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An automated framework for efficiently designing deep convolutional neural networks in genomics [article]

Zijun Zhang, Christopher Y Park, Chandra L. Theesfeld, Olga G. Troyanskaya
2020 bioRxiv   pre-print
AMBER provides an efficient automated method for designing accurate deep learning models in genomics.  ...  Here, we present AMBER, a fully automated framework to efficiently design and apply CNNs for genomic sequences.  ...  To address this challenge, we developed an automatic architecture search 370 framework, AMBER, for efficiently designing optimal deep learning models in genomics.  ... 
doi:10.1101/2020.08.18.251561 fatcat:34s7gnb66jah7mkytm2lgmmony

Intelligent Health Care: Applications of Deep Learning in Computational Medicine

Sijie Yang, Fei Zhu, Xinghong Ling, Quan Liu, Peiyao Zhao
2021 Frontiers in Genetics  
Deep learning plays an increasingly important role in the field of medical health and has a broad prospect of application.  ...  The applications of deep learning in medical image, electronic health record, genomics, and drug development are studied, where the suggestion is that deep learning has obvious advantage in making full  ...  AUTHOR CONTRIBUTIONS XL conceived and designed the survey. SY analyzed the data and contributed reagents, materials, and analysis tools. All authors wrote the manuscript.  ... 
doi:10.3389/fgene.2021.607471 pmid:33912213 pmcid:PMC8075004 fatcat:f4kaii7egjff3dxiuzyp3puawq

Epithelial Segmentation From In Situ Hybridisation Histological Samples Using A Deep Central Attention Learning Approach

Tzu-Hsi Song, Gabriel Landini, Shereen Fouad, Hisham Mehanna
2019 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)  
[5] proposed a framework that used convolutional neural network (CNN) and superpixels to identify epithelial regions in breast cancer histopathological images.  ...  METHOD In order to efficiently and precisely segment epithelial regions, we propose a framework using a novel residual network.  ... 
doi:10.1109/isbi.2019.8759384 dblp:conf/isbi/SongLFM19 fatcat:ypbfjdf52bb35hh4mtylhbcgba

AMBIENT: Accelerated Convolutional Neural Network Architecture Search for Regulatory Genomics [article]

Zijun Zhang, Evan M. Cofer, Olga G. Troyanskaya
2021 bioRxiv   pre-print
Convolutional neural networks (CNN) have become a standard approach for modeling genomic sequences.  ...  Yet, the consumption of immense computing power is a major practical, financial, and environmental issue for deep learning.  ...  O.G.T. is a senior fellow of the Canadian Institute for Advanced Research (CIFAR) Genetic Networks program.  ... 
doi:10.1101/2021.02.25.432960 fatcat:e54olb2j2jdcplbkwdfrqbrape


2020 Journal of Critical Reviews  
In this paper the major areas of applications are elaborately discussed. A snapshot on various algorithms that are frequently used in deep learning is depicted.  ...  Artificial Intelligence is a super set of current technology comprising of machine learning in turn deep learning.  ...  It has been a great challenge to define recursive neural network, recurrent neural network,convolution neural network and unsupervised pre trained neural network.  ... 
doi:10.31838/jcr.07.05.246 fatcat:wui6u6tflbgzhd7tb6lbt5wd3u

Efficient Visual Recognition

Li Liu, Matti Pietikäinen, Jie Qin, Wanli Ouyang, Luc Van Gool
2020 International Journal of Computer Vision  
The paper "Spatially Adaptive Filter Units for Compact and Efficient Deep Neural Networks" by Domen Tabernik and Matej Kristan and Alesˇ Leonardis presents a new convolution filter composed of Displaced  ...  Towards efficient and robust facial landmark localization, the paper "Rectified Wing Loss for Efficient and Robust Facial Landmark Localization with Convolutional Neural Networks" by ZhenHua Feng, Josef  ... 
doi:10.1007/s11263-020-01351-w fatcat:mbcq6shmerbo5njayscgb3t4rq

A Dynamic DL-driven architecture to Combat Sophisticated Android Malware

Iram Bibi, Adnan Akhunzada, Jahanzaib Malik, Javed Iqbal, Arslan Musaddiq, Sung Won Kim
2020 IEEE Access  
INDEX TERMS Android malware, deep learning, recurrent neural network, convolutional neural network, deep neural network, mobile security. 129600 This work is licensed under a Creative Commons Attribution  ...  The GRU-based malware detection system outperforms with 98.99% detection accuracy for malware identification with a trivial trade off in speed efficiency.  ...  other DL-driven algorithms (i.e., Long short-term memory, Convolutional Neural Network, and Deep Neural Network) and current benchmarks.  ... 
doi:10.1109/access.2020.3009819 fatcat:42ezsnhtbfddll6g2z5ex4fhcy

Deep understanding of 3-D multimedia information retrieval on social media: implications and challenges

Ritika Wason, Vishal Jain, Gagandeep Singh Narula, Anupam Balyan
2019 Iran Journal of Computer Science  
The big explosion of multimedia data on the web has enabled social networks to gauge user likes, dislikes, and needs.  ...  This manuscript illustrates the MIR concept in terms of its application to social media. It further positions the current research in the field of 3D MIR.  ...  segmenta- tion of macular edema in optical coherence tomography [47] Proposes a convolutional neural network (CNN) that identifies intraretinal fluid (IRF) on OCT Convolutional neural network  ... 
doi:10.1007/s42044-019-00030-5 fatcat:e7kgskeqxbaznbjh3hrmhw3nke

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge [article]

Zijun Zhang, Linqi Zhou, Liangke Gou, Ying Nian Wu
2019 arXiv   pre-print
We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep learning models.  ...  BioNAS provides a useful tool for domain experts to inject their prior belief into automated machine learning and therefore making deep learning easily accessible to practitioners.  ...  In this work we aim to design knowledge dissimilarity functions for Convolutional Neural Network (CNN) models with genomic sequences as inputs.  ... 
arXiv:1909.00337v1 fatcat:j6h7iaudzzcpbfvyfkxu2oxvcu

Computational biology: deep learning

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
2017 Emerging Topics in Life Sciences  
This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems.  ...  Deep learning is the trendiest tool in a computational biologist's toolbox.  ...  Acknowledgements We thank Oliver Stegle for the comments on the text.  ... 
doi:10.1042/etls20160025 pmid:33525807 pmcid:PMC7289034 fatcat:qnw2yndsp5aqlnxxshtaipzctu

CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes

Victoria R Li, Zijun Zhang, Olga G Troyanskaya
2021 Bioinformatics  
Results Herein, deep multi-task convolutional neural networks (CNNs) and neural architecture search (NAS) were used to automate both feature and model engineering and create an end-to-end deep-learning  ...  framework, CROTON (CRISPR Outcomes Through cONvolutional neural networks).  ...  Natalie Sauerwald as well as all members of the Troyanskaya lab for their helpful discussions.  ... 
doi:10.1093/bioinformatics/btab268 pmid:34252931 fatcat:ozeb3g4x6zepvdfqp2lhmyzsba

Indoor Scene Recognition using ResNet-18

Hafiz Zeeshan Ali, Summiya Kabir, Ghufran Ullah
2021 International Journal of Research Publications  
The development of deep learning has made fine-tuning of CNN (Convolutional Neural Network) on target datasets a common way of solving classification problems.  ...  Scene Recognition is an area of visual recognition where we design and automate our system to recognize and identify the scene of the image.  ...  As deep Convolutional Neural Networks are designed to benefit from a huge amount of data and learn from it, we are trying to implement scene recognition and our goal is to achieve results with efficient  ... 
doi:10.47119/ijrp100691120211667 fatcat:wbt5tqfztresbdhi5rizr2usiq

Deep learning for computational biology

Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle
2016 Molecular Systems Biology  
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples.  ...  Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions.  ...  Central parameters of a neural network and recommended settings.  ... 
doi:10.15252/msb.20156651 pmid:27474269 pmcid:PMC4965871 fatcat:fyg72mq7zjglfmbldy3gb4lpqi

Table of Contents

2020 Proceedings of the IEEE  
|INVITED PAPER| This article provides an overview of use of deep, data-driven learning strategies in ultrasound systems, from the front-end to advanced applications.  ...  ) and image formation, to learning compressive codes for color Doppler acquisition to learning strategies for performing clutter suppression.  ...  |INVITED PAPER| This article provides an overview of efforts to advance the field of computational microscopy and optical sensing systems for microscopy using deep neural networks.  ... 
doi:10.1109/jproc.2019.2950647 fatcat:bbjxqmzvzbedfpurdt3fqzkqm4

Deep Learning and Its Applications in Biomedicine

Chensi Cao, Feng Liu, Hai Tan, Deshou Song, Wenjie Shu, Weizhong Li, Yiming Zhou, Xiaochen Bo, Zhi Xie
2018 Genomics, Proteomics & Bioinformatics  
Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data.  ...  Finally, we offer our perspectives for the future directions in the field of deep learning.  ...  The early framework for deep learning was built on artificial neural networks (ANNs) in the 1980s [2] , while the real impact of deep learning became apparent in 2006 [3, 4] .  ... 
doi:10.1016/j.gpb.2017.07.003 pmid:29522900 pmcid:PMC6000200 fatcat:kennxi3ga5dcpjdtnx27ngvcji
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