scholarly journals Segmenting Microarrays with Deep Neural Networks

2015 ◽  
Author(s):  
Andrew L Jones
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Biological Material ◽  
Deep Neural Networks ◽  
Source Code ◽  
Segmentation Problem

Microarray images consist of thousands of spots, each of which corresponds to a different biological material. The microarray segmentation problem is to work out which pixels belong to which spots, even in presence of noise and corruption. We propose a solution based on deep neural networks, which achieves excellent results both on simulated and experimental data. We have made the source code for our solution available on Github under a permissive license.

FAILURE PREDICTION OF COMPOSITE MATERIALS USING DEEP NEURAL NETWORKS

2021 ◽  
Author(s):  
ALLYSON FONTES ◽  
FARJAD SHADMEHRI
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Composite Materials ◽  
Composite Laminates ◽  
Deep Neural Networks ◽  
Mean Squared Error ◽  
Failure Criteria ◽  
Failure Strength ◽  
Failure Data ◽  
Frp Composite

Fiber-reinforced polymer (FRP) composite materials are increasingly used in engineering applications. However, an investigation into the precision of conventional failure criteria, known as the World-Wide Failure Exercise (WWFEI), revealed that current theories remain unable to predict failure within an acceptable degree of accuracy. Deep Neural Networks (DNN) are emerging as an alternate and time-efficient technique for predicting the failure strength of FRP composite materials. The present study examined the applicability of DNNs as a tool for creating a data-driven failure model for composite materials. The experimental failure data presented in the WWFE-I were used to develop the datadriven model. A fully connected DNN with 23 input units and 1 output unit trained with a constant learning rate (α=0.0001). The network’s inputs described the laminates and the loading conditions applied to the test specimen, whereas the output was the length of the failure vector (L=(σx+σy+τxy)0.5). The DNN’s performance was evaluated using the mean squared error on a subset of the experimental data unseen during training. Network configurations with a varying number of hidden layers and units per layer were evaluated. The DNN with 3 hidden layers and 20 units per hidden layer performed the best. In fact, the network’s predictions show good agreement with the experimental results. The failure boundaries generated by the DNN were compared to three conventional theories: the Tsai-Wu, Cuntze, and Puck theory. The DNN’s failure envelopes were found to fit the experimental data more closely than the above-mentioned theories. In sum, the DNN’s ability to fit higher-order polynomials to data separates it from conventional failure criteria. This characteristic makes DNNs an effective method for predicting the failure strength of composite laminates.

scholarly journals Construction of a neural network energy function for protein physics

2021 ◽  
Author(s):  
Huan Yang ◽  
Zhaoping Xiong ◽  
Francesco Zonta
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Energy Function ◽  
Protein Sequence ◽  
Deep Neural Networks ◽  
Protein Structures ◽  
Learning Method ◽  
Local Structures ◽  
The Neural Network

AbstractClassical potentials are widely used to describe protein physics, due to their simplicity and accuracy, but they are continuously challenged as real applications become more demanding with time. Deep neural networks could help generating alternative ways of describing protein physics. Here we propose an unsupervised learning method to derive a neural network energy function for proteins. The energy function is a probability density model learned from plenty of 3D local structures which have been extensively explored by evolution. We tested this model on a few applications (assessment of protein structures, protein dynamics and protein sequence design), showing that the neural network can correctly recognize patterns in protein structures. In other words, the neural network learned some aspects of protein physics from experimental data.

Biomedical image augmentation using Augmentor

2019 ◽  
Vol 35 (21) ◽  
pp. 4522-4524 ◽  
Author(s):  
Marcus D Bloice ◽  
Peter M Roth ◽  
Andreas Holzinger
Keyword(s):  
Neural Networks ◽  
Software Package ◽  
Biomedical Imaging ◽  
Deep Neural Networks ◽  
Source Code ◽  
Imaging Features ◽  
Software Library ◽  
Biomedical Image ◽  
To Come ◽  
Python Package

Abstract Motivation Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognized due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed. Results Here we present the Augmentor software package for image augmentation. It provides a stochastic, pipeline-based approach to image augmentation with a number of features that are relevant to biomedical imaging, such as z-stack augmentation and randomized elastic distortions. The software has been designed to be highly extensible meaning an operation that might be specific to a highly specialized task can easily be added to the library, even at runtime. Although it has been designed as a general software library, it has features that are particularly relevant to biomedical imaging and the techniques required for this domain. Availability and implementation Augmentor is a Python package made available under the terms of the MIT licence. Source code can be found on GitHub under https://github.com/mdbloice/Augmentor and installation is via the pip package manager (A Julia version of the package, developed in parallel by Christof Stocker, is also available under https://github.com/Evizero/Augmentor.jl).

scholarly journals Androgen Receptor Binding Prediction with Random Forest, Deep Neural Networks, and Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Alfonso T. García-Sosa
Keyword(s):  
Prostate Cancer ◽  
Neural Networks ◽  
Androgen Receptor ◽  
Random Forest ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Source Code ◽  
Protein Structures ◽  
Binding Prediction ◽  
Machine Learning Classifiers

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine learning classifiers and regressors and evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks on user-defined physicochemically-relevant features developed for this work outperform graph convolutional, random forest, and large featurizations. The results can help provide clues on risk of substances and better experimental design for toxicity assays. Source code and data are available at https://github.com/AlfonsoTGarcia-Sosa/ML

scholarly journals Turbulence aberration correction for vector vortex beams using deep neural networks on experimental data

2020 ◽  
Vol 28 (5) ◽  
pp. 7515 ◽  
Author(s):  
Yanwang Zhai ◽  
Shiyao Fu ◽  
Jianqiang Zhang ◽  
Xueting Liu ◽  
Heng Zhou ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Aberration Correction ◽  
Vortex Beams

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation
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