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

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

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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

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Sullivan, Brendan, Rick Archibald, Jahaun Azadmanesh, Venu Gopal Vandavasi, Patricia S. Langan, Leighton Coates, Vickie Lynch, and Paul Langan. "BraggNet: integrating Bragg peaks using neural networks." Journal of Applied Crystallography 52, no. 4 (July 26, 2019): 854–63. http://dx.doi.org/10.1107/s1600576719008665.

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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-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data.

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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 (April 24, 2018): 655–70. http://dx.doi.org/10.1107/s1600577518004873.

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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 for neural network optimization.

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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 (June 3, 2019): 1361–66. http://dx.doi.org/10.1107/s160057751900434x.

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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. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography.

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Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, et al. "Accurate prediction of protein structures and interactions using a three-track neural network." Science 373, no. 6557 (July 15, 2021): 871–76. http://dx.doi.org/10.1126/science.abj8754.

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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 and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

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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 (December 2020): i709—i717. http://dx.doi.org/10.1093/bioinformatics/btaa791.

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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 current methods is modest. Results We propose a deep learning model, so-called CLPred, which uses a bidirectional recurrent neural network with long short-term memory (BLSTM) to capture the long-range interaction patterns between k-mers amino acids to predict protein crystallizability. Using sequence only information, CLPred outperforms the existing deep-learning predictors and a vast majority of sequence-based diffraction-quality crystals predictors on three independent test sets. The results highlight the effectiveness of BLSTM in capturing non-local, long-range inter-peptide interaction patterns to distinguish proteins that can result in diffraction-quality crystals from those that cannot. CLPred has been steadily improved over the previous window-based neural networks, which is able to predict crystallization propensity with high accuracy. CLPred can also be improved significantly if it incorporates additional features from pre-extracted evolutional, structural and physicochemical characteristics. The correctness of CLPred predictions is further validated by the case studies of Sox transcription factor family member proteins and Zika virus non-structural proteins. Availability and implementation https://github.com/xuanwenjing/CLPred.

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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 (November 21, 2018): 2216–25. http://dx.doi.org/10.1093/bioinformatics/bty953.

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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 prediction. It uses deep learning to identify proteins which can produce diffraction-quality crystals without the need to manually engineer additional biochemical and structural features from sequence. Our model is based on convolutional neural networks, which can exploit frequently occurring k-mers and sets of k-mers from the protein sequences to distinguish proteins that will result in diffraction-quality crystals from those that will not. Results Our model surpasses previous sequence-based protein crystallization predictors in terms of recall, F-score, accuracy and Matthew’s correlation coefficient (MCC) on three independent test sets. DeepCrystal achieves an average improvement of 1.4, 12.1% in recall, when compared to its closest competitors, Crysalis II and Crysf, respectively. In addition, DeepCrystal attains an average improvement of 2.1, 6.0% for F-score, 1.9, 3.9% for accuracy and 3.8, 7.0% for MCC w.r.t. Crysalis II and Crysf on independent test sets. Availability and implementation The standalone source code and models are available at https://github.com/elbasir/DeepCrystal and a web-server is also available at https://deeplearning-protein.qcri.org. Supplementary information Supplementary data are available at Bioinformatics online.

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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 (May 21, 2020): 4599–608. http://dx.doi.org/10.1093/bioinformatics/btaa531.

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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-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction. Results We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins. Availability and implementation SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.

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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 (August 4, 2013): 896–902. http://dx.doi.org/10.1038/nmeth.2592.

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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 (November 15, 2014): 1161–76. http://dx.doi.org/10.1246/bcsj.20140217.

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Romo, T., K. Gopal, E. McKee, L. Kanbi, Reetal Pai, J. Smith, J. Sacchettini, and T. Ioerger. "TEXTAL: AI-Based Structural Determination for X-ray Protein Crystallography." IEEE Intelligent Systems 20, no. 6 (November 2005): 59–63. http://dx.doi.org/10.1109/mis.2005.114.

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

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

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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.

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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.

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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 předzpracování a postprocessing dat. Základní architektura byla dále optimalizována pomocí pokročilých architektonických modifikací jako jsou pyramidální modul dilatovaných konvolucí (ASPP modul), škálovatelná exponenciálně-lineární jednotka (SELU aktivační funkce), víceúrovňová kontrola učení (multi-output supervision) či bloky s hustými propojeními (Dense blocks). Pro učení sítě byly použity moderní přístupy jako zahřívání kroku učení (learning rate warmup) či AdamW optimalizátor. I přes to, že 3D CNN v úloze segmentace obličejové chrupavky nepřekonala U-Net, optimalizace zvýšila medián Dice koeficientu z 69,74 % na 80,01 %. Používání těchto pokročilých architektonických modifikací v dalším výzkumu je proto vřele doporučováno, jelikož můžou být přidány do libovolné architektury typu U-Net a zároveň výrazně zlepšit výsledky.

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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.

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碩士
國立臺灣科技大學
電子工程系
107
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 neural networks. The experimental results revealed that the proposed method can achieve comparable segmentation accuracy to those of state-of-the-art methods with inferring time and memory consumption for the model input cut by 19.10% and 94.6% on average.

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

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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 been around for several years but is only now making ground-breaking advancements in speech and image processing because of the increased processing power at our disposal. Artificial intelligence, especially Deep Learning Convolutional Neural Networks (DLCNN), has the potential to diagnose and detect the disease immediately. If DLCNN can be used in conjunction with the professional medical institutions, crucial time and effort can be saved. This project aims to determine and investigate proper methods to identify and detect Pulmonary Tuberculosis in the patient chest X-Ray images using DLCNN. Detection accuracy and success form a crucial part of the research. Simulations on an input dataset of infected and healthy patients are carried out. My research consists of firstly evaluating the colour depth and image resolution of the input images. The best resolution to use is found to be 64x64. Subsequently, a colour depth of 8 bit is found to be optimal for CXR images. Secondly, building upon the optimal resolution and colour depth, various image pre-processing techniques are evaluated. In further simulations, the pre-processed images with the best outcome are used. Thirdly the techniques evaluated are transfer learning, hyperparameter adjustment and data augmentation. Of these, the best results are obtained from data augmentation. Fourthly, a proposed hybrid approach. The hybrid method is a mixture of CAD and DLCNN using only the lung ROI images as training data. Finally, a combination of the proposed hybrid method, coupled with augmented data and specific hyperparameter adjustment, is evaluated. Overall, the best result is obtained from the proposed hybrid method combined with synthetic augmented data and specific hyperparameter adjustment.
Electrical and Mining Engineering

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

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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, 465–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_54.

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Oliveira, Gabriel, Rafael Padilha, André Dorte, Luis Cereda, Luiz Miyazaki, Maurício Lopes, and Zanoni Dias. "COVID-19 X-ray Image Diagnostic with Deep Neural Networks." In Advances in Bioinformatics and Computational Biology, 57–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65775-8_6.

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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, 1036–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760191_151.

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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, 733–41. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2380-6_64.

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Rustichelli, Franco. "Structural Properties of Monolayers and Langmuir-Blodgett Films by X-Ray Scattering Techniques." In From Neural Networks and Biomolecular Engineering to Bioelectronics, 189–215. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-1088-2_16.

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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, 523–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21074-8_41.

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Tsukada, Ryotaro, Lekang Zou, and Hitoshi Iba. "Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects." In Natural Computing Series, 325–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3685-4_12.

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Kondo, Tadashi, and Abhijit S. Pandya. "Recognition of X-ray Images by Using Revised GMDH-type Neural Networks." In Lecture Notes in Computer Science, 849–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45226-3_116.

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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, 61–72. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003109327-5.

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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, 588–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100526.

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

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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.

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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.

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Cooley, Victoria, Stuart R. Stock, William Guise, Adya Verma, Tomas Wald, Ophir Klein, and Derk Joester. "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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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