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Interpretable Convolutional Neural Networks for Effective Translation Initiation Site Prediction [article]

Jasper Zuallaert, Mijung Kim, Yvan Saeys, Wesley De Neve
2017 arXiv   pre-print
In this paper, we propose a novel approach for automatic prediction of translation initiation sites, leveraging convolutional neural networks that allow for automatic feature extraction.  ...  An important part in that determination process is the identification of translation initiation sites.  ...  The research activities described in this paper were funded by Ghent University Global Campus, Ghent University, imec, Flanders Innovation & Entrepreneurship (VLAIO), the Fund for Scientific Research-Flanders  ... 
arXiv:1711.09558v1 fatcat:jkb6ziikunekbdmi7anyjqea7q

Interpretable convolutional neural networks for effective translation initiation site prediction

Jasper Zuallaert, Mijung Kim, Yvan Saeys, Wesley De Neve
2017 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
In this paper, we propose a novel approach for automatic prediction of translation initiation sites, leveraging convolutional neural networks that allow for automatic feature extraction.  ...  An important part in that determination process is the identification of translation initiation sites.  ...  The research activities described in this paper were funded by Ghent University Global Campus, Ghent University, imec, Flanders Innovation & Entrepreneurship (VLAIO), the Fund for Scientific Research-Flanders  ... 
doi:10.1109/bibm.2017.8217833 dblp:conf/bibm/ZuallaertKSN17 fatcat:qgdnnj5skrcuno2c7sewwilhba

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.  ...  Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational  ...  Acknowledgements We thank Oliver Stegle for the comments on the text.  ... 
doi:10.1042/etls20160025 pmid:33525807 pmcid:PMC7289034 fatcat:qnw2yndsp5aqlnxxshtaipzctu

Guest Editorial: Deep Learning For Genomics

Barbara Di Camillo, Giuseppe Nicosia
2022 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
A first paper [1] addresses biological sequences annotation for the automatic detection of transcription start sites, translation initiation sites, methylation sites, etc. using the deep learning transformer-based  ...  [5] propose a graph convolutional network for drug response prediction on different cell lines.  ... 
doi:10.1109/tcbb.2021.3080094 fatcat:5m7znnottbc6tp2qtwrmkhkt3y

Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities [article]

Ameni Trabelsi, Mohamed Chaabane, Asa Ben Hur
2019 arXiv   pre-print
Existing methods fall into three classes: Some are based on Convolutional Neural Networks (CNNs), others use Recurrent Neural Networks (RNNs), and others rely on hybrid architectures combining CNNs and  ...  Results: In this study, We present a systematic exploration of various deep learning architectures for predicting DNA- and RNA-binding specificities.  ...  Methods based on Convolutional Neural Networks (CNNs) (LeCun et al. 1998) and Recurrent Neural Networks (RNNs) (Bullinaria 2013) have been proposed for the task of identifying protein binding sites  ... 
arXiv:1901.10526v1 fatcat:m3xolv63gzghvhdejupp7mjtf4

Enhancing the interpretability of transcription factor binding site prediction using attention mechanism

Sungjoon Park, Yookyung Koh, Hwisang Jeon, Hyunjae Kim, Yoonsun Yeo, Jaewoo Kang
2020 Scientific Reports  
To address these challenges, we propose TBiNet, an attention based interpretable deep neural network for predicting transcription factor binding sites.  ...  However, it is difficult to interpret the prediction results obtained from the previous models.  ...  For a user-friendly interface, we have also provided the web application of TBiNet at http://tbine t.korea .ac.kr. We thank Susan Kim for editing the manuscript.  ... 
doi:10.1038/s41598-020-70218-4 pmid:32770026 fatcat:ys5jry2zmja5rdn7zyxzgw6mmi

Predicting Active Sites in Photocatalytic Degradation Process Using an Interpretable Molecular-Image Combined Convolutional Neural Network

Zhuoying Jiang, Jiajie Hu, Anna Samia, Xiong (Bill) Yu
2022 Catalysts  
This paper describes the development of an interpretable neural-network model on the performance of photocatalytic degradation of organic contaminants by TiO2.  ...  The molecular structures of the organic contaminants are represented by molecular images, which are subsequently encoded by feeding into a special convolutional neural network (CNN), EfficientNet, to extract  ...  Acknowledgments: The authors acknowledge the support of US National Science Foundation for supporting this research. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/catal12070746 fatcat:wbrpr6h6vnffhotv4npgctneie

Deep Learning for Genomics: A Concise Overview [article]

Tianwei Yue, Haohan Wang
2018 arXiv   pre-print
Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome.  ...  In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing.  ...  One initial work by Horton and Kanehisa (1992) applied neural networks to predict E. coli promoter sites and provided a comparison of neural networks versus statistical methods.  ... 
arXiv:1802.00810v2 fatcat:u6s7pz2p6jdxzodz5k34it2hiu

Multi-column deep neural network for traffic sign classification

Dan Cireşan, Ueli Meier, Jonathan Masci, Jürgen Schmidhuber
2012 Neural Networks  
We use a fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way  ...  Multi-column deep neural networks As a basic building block we use a deep hierarchical neural network that alternates convolutional with max-pooling layers, reminiscent of the classic work of Hubel and  ...  final competition at the International Joint Conference on Neural Networks in 2011.  ... 
doi:10.1016/j.neunet.2012.02.023 pmid:22386783 fatcat:2mohjc4nvnawpnwyah6equv4oe

Artificial intelligence used in genome analysis studies

Edo D'Agaro
2018 The EuroBiotech Journal  
To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field  ...  A deep artificial neural network consists of a group of artificial neurons that mimic the properties of living neurons.  ...  Training of a neural network: One of the most well-known and effective methods for training neural networks is the socalled error retro-propagation algorithm (error back-propagation), which systematically  ... 
doi:10.2478/ebtj-2018-0012 fatcat:5vorc7y3ajgljcgscjg2wt25bi

Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks

Jiesi Luo, Wei Chen, Li Xue, Bin Tang
2019 BMC Bioinformatics  
Results: We present DeepCpf1, a deep convolution neural networks (CNN) approach to predict Cpf1 guide RNAs on-target activity and off-target effects using their matched and mismatched DNA sequences.  ...  Trained on published data sets, DeepCpf1 is superior to other machine learning algorithms and reliably predicts the most efficient and less off-target effects guide RNAs for a given gene.  ...  Acknowledgements We would like to acknowledge the members of Center for Bioinformatics and Systems Biology at Wake Forest School of Medicine.  ... 
doi:10.1186/s12859-019-2939-6 fatcat:nec3ett22ncxddox2itkefluom

Genomics enters the deep learning era

Etienne Routhier, Julien Mozziconacci
2022 PeerJ  
ACKNOWLEDGEMENTS We would like to thank Lou Duron and Alex Westbrook for their comments on the manuscript. We are also grateful to the reviewers for their invaluable work.  ...  These data have paved the way for the use of deep learning to predict these initiation sites. Zhang et al. (2017) developed a network capable of predicting initiation sites from mRNA sequences.  ...  Translation initiation of mRNAs does not always occur at the canonical AUG codon, as shown by the recent QTI-seq method which precisely maps translation initiation sites (Gao et al., 2015) .  ... 
doi:10.7717/peerj.13613 pmid:35769139 pmcid:PMC9235815 fatcat:74nzpo7rdrevfpvlestbjvxcp4

Predicting protein-ligand binding residues with deep convolutional neural networks

Yifeng Cui, Qiwen Dong, Daocheng Hong, Xikun Wang
2019 BMC Bioinformatics  
The classifier of DeepCSeqSite is based on a deep convolutional neural network. Several convolutional layers are stacked on top of each other to extract hierarchical features.  ...  The size of the effective context scope is expanded as the number of convolutional layers increases.  ...  Acknowledgements We are grateful to our labmates in DaSE for their suggestions.  ... 
doi:10.1186/s12859-019-2672-1 fatcat:rrf2wmjmuffg7fxuf2w4z3v4vm

Visualizing convolutional neural network protein-ligand scoring

Joshua Hochuli, Alec Helbling, Tamar Skaist, Matthew Ragoza, David Ryan Koes
2018 Journal of Molecular Graphics and Modelling  
Here we present three methods for visualizing how individual protein-ligand complexes are interpreted by 3D convolutional neural networks.  ...  Convolutional neural network (CNN) scoring functions in particular have shown promise in pose selection and affinity prediction for protein-ligand complexes.  ...  Acknowledgements We thank Jocelyn Sunseri, Justin Spiriti, and Paul Francoeur for their feedback during the preparation of the manuscript.  ... 
doi:10.1016/j.jmgm.2018.06.005 pmid:29940506 pmcid:PMC6343664 fatcat:t2edx34y4rbo7kz5yz3ziirwyy

DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data [article]

Jim Clauwaert, Gerben Menschaert, Willem Waegeman
2018 bioRxiv   pre-print
We present DeepRibo, a novel neural network applying ribosome profiling data that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes.  ...  The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and Shine-Dalgarno region into one model  ...  Yet, the models' performances indicate this effect to be minimal. Moreover, recent studies have discovered genes with multiple translation initiation sites [41, 14, 12] .  ... 
doi:10.1101/317180 fatcat:p6nqxo2dzjbidnrbjz74nznjyq
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