101
|
Papp L, Pötsch N, Grahovac M, Schmidbauer V, Woehrer A, Preusser M, Mitterhauser M, Kiesel B, Wadsak W, Beyer T, Hacker M, Traub-Weidinger T. Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning. J Nucl Med 2017; 59:892-899. [DOI: 10.2967/jnumed.117.202267] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 10/31/2017] [Indexed: 01/03/2023] Open
|
102
|
Takeda K, Takanami K, Shirata Y, Yamamoto T, Takahashi N, Ito K, Takase K, Jingu K. Clinical utility of texture analysis of 18F-FDG PET/CT in patients with Stage I lung cancer treated with stereotactic body radiotherapy. JOURNAL OF RADIATION RESEARCH 2017; 58:862-869. [PMID: 29036692 PMCID: PMC5710655 DOI: 10.1093/jrr/rrx050] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 07/06/2017] [Indexed: 06/04/2023]
Abstract
We evaluated the reproducibility and predictive value of texture parameters and existing parameters of 18F-FDG PET/CT images in Stage I non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT). Twenty-six patients with Stage I NSCLC (T1-2N0M0) were retrospectively analyzed. All of the patients underwent an 18F-FDG PET/CT scan before treatment and were treated with SBRT. Each tumor was delineated using PET Edge (MIM Software Inc., Cleveland, OH), and texture parameters were calculated using open-source code CGITA. From 18F-FDG PET/CT images, three conventional parameters, including maximum standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and four texture parameters, including entropy and dissimilarity (derived from a co-occurrence matrix) and high-intensity large-area emphasis (HILAE) and zone percentage (derived from a size-zone matrix) were analyzed. Reproducibility was evaluated using two independent delineations conducted by two observers. The ability to predict local control (LC), progression-free survival (PFS) and overall survival (OS) was tested for each parameter. All of the seven parameters except zone percentage showed good reproducibility, with intraclass correlation coefficient values >0.8. In univariate analysis, only HILAE was a significant predictor for LC. Histology, dose fractionation, and maximum SUV were associated with PFS, and histology and dose fractionation were associated with OS. We showed that texture parameters derived from 18F-FDG PET/CT were reproducible and potentially beneficial for predicting LC in Stage I lung cancer patients treated with SBRT.
Collapse
Affiliation(s)
- Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kentaro Takanami
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Yuko Shirata
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kei Takase
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| |
Collapse
|
103
|
Lovinfosse P, Polus M, Van Daele D, Martinive P, Daenen F, Hatt M, Visvikis D, Koopmansch B, Lambert F, Coimbra C, Seidel L, Albert A, Delvenne P, Hustinx R. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging 2017; 45:365-375. [PMID: 29046927 DOI: 10.1007/s00259-017-3855-5] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 10/09/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE The aim of this study was to investigate the prognostic value of baseline 18F-FDG PET/CT textural analysis in locally-advanced rectal cancer (LARC). METHODS Eighty-six patients with LARC underwent 18F-FDG PET/CT before treatment. Maximum and mean standard uptake values (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), histogram-intensity features, as well as 11 local and regional textural features, were evaluated. The relationships of clinical, pathological and PET-derived metabolic parameters with disease-specific survival (DSS), disease-free survival (DFS) and overall survival (OS) were assessed by Cox regression analysis. Logistic regression was used to predict the pathological response by the Dworak tumor regression grade (TRG) in the 66 patients treated with neoadjuvant chemoradiotherapy (nCRT). RESULTS The median follow-up of patients was 41 months. Seventeen patients (19.7%) had recurrent disease and 18 (20.9 %) died, either due to cancer progression (n = 10) or from another cause while in complete remission (n = 8). DSS was 95% at 1 year, 93% at 2 years and 87% at 4 years. Weight loss, surgery and the texture parameter coarseness were significantly associated with DSS in multivariate analyses. DFS was 94 % at 1 year, 86 % at 2 years and 79 % at 4 years. From a multivariate standpoint, tumoral differentiation and the texture parameters homogeneity and coarseness were significantly associated with DFS. OS was 93% at 1 year, 87% at 2 years and 79% after 4 years. cT, surgery, SUVmean, dissimilarity and contrast from the neighborhood intensity-difference matrix (contrastNGTDM) were significantly and independently associated with OS. Finally, RAS-mutational status (KRAS and NRAS mutations) and TLG were significant predictors of pathological response to nCRT (TRG 3-4). CONCLUSION Textural analysis of baseline 18F-FDG PET/CT provides strong independent predictors of survival in patients with LARC, with better predictive power than intensity- and volume-based parameters. The utility of such features, especially coarseness, should be confirmed by larger clinical studies before considering their potential integration into decisional algorithms aimed at personalized medicine.
Collapse
Affiliation(s)
- Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics CHU, University of Liège, B35 Domaine Universitaire du Sart-Tilman, 4000, Liege, Belgium.
| | - Marc Polus
- Department of Gastro-enterology, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Daniel Van Daele
- Department of Gastro-enterology, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Philippe Martinive
- Division of Radiation Oncology, Department of Medical Physics, CHU and University of Liège, Liège, Belgium
| | - Frédéric Daenen
- Department of Nuclear Medicine, Centre Hospitalier Régional de la Citadelle, Liège, Belgium
| | | | | | - Benjamin Koopmansch
- Center for Human Genetic, Molecular Haemato-Oncology Unit, UniLab Liège, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Frédéric Lambert
- Center for Human Genetic, Molecular Haemato-Oncology Unit, UniLab Liège, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Carla Coimbra
- Department of Abdominal Surgery and Transplantation, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Laurence Seidel
- Department of Biostatistics and Medico-economic Information, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Adelin Albert
- Department of Biostatistics and Medico-economic Information, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Philippe Delvenne
- Department of Pathology, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics CHU, University of Liège, B35 Domaine Universitaire du Sart-Tilman, 4000, Liege, Belgium
| |
Collapse
|
104
|
Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD. Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging. Radiology 2017; 285:923-931. [PMID: 28678669 DOI: 10.1148/radiol.2017162664] [Citation(s) in RCA: 147] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images. Examinations were processed by using a convolutional neural network (deep learning) using two different window and level configurations (brain window and stroke window). AI algorithm performance was tested on a separate dataset containing 50 examinations with HMH findings, 15 with SAI findings, and 35 with noncritical findings. Results Final algorithm performance for HMH showed 90% (45 of 50) sensitivity (95% confidence interval [CI]: 78%, 97%) and 85% (68 of 80) specificity (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 with the brain window. For SAI, the best performance was achieved with the stroke window showing 62% (13 of 21) sensitivity (95% CI: 38%, 82%) and 96% (27 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81. Conclusion AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings. © RSNA, 2017 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Luciano M Prevedello
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| | - Barbaros S Erdal
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| | - John L Ryu
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| | - Kevin J Little
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| | - Mutlu Demirer
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| | - Songyue Qian
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| | - Richard D White
- From the Department of Radiology, The Ohio State University Wexner Medical Center, 395 W 12th Ave, 4th Floor, Room 422, Columbus, OH 43210
| |
Collapse
|
105
|
Abstract
The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.
Collapse
|
106
|
Lee G, Lee HY, Ko ES, Jeong WK. Radiomics and imaging genomics in precision medicine. PRECISION AND FUTURE MEDICINE 2017. [DOI: 10.23838/pfm.2017.00101] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
|
107
|
Liu X, Jiang J, Zhang K, Long E, Cui J, Zhu M, An Y, Zhang J, Liu Z, Lin Z, Li X, Chen J, Cao Q, Li J, Wu X, Wang D, Lin H. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One 2017; 12:e0168606. [PMID: 28306716 PMCID: PMC5356999 DOI: 10.1371/journal.pone.0168606] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 11/11/2016] [Indexed: 12/12/2022] Open
Abstract
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.
Collapse
Affiliation(s)
- Xiyang Liu
- School of Computer Science and Technology, Xidian University, Xi’an, China
- School of Software, Xidian University, Xi’an, China
| | - Jiewei Jiang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Kai Zhang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Erping Long
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jiangtao Cui
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Mingmin Zhu
- School of Mathematics and Statistics, Xidian University, Xi’an, China
| | - Yingying An
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Jia Zhang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Zhenzhen Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhuoling Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jingjing Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Qianzhong Cao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jing Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Dongni Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
108
|
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 2017; 44:151-165. [PMID: 27271051 PMCID: PMC5283691 DOI: 10.1007/s00259-016-3427-0] [Citation(s) in RCA: 320] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/18/2016] [Indexed: 02/07/2023]
Abstract
After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.
Collapse
Affiliation(s)
- Mathieu Hatt
- INSERM, UMR 1101, LaTIM, University of Brest IBSAM, Brest, France.
| | - Florent Tixier
- Nuclear Medicine, University Hospital, Poitiers, France
- Medical school, EE DACTIM, University of Poitiers, Poitiers, France
| | - Larry Pierce
- Imaging Research Laboratory, University of Washington, Seattle, WA, USA
| | - Paul E Kinahan
- Imaging Research Laboratory, University of Washington, Seattle, WA, USA
| | - Catherine Cheze Le Rest
- Nuclear Medicine, University Hospital, Poitiers, France
- Medical school, EE DACTIM, University of Poitiers, Poitiers, France
| | | |
Collapse
|
109
|
Hatt M, Tixier F, Visvikis D, Cheze Le Rest C. Radiomics in PET/CT: More Than Meets the Eye? J Nucl Med 2016; 58:365-366. [PMID: 27811126 DOI: 10.2967/jnumed.116.184655] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 10/11/2016] [Indexed: 01/07/2023] Open
Affiliation(s)
- Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, IBSAM, Brest, France; and
| | - Florent Tixier
- Academic Department of Nuclear Medicine, CHU Poitiers, Poitiers, France
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, IBSAM, Brest, France; and
| | | |
Collapse
|
110
|
van Rossum PSN, Xu C, Fried DV, Goense L, Court LE, Lin SH. The emerging field of radiomics in esophageal cancer: current evidence and future potential. Transl Cancer Res 2016; 5:410-423. [PMID: 30687593 DOI: 10.21037/tcr.2016.06.19] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
'Radiomics' is the name given to the emerging field of extracting additional information from standard medical images using advanced feature analysis. This innovative form of quantitative image analysis appears to have future potential for clinical practice in patients with esophageal cancer by providing an additional layer of information to the standard imaging assessment. There is a growing body of evidence suggesting that radiomics may provide incremental value for staging, predicting treatment response, and predicting survival in esophageal cancer, for which the current work-up has substantial limitations. This review outlines the available evidence and future potential for the application of radiomics in the management of patients with esophageal cancer. In addition, an overview of the current evidence on the importance of reproducibility of image features and the substantial influence of varying smoothing scales, quantization levels, and segmentation methods is provided.
Collapse
Affiliation(s)
- Peter S N van Rossum
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA.,Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cai Xu
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA.,Department of Radiation Oncology, Cancer Hospital & Institute, Chinese Academy of Medical Science, Beijing 100021, China
| | - David V Fried
- Department of Radiation Oncology, University of North Carolina, Chapel Hill (North Carolina), USA
| | - Lucas Goense
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA
| | - Steven H Lin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA
| |
Collapse
|
111
|
Gawehn E, Hiss JA, Schneider G. Deep Learning in Drug Discovery. Mol Inform 2015; 35:3-14. [PMID: 27491648 DOI: 10.1002/minf.201501008] [Citation(s) in RCA: 309] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
Abstract
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
Collapse
Affiliation(s)
- Erik Gawehn
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.
| |
Collapse
|