scholarly journals GenNet framework: interpretable deep learning for predicting phenotypes from genetic data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arno van Hilten ◽  
Steven A. Kushner ◽  
Manfred Kayser ◽  
M. Arfan Ikram ◽  
Hieab H. H. Adams ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Complex Traits ◽  
Population Genomics ◽  
Endocrine System ◽  
Network Architectures ◽  
Eye Color ◽  
Learning Framework ◽  
Interpretable Models ◽  
Memory Efficient

AbstractApplying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.

scholarly journals GenNet framework: interpretable neural networks for phenotype prediction

2020 ◽  
Author(s):  
Arno van Hilten ◽  
Steven A. Kushner ◽  
Manfred Kayser ◽  
M. Arfan Ikram ◽  
Hieab H.H. Adams ◽  
...  
Keyword(s):  
Neural Networks ◽  
Population Genomics ◽  
Predictive Performance ◽  
Biological Knowledge ◽  
Network Architectures ◽  
Multiple Traits ◽  
Learning Framework ◽  
Prior Biological Knowledge ◽  
Good Predictive Performance ◽  
Memory Efficient

Neural networks have been seldomly leveraged in population genomics due to the computational burden and challenge of interpretability. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotype from genotype. In this framework, public prior biological knowledge is used to construct interpretable and memory-efficient neural network architectures. These architectures obtain good predictive performance for multiple traits and complex diseases, opening the door for neural networks in population genomics.

scholarly journals Enabling deeper learning on big data for materials informatics applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dipendra Jha ◽  
Vishu Gupta ◽  
Logan Ward ◽  
Zijiang Yang ◽  
Christopher Wolverton ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Materials Science ◽  
Prediction Models ◽  
Model Performance ◽  
Materials Informatics ◽  
Learning Framework ◽  
Significant Attention

AbstractThe application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures

2022 ◽  
pp. 1-27
Author(s):  
Clifford Bohm ◽  
Douglas Kirkpatrick ◽  
Arend Hintze
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mental Representations ◽  
Network Architectures ◽  
Information Theoretic ◽  
Time Points ◽  
The Past ◽  
Black Boxes ◽  
Past Experiences ◽  
Computational Systems

Abstract Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

scholarly journals Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation

2020 ◽  
Vol 34 (05) ◽  
pp. 8139-8146
Author(s):  
Duong Le ◽  
My Thai ◽  
Thien Nguyen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Knowledge Resources ◽  
Useful Knowledge ◽  
Learning Framework ◽  
Task Learning ◽  
Sense Disambiguation

The current deep learning works on metaphor detection have only considered this task independently, ignoring the useful knowledge from the related tasks and knowledge resources. In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. The first mechanism employs graph convolutional neural networks (GCN) with dependency parse trees to directly connect the words of interest with their important context words for metaphor detection. The GCN networks in this work also present a novel control mechanism to filter the learned representation vectors to retain the most important information for metaphor detection. The second mechanism, on the other hand, features a multi-task learning framework that exploits the similarity between word sense disambiguation and metaphor detection to transfer the knowledge between the two tasks. The extensive experiments demonstrate the effectiveness of the proposed techniques, yielding the state-of-the-art performance over several datasets.

scholarly journals Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenic Alopecia

2021 ◽  
Author(s):  
Meng Gao ◽  
Yue Wang ◽  
Haipeng Xu ◽  
Congcong Xu ◽  
Xianhong Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Quantitative Model ◽  
High Accuracy ◽  
Diameter Distribution ◽  
Androgenic Alopecia ◽  
Hair Density ◽  
Learning Framework ◽  
Quantitative Indicators

Since the results of basic and specific classification in male androgenic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. The aim of this study was to develop a deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images, and a quantitative model for predicting basic and specific classification in male androgenic alopecia with high accuracy.

scholarly journals Application of a Convolutional Neural Network for image classification for the analysis of collisions in High Energy Physics

2019 ◽  
Vol 214 ◽  
pp. 06017 ◽  
Author(s):  
Celia Fernández Madrazo ◽  
Ignacio Heredia ◽  
Lara Lloret ◽  
Jesús Marco de Lucas
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Energy Physics ◽  
Open Data ◽  
Feedforward Neural Networks ◽  
Relevant Information ◽  
High Energy ◽  
Particle Collisions ◽  
Learning Framework ◽  
Energy Physics

The application of deep learning techniques using convolutional neural networks for the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well-known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.

scholarly journals Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3891
Author(s):  
Yi-Jia Lin ◽  
Tai-Kuang Chao ◽  
Muhammad-Adil Khalil ◽  
Yu-Ching Lee ◽  
Ding-Zhi Hong ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Deep Learning ◽  
False Negative ◽  
Screening Method ◽  
Endocrine System ◽  
Visual Assessment ◽  
Jaccard Index ◽  
Learning Framework ◽  
Fast Screening ◽  
Difficult Time

Thyroid cancer is the most common cancer in the endocrine system, and papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, accounting for 70 to 80% of all thyroid cancer cases. In clinical practice, visual inspection of cytopathological slides is an essential initial method used by the pathologist to diagnose PTC. Manual visual assessment of the whole slide images is difficult, time consuming, and subjective, with a high inter-observer variability, which can sometimes lead to suboptimal patient management due to false-positive and false-negative. In this study, we present a fully automatic, efficient, and fast deep learning framework for fast screening of papanicolaou-stained thyroid fine needle aspiration (FNA) and ThinPrep (TP) cytological slides. To the authors’ best of knowledge, this work is the first study to build an automated deep learning framework for identification of PTC from both FNA and TP slides. The proposed deep learning framework is evaluated on a dataset of 131 WSIs, and the results show that the proposed method achieves an accuracy of 99%, precision of 85%, recall of 94% and F1-score of 87% in segmentation of PTC in FNA slides and an accuracy of 99%, precision of 97%, recall of 98%, F1-score of 98%, and Jaccard-Index of 96% in TP slides. In addition, the proposed method significantly outperforms the two state-of-the-art deep learning methods, i.e., U-Net and SegNet, in terms of accuracy, recall, F1-score, and Jaccard-Index (p<0.001). Furthermore, for run-time analysis, the proposed fast screening method takes 0.4 min to process a WSI and is 7.8 times faster than U-Net and 9.1 times faster than SegNet, respectively.

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