Generic placeholder image

Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Review Article

Review of the Applications of Deep Learning in Bioinformatics

Author(s): Yongqing Zhang, Jianrong Yan, Siyu Chen, Meiqin Gong, Dongrui Gao, Min Zhu* and Wei Gan

Volume 15 , Issue 8 , 2020

Page: [898 - 911] Pages: 14

DOI: 10.2174/1574893615999200711165743

Price: $65


Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

Keywords: Bioinformatics, biomedical, deep learning, biological data, high-throughput, high-dimensional.

Graphical Abstract
Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372(9): 793-5.
[] [PMID: 25635347]
Dahlberg AE, Dingman CW, Peacock AC. Electrophoretic characterization of bacterial polyribosomes in agarose-acrylamide composite gels. J Mol Biol 1969; 41(1): 139-47.
[] [PMID: 4979520]
Czworkowski J, Odom OW, Hardesty B. Fluorescence study of the topology of messenger RNA bound to the 30S ribosomal subunit of Escherichia coli. Biochemistry 1991; 30(19): 4821-30.
Licatalosi DD, Mele A, Fak JJ, et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 2008; 456(7221): 464-9.
[] [PMID: 18978773]
Stoltenburg R, Reinemann C, Strehlitz B. SELEX--a (r)evolutionary method to generate high-affinity nucleic acid ligands. Biomol Eng 2007; 24(4): 381-403.
[] [PMID: 17627883]
Ray D, Kazan H, Chan ET, et al. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat Biotechnol 2009; 27(7): 667-70.
[] [PMID: 19561594]
Bailey TL, Boden M, Buske FA, et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 2009; 37: 202-8.
Foat BC, Morozov AV, Bussemaker HJ. Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 2006; 22(14): e141-9.
[] [PMID: 16873464]
Corrado G, Tebaldi T, Costa F, Frasconi P, Passerini A. RNAcommender: genome-wide recommendation of RNA-protein interactions. Bioinformatics 2016; 32(23): 3627-34.
[PMID: 27503225]
Maticzka D, Lange SJ, Costa F, Backofen R. GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol 2014; 15(1): R17.
[] [PMID: 24451197]
Pan X, Shen HB. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics 2017; 18(1): 136-6.
[] [PMID: 28245811]
Ben-Bassat I, Chor B, Orenstein Y. A deep neural network approach for learning intrinsic protein-RNA binding preferences. Bioinformatics 2018; 34(17): i638-46.
[] [PMID: 30423078]
Pan X, Shen HB. Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks. Bioinformatics 2018; 34(20): 3427-36.
[] [PMID: 29722865]
Zhang Y, Qiao S, Ji S, Han N, Liu D, Zhou J. Identification of DNA-protein binding sites by bootstrap multiple convolutional neural networks on sequence information. Eng Appl of AI 2019; 79: 58-66.
Zhang Y, Zhang D, Mi G, et al. Using ensemble methods to deal with imbalanced data in predicting protein-protein interactions. Comput Biol Chem 2012; 36: 36-41.
[] [PMID: 22286086]
Gatys LA, Ecker AS, Bethge M. Image Style Transfer Using Convolutional Neural Networks. computer vision and pattern recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30; Las Vegas, NV, USA: IEEE 2016.
Graves A, Mohamed A-r, Hinton GE. Speech recognition with deep recurrent neural networks. IEEE International Conference on Acoustics, Speech and Signal Processing May 26-31 Vancouver, BC, Canada IEEE 2013.
Shao Y, Gao Y, Guo Y, Shi Y, Yang X, Shen D. Hierarchical lung field segmentation with joint shape and appearance sparse learning. IEEE Trans Med Imaging 2014; 33(9): 1761-80.
[] [PMID: 25181734]
Asgari E, Mofrad MRK. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS One 2015; 10(11)e0141287
[] [PMID: 26555596]
Angermueller C, Lee HJ, Reik W, Stegle O. Erratum to: DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol 2017; 18(1): 90.
[] [PMID: 28499443]
Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol 2016; 12(7): 878.
[] [PMID: 27474269]
Zhong B, Xing X, Love PED, Wang X, Luo H. Convolutional neural network: Deep learning-based classification of building quality problems. Adv Eng Inform 2019; 40: 46-57.
Cao Y, Jia L-L, Chen Y-X, et al. Recent advances of generative adversarial networks in computer vision IEEE Access 2019; 7: 14985-5006.
Jiang B, Zhang Z, Lin D, Tang J. Graph Learning-Convolutional Networks CoRR. arXiv preprint arXiv:1811.09971; 2018.
Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48.
[] [PMID: 28301734]
Scherer R. Computer vision methods for fast image classification and retrieval. Springer 2020; pp. 1-137.
Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics 2016; 32(12): 1832-9.
[] [PMID: 26873929]
Wang D, Zeng S, Xu C, et al. MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction. Bioinformatics 2017; 33(24): 3909-16.
[] [PMID: 29036382]
Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res 2016; 44(11): e107-7.
[] [PMID: 27084946]
Haque MM, Holder LB, Skinner MK, Cook DJ. Generalized query-based active learning to identify differentially methylated regions in DNA. IEEE/ACM Trans Comput Biol Bioinformatics 2013; 10(3): 632-44.
[] [PMID: 24091397]
Fout A, Byrd J, Shariat B, Benhur A. Protein Interface Prediction using Graph Convolutional Networks. Proceedings of the 31st International Conference on Neural Information Processing Systems ACM Digital Library 2017.
Li Z, Nguyen SP, Xu D, Shang Y. Protein Loop Modeling Using Deep Generative Adversarial Network. IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) November 6-8; Boston, MA, USA; IEEE 2017; pp. 1085-.
Glisovic T, Bachorik JL, Yong J, Dreyfuss G. RNA-binding proteins and post-transcriptional gene regulation. FEBS Lett 2008; 582(14): 1977-86.
[] [PMID: 18342629]
Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet 2016; 17(3): 175-88.
[] [PMID: 26806412]
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016; 35(5): 1240-51.
[] [PMID: 26960222]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[] [PMID: 27310171]
Chakravarty A, Sivaswamy J. RACE-Net: a recurrent neural network for biomedical image segmentation. IEEE J Biomed Health Inform 2019; 23(3): 1151-62.
[] [PMID: 29994410]
Singh VK, Romani S, Rashwan HA, et al. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. International Conference on Medical Image Computing and Computer-Assisted Intervention Cham, Switzerland; Springer 2018; pp. 833-40.
Rezaei M, Yang H, Harmuth K, Meinel C. Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation. Workshop on applications of computer vision; January 7-11; Waikoloa Village, HI, USA; IEEE: 2019.
Zhang Z, Li J, Zhong Z, Jiao Z, Gao X. A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation. Clin Orthop Relat Res 2019; 2019: 1.
Chen X, Williams BM, Vallabhaneni SR, Czanner G, Williams R, Zheng Y. Learning Active Contour Models for Medical Image Segmentation. 11632-40 2019.
Xu Y, Zhu J-Y, Chang EIC, Tu Z. Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. IEEE Conference on Computer Vision and Pattern Recognition June 16-21 Providence, RI, USA IEEE 2012; PP. 964-71.
Gao M, Bagci U, Lu L, et al. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 2018; 6(1): 1-6.
[] [PMID: 29623248]
Rhee S, Seo S, Kim S. Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence; July; IJCAI 2018; 3527-34.
Sheet D, Karri SPK, Katouzian A, Navab N, Ray AK, Chatterjee J. Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology. IEEE 12th International Symposium on Biomedical Imaging (ISBI); April 16-19; New York, NY, USA; IEEE 2015.
Sirinukunwattana K, Ahmed Raza SE, Tsan Y-W, Snead DR, Cree IA, Rajpoot NM. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans Med Imaging 2016; 35(5): 1196-206.
[] [PMID: 26863654]
Ciresan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. International Conference on Medical Image Computing and Computer-Assisted Intervention Berlin: Springer 2013; PP. 411-8.
Coley CW, Jin W, Rogers L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem Sci (Camb) 2018; 10(2): 370-7.
[] [PMID: 30746086]
Parisot S, Ktena SI, Ferrante E, et al. Disease prediction using graph convolutional networks: application to Autism Spectrum Disorder and Alzheimer’s disease. Med Image Anal 2018; 48: 117-30.
[] [PMID: 29890408]
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging 2016; 35(5): 1207-16.
[] [PMID: 26955021]
Chen J, Qi X, Tervonen O, Silven O, Zhao G, Pietikainen M. Thorax disease diagnosis using deep convolutional neural network. 910 Current Bioinformatics, 2020, Vol. 15, No. 8 Zhang et al. international conference of the IEEE engineering in medicine and biology society 2016; PP. 2287-.
Liang Z, Zhang G, Huang JX, Hu QV. Deep learning for healthcare decision making with EMRs. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) November 2-5 Belfast, UK IEEE 2014.
Nie L, Meng W, Zhang L, Yan S, Chua TS. Disease inference from health-related questions via sparse deep learning. IEEE Trans Knowl Data Eng 2015; 27(8): 2107-19.
Rahul PVSS, Sahu SK, Anand A. Biomedical event trigger identification using bidirectional recurrent neural network based models Assoc Comp Ling 2017; BioNLP 2017; 316-21
Guo D, Li M, Yu Y, et al. Disease Inference with Symptom Extraction and Bidirectional Recurrent Neural Network. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) December 3-6 Madrid, Spain IEEE 2018; PP. 864-8.
Willem L, Stijven S, Vladislavleva E, Broeckhove J, Beutels P, Hens N. Active learning to understand infectious disease models and improve policy making. PLOS Comput Biol 2014; 10(4)e1003563
[] [PMID: 24743387]
Choi E, Biswal S, Malin BA, Duke J, Stewart WF, Sun J. Generating Multi-label Discrete Patient Records using Generative Adversarial Networks. Proceedings of the 2nd Machine Learning for Healthcare Conference. August 18-19; Boston, MA, USA; MLResearch Press 2017.
Hwang U, Choi S, Lee HB, Yoon S. Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data 2017.
Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol Pharm 2016; 13(7): 2524-30.
[] [PMID: 27200455]
Wallach I, Dzamba M, Heifets A. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. Math Z 2015; 47(1): 34-46.
Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 2013; 53(7): 1563-75.
[] [PMID: 23795551]
Karimi M, Wu D, Wang Z, Shen Y. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 2019; 35(18): 3329-38.
[] [PMID: 30768156]
Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent Sci 2018; 4(1): 120-31.
[] [PMID: 29392184]
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997; 9(8): 1735-80.
[] [PMID: 9377276]
Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. International Conference on Artificial Neural Networks.
Li Y, Yu R, Shahabi C, Liu Y. Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting CoRR. 2017.
Yu B, Yin H, Zhu Z. Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting CoRR 2017.
Yan S, Xiong Y, Lin D. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition 2018; 7444-52.
Pan S, Jia W, Zhuy X, Zhang C, Yuz PS. Joint structure feature exploration and regularization for multi-task graph classification. IEEE Trans Knowl Data Eng 2016; 28(3): 715-28.
Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Nets 2014; 2672-80.
Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks 2016.
Mirza M, Osindero S. Conditional Generative Adversarial Nets. Comput Sci 2014; 2672-80.
Arjovsky M, Chintala S, Bottou L. Wasserstein GAN CoRR 2017.
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC. Improved Training of Wasserstein GANs 2017; 5767-77.
Pollastri G, Przybylski D, Rost B, Baldi P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 2002; 47(2): 228-35.
[] [PMID: 11933069]
Liu X. Deep Recurrent Neural Network for Protein Function Prediction from Sequence. bioRxiv 2017; 1-32.
Liang M, Hu X. Recurrent convolutional neural network for object recognition 2015; 3367-75.
Putin E, Mamoshina P, Aliper A, et al. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY) 2016; 8(5): 1021-33.
[] [PMID: 27191382]
Zhang X, Chou J, Wang F. Integrative Analysis of Patient Health Records and Neuroimages via Memory-based GraphConvolutional Network CoRR 2018.
Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 2018; 34(13): i457-66.
[] [PMID: 29949996]
Ghosh SS, Hua Y, Mukherjee SS, Robertson NM. IEGAN: Multipurpose perceptual quality image enhancement using generative Adversarial Network. 2019; 11-20.
Yang Q, Yan P, Zhang Y, et al. Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans Med Imaging 2018; 37(6): 1348-57.
[] [PMID: 29870364]
Mahapatra D, Bozorgtabar B, Hewavitharanage S, Garnavi R. Image super resolution using generative adversarial networks and local saliency maps for retinal image analysis. International Conference on Medical Image Computing and Computer-Assisted Intervention Cham, Switzerland; Springer 2017; PP. 382-90.
Ramponi G, Protopapas P, Brambilla M, Janssen R. CGAN: conditional generative adversarial network for data augmentation in noisy time series with irregular sampling CoRR. arXiv preprint arXiv:1811.08295 2018.
Benhenda M. ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? CoRR abs/170808227 2017.
Zhang Y, Qiao S, Ji S, Li Y. DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding. Int J Mach Learn Cybern 2019; 11: 845-51.
Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015; 33(8): 831-8.
[] [PMID: 26213851]
Xia Z, Li Y, Zhang B, et al. DeeReCT-PolyA: a robust and generic deep learning method for PAS identification. Bioinformatics 2019; 35(14): 2371-9.
[] [PMID: 30500881]
Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Gen Prot Bioinform 2018; 16(1): 17-32.
[] [PMID: 29522900]
Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 2011; 8(3): 184-7.
[] [PMID: 21364692]
Lanchantin J, Singh R, Lin Z, Qi Y. Deep Motif: Visualizing Genomic Sequence Classifications. arXiv: Learning 2016.
Zeng H, Edwards MD, Liu G, Gifford DK. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics 2016; 32(12): i121-7.
[] [PMID: 27307608]
Yuan XH, Yoseph B, Frey BJ. Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context. Oxford Acad 2011; 27(18): 2554-62.
Leung MKK, Xiong HY, Lee LJ, Frey BJ. Deep learning of the tissue-regulated splicing code. Bioinformatics 2014; 30(12): i121-9.
[] [PMID: 24931975]
Lee T, Yoon S. Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions 2015; 37: 2483-92.
Matthews HR. Protein kinases and phosphatases that act on histidine, lysine, or arginine residues in eukaryotic proteins: a possible regulator of the mitogen-activated protein kinase cascade. Pharmacol Ther 1995; 67(3): 323-50.
[] [PMID: 8577821]
Trost B, Kusalik A. Computational prediction of eukaryotic phosphorylation sites. Bioinformatics 2011; 27(21): 2927-35.
[] [PMID: 21926126]
Li T, Li F, Zhang X. Prediction of kinase-specific phosphorylation sites with sequence features by a log-odds ratio approach. Proteins 2008; 70(2): 404-14.
[] [PMID: 17680694]
Eipper BA. Posttranslational modification of proteins: expanding nature’s inventory. Q Rev Biol 2008; 403(4): 1.
Luo F, Wang M, Liu Y, Zhao XM, Li A. DeepPhos: prediction of protein phosphorylation sites with deep learning. Bioinformatics 2019; 35(16): 2766-73.
[] [PMID: 30601936]
Fout A, Byrd J, Shariat B, Ben-Hur A. Protein interface prediction using graph convolutional networks. Proceedings of the 31st International Conference on Neural Information Processing Systems ACM Digital Library 2017.
Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol 2012; 19(2): 236-48.
[] [PMID: 22078258]
Heath MD, Bowyer KW, Kopans DB, et al. Digital Mammography. In:Current status of the digital database for screening mammography. Dordrecht: Springer 1998; pp. 457-60.
Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal 2017; 35: 159-71.
[] [PMID: 27423113]
Roth HR, Lee CT, Shin H-C, et al. Anatomy-specific classification of medical images using deep convolutional nets 2015; 101-4.
Liao S, Gao Y, Oto A, Shen D. Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation. MICCAI 2013; pp. 254-61.
Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T. A Cross-Modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 2016; 35(1): 109-18.
[] [PMID: 26208306]
Cruzroa A, Gilmore H, Feldman M, Tomaszewski J, Madabhushi A. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Proceedings of SPIE 2014; pp. 139-44.
Xu J, Xiang L, Liu Q, et al. Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 2016; 35(1): 119-30.
[] [PMID: 26208307]
Jiang B, Wang X, Luo J, Zhang X, Xiong Y, Pang H. Convolutional Neural Networks in Automatic Recognition of Trans-differentiated Neural Progenitor Cells under Bright-Field Microscopy. International Conference on Instrumentation and Measurement Computer Communication and Control 2015.
van Grinsven MJ, van Ginneken B, Hoyng CB, Theelen T, Sanchez CI. Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 2016; 35(5): 1273-84.
[] [PMID: 26886969]
Yan Z, Zhan Y, Peng Z, et al. Xiang sean zhou. Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans Med Imaging 2016; 35(5): 1332-43.
[] [PMID: 26863652]
Chen CL, Mahjoubfar A, Tai LC, et al. Deep Learning in Label-free Cell Classification. Sci Rep 2016; 6: 21471.
[] [PMID: 26975219]
Sheng W, Fu L, Yao J, Yun L. The Application of Deep Learning in Biomedical Informatics. International Conference on Robots & Intelligent System (ICRIS).
Shin H-C, Lu L, Kim L, Seff A, Yao J, Summers RM. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database. Deep Learning and Convolutional Neural Networks for Medical Image Computing - Precision Medicine. High Performance and Large-Scale Datasets 2017; pp. 305-21.
Vandenberghe ME, Scott MLJ, Scorer PW, Söderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep 2017; 7: 45938.
[] [PMID: 28378829]
Clark NA, Hafner M, Kouril M, et al. GRcalculator: an online tool for calculating and mining dose-response data. BMC Cancer 2017; 17(1): 698.
[] [PMID: 29065900]
Schmidtke P, Barril X. Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 2010; 53(15): 5858-67.
[] [PMID: 20684613]
Zanni R, Gálvez-Llompart M, Gálvez J, García-Domenech R. QSAR multi-target in drug discovery: a review. Curr Comput Aided Drug Des 2014; 10(2): 129-36.
[] [PMID: 24724898]
Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 2007; 25(2): 197-206.
[] [PMID: 17287757]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-48.
[] [PMID: 9126849]
Naïm M, Bhat S, Rankin KN, et al. Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 1. Exploring the parameter space. J Chem Inf Model 2007; 47(1): 122-33.
[] [PMID: 17238257]
Åqvist J, Luzhkov VB, Brandsdal BO. Ligand binding affinities from MD simulations. Acc Chem Res 2002; 35(6): 358-65.
[] [PMID: 12069620]
Mamitsuka H, Delisi C, Kanehisa M. Data Mining for Systems Biology: Methods and Protocols. 2013.
Giansanti V, Castelli M, Beretta S, Merelli I. Comparing deep and machine learning approaches in bioinformatics: a miRNA-target prediction case study. Comput Sci (ICCS) 2019; 31-44.
Vangone A, Schaarschmidt J, Koukos P, et al. Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server. Bioinformatics 2019; 35(9): 1585-7.
[] [PMID: 31051038]
Zaki N, Yammahi MA, Habuza T. ProtRet: A Web Server for Retrieving Proteins in a Functional Complex. PACBB 2019; pp. 1-7.
Haehn D. Slice: drop: collaborative medical imaging in the browser ACM SIGGRAPH 2013 Computer Animation Festival July 2013.
Mullie L, Afilalo J. CoreSlicer: a web toolkit for analytic morphomics. BMC Med Imaging 2019; 19(1): 15.
[] [PMID: 30744586]
Wollny G, Kellman P, Ledesma-Carbayo M-J, Skinner MM, Hublin J-J, Hierl T. MIA - A free and open source software for gray scale medical image analysis. Source Code Biol Med 2013; 8(1): 20.
[] [PMID: 24119305]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy