orientation Classification for X-ray Images Using Deep Neural Networks." In Smart Computational Intelligence in Biomedical and Health Informatics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003109327-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

König, Andreas, Andreas Herenz, and Klaus Wolter. "Application of neural networks for automated X-ray image inspection in electronics manufacturing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100526.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Neural networks; X-ray crystallography"

1

Fan, Fenglei, Hongming Shan, Lars Gjesteby, and Ge Wang. "Quadratic neural networks for CT metal artifact reduction." In Developments in X-Ray Tomography XII, edited by Bert Müller and Ge Wang. SPIE, 2019. http://dx.doi.org/10.1117/12.2530363.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Achkar, Roger, Johnny Narcis, Wael Abou Awad, and Karim Hitti. "Smart X-Ray Scanners Using Artificial Neural Networks." In 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim). IEEE, 2018. http://dx.doi.org/10.1109/uksim.2018.00013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Cooley, Victoria, Stuart R. Stock, William Guise, et al. "Semantic segmentation of mouse jaws using convolutional neural networks." In Developments in X-Ray Tomography XIII, edited by Bert Müller and Ge Wang. SPIE, 2021. http://dx.doi.org/10.1117/12.2594332.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Tekawade, Aniket, Brandon A. Sforzo, Katarzyna E. Matusik, Alan L. Kastengren, and Christopher F. Powell. "High-fidelity geometry generation from CT data using convolutional neural networks." In Developments in X-Ray Tomography XII, edited by Bert Müller and Ge Wang. SPIE, 2019. http://dx.doi.org/10.1117/12.2540442.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Sushmit, Asif Shahriyar, Shakib Uz Zaman, Ahmed Imtiaz Humayun, Taufiq Hasan, and Mohammed Imamul Hassan Bhuiyan. "X-Ray Image Compression Using Convolutional Recurrent Neural Networks." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834656.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Allred, Lloyd G., Martin H. Jones, Matthew J. Sheats, and Anthony W. Davis. "Computed tomography of x-ray images using neural networks." In AeroSense 2000, edited by Kevin L. Priddy, Paul E. Keller, and David B. Fogel. SPIE, 2000. http://dx.doi.org/10.1117/12.380600.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Yin, Wei, Baolian Qi, Ting Cai, and Jinpeng Li. "X-Ray Image Enhancement Using Blind Denoising Neural Networks." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9497945.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Lindgren, Erik, and Christopher Zach. "Analysis of industrial x-ray computed tomography data with deep neural networks." In Developments in X-Ray Tomography XIII, edited by Bert Müller and Ge Wang. SPIE, 2021. http://dx.doi.org/10.1117/12.2594714.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Dey, Sumi, and Olac Fuentes. "Predicting Solar X-ray Flux Using Deep Learning Techniques." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207284.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Khosa, Ikramullah, and Eros Pasero. "Feature extraction in X-ray images for hazelnuts classification." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889661.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!