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arXiv 2020年

作者： Valdivia, Dulce I. Geiß, Manuela Rosales, Maribel Hernández Stadler, Peter F. Hellmuth, Marc Km. 9.6 Libramiento Norte Carretera Irapuato-León Irapuato GTOMX-36821 Mexico Bioinformatics Group Department of Computer Science & Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Software Competence Center Hagenberg GmbH Softwarepark 21 HagenbergA-4232 Austria CONACYT-Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro QRO76230 Mexico Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States School of Computing University of Leeds EC Stoner Building LeedsLS2 9JT United Kingdom

Cographs are exactly the hereditarily well-colored graphs, i.e., the graphs for which a greedy vertex coloring of every induced subgraph uses only the minimally necessary number of colors χ(G). We show that greedy colorings are a special case of the more general hierarchical vertex colorings, which recently were introduced in phylogenetic combinatorics. Replacing cotrees by modular decomposition trees generalizes the concept of hierarchical colorings to arbitrary graphs. We show that every graph has a modularly-minimal coloring σ satisfying |σ(M)| = χ(M) for every strong module M of G. This, in particular, shows that modularly-minimal colorings provide a useful device to design efficient coloring algorithms for certain hereditary graph classes. For cographs, the hierarchical colorings coincide with the modularly-minimal coloring. As a by-product, we obtain a simple linear-time algorithm to compute a modularly-minimal coloring of P4-sparse graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Coloring

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

arXiv

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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

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STATE OF THE CLIMATE IN 2021

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2022年第8期103卷 S1-S1页

作者： Blunden, Jessica NOAA/NESDIS National Centers for Environmental Information Asheville NC United States NOAA NESDIS National Centers for Environmental Information Silver Spring MD United States Met Office Hadley Centre Exeter United Kingdom Cooperative Institute for Satellite Earth System Studies North Carolina State University Asheville NC United States Communications and Outreach NOAA/NESDIS National Centers for Environmental Information Asheville NC United States Innovative Consulting and Management Services LLC NOAA/NESDIS National Centers for Environmental Information Asheville NC United States European Centre for Medium-Range Weather Forecasts Reading United Kingdom CMNS-Earth System Science Interdisciplinary Center University of Maryland College Park MD United States Department of Meteorology National Centre for Earth Observation University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton VA United States National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Life and Environmental Sciences Osaka Prefecture University Osaka Sakai Japan University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder CO United States Centro de Investigaciones sobre Desertificación Spanish National Research Council (CIDE CSIC-UV-Generalitat Valenciana) Climate Atmosphere and Ocean Laboratory (Climatoc-Lab) Moncada Valencia Spain Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich United Kingdom Joint Research Centre of the European Commission Ispra Italy GloH2O Almere Netherlands University of Reading Reading United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Cité Institut de physique du globe de Paris CNRS IGN Paris

Editors note: For easy download the posted pdf of the State of the Climate in 2021 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few mi... 详细信息

关键词： Greenhouse gases

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Output statistics of quantum annealers with disorder

arXiv

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arXiv 2018年

作者： Brugger, Jonathan Seidel, Christian Streif, Michael Wudarski, Filip A. Dittel, Christoph Buchleitner, Andreas Physikalisches Institut Albert-Ludwigs-Universität Freiburg Herrmann-Herder-Straße 3 FreiburgD-79104 Germany Data:Lab Volkswagen Group Ungererstr. 69 München80805 Germany Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziądzka 5/7 Toruń87–100 Poland EUCOR Centre for Quantum Science and Quantum Computing Albert-Ludwigs-Universität Freiburg Herrmann-Herder-Straße 3 FreiburgD-79104 Germany

We demonstrate that the output statistics of a standard quantum annealing protocol run on D-Wave 2000Q can be mimicked by static disorder garnishing an otherwise ideal device hardware – for a 10-qubit toy Hamiltonian as well as for problem instances with thousands of qubits. A Boltzmann-like distribution over distinct output states is shown to emerge with increasing disorder strength. Copyright © 2018, The Authors. All rights reserved.

关键词： Toys

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Structure-preserving image super-resolution via contextualized multi-task learning

arXiv

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arXiv 2017年

作者： Shi, Yukai Wang, Keze Chen, Chongyu Xu, Li Lin, Liang School of Data and Computer Science Sun Yat-sen University Guangzhou China Dept. of Computing Hong Kong Polytechnic University Hong Kong Sense-Time Group Limited China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China

Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials. Despite the breakthroughs of recently proposed SR methods using convolutional neural networks (CNNs), their generated results usually lack of preserving structural (high-frequency) details. In this paper, regarding global boundary context and residual context as complimentary information for enhancing structural details in image restoration, we develop a contextualized multi-task learning framework to address the SR problem. Specifically, our method first extracts convolutional features from the input LR image and applies one deconvolutional module to interpolate the LR feature maps in a content-adaptive way. Then, the resulting feature maps are fed into two branched sub-networks. During the neural network training, one sub-network outputs salient image boundaries and the HR image, and the other sub-network outputs the local residual map, i.e., the residual difference between the generated HR image and ground-truth image. On several standard benchmarks (i.e., Set5, Set14 and BSD200), our extensive evaluations demonstrate the effectiveness of our SR method on achieving both higher restoration quality and computational efficiency compared with several state-of-the-art SR approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Optical resolving power

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Chemception: A deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models

arXiv

引用

arXiv 2017年

作者： Goh, Garrett B. Siegel, Charles Vishnu, Abhinav Hodas, Nathan O. Baker, Nathan High Performance Computing Group Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Data Science and Analytics Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States

In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed "Chemception", a deep CNN for the prediction of chemical properties, using just the images of 2D drawings of molecules. We develop Chemception without providing any additional explicit chemistry knowledge, such as basic concepts like periodicity, or advanced features like molecular descriptors and fingerprints. We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40,000 compounds. When compared to multi-layer perceptron (MLP) deep neural networks trained with ECFP fingerprints, Chemception slightly outperforms in activity and solvation prediction and slightly underperforms in toxicity prediction. Having matched the performance of expert-developed QSAR/QSPR deep learning models, our work demonstrates the plausibility of using deep neural networks to assist in computational chemistry research, where the feature engineering process is performed primarily by a deep learning algorithm. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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