A convolutional neural network for predicting transcriptional regulators of genes in Arabidopsis transcriptome data reveals classification based on positive regulatory interactions

Expression Profiles ◽

Biological Data ◽

Expression Data ◽

Data Set

AbstractGene Regulatory networks that control gene expression are widely studied yet the interactions that make them up are difficult to predict from high throughput data. Deep Learning methods such as convolutional neural networks can perform surprisingly good classifications on a variety of data types and the matrix-like gene expression profiles would seem to be ideal input data for deep learning approaches. In this short study I compiled training sets of expression data using the Arabidopsis AtGenExpress global stress expression data set and known transcription factor-target interactions from the Arabidopsis PLACE database. I built and optimised convolutional neural networks with a best model providing 95 % accuracy of classification on a held-out validation set. Investigation of the activations within this model revealed that classification was based on positive correlation of expression profiles in short sections. This result shows that a convolutional neural network can be used to make classifications and reveal the basis of those calssifications for gene expression data sets, indicating that a convolutional neural network is a useful and interpretable tool for exploratory classification of biological data. The final model is available for download and as a web application.

Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network

Wireless Communications and Mobile Computing ◽

10.1155/2021/3138398 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Haibin Chang ◽

Ying Cui

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Classification ◽

Classification Algorithm ◽

Large Set ◽

Data Set ◽

Multilabel Learning ◽

Average Maximum

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

10.1101/2020.08.05.238519 ◽

2020 ◽

Author(s):

Hryhorii Chereda ◽

Annalen Bleckmann ◽

Kerstin Menck ◽

Júlia Perera-Bel ◽

Philip Stegmaier ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Neural Networks ◽

Deep Learning ◽

Gene Expression Data ◽

Molecular Network ◽

Patient Specific ◽

Expression Data ◽

Learning Methods

AbstractMotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient.ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrphttps://frankkramer-lab.github.io/MetaRelSubNetVis/[email protected]

A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis

Materials Today Proceedings ◽

10.1016/j.matpr.2020.10.263 ◽

2020 ◽

Author(s):

C. Gunavathi ◽

K. Sivasubramanian ◽

P. Keerthika ◽

C. Paramasivam

Keyword(s):

Neural Network ◽

Gene Expression ◽

Deep Learning ◽

Gene Expression Data ◽

Disease Diagnosis ◽

Expression Data ◽

Learning Methods

Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i3.3137 ◽

2021 ◽

Vol 5 (3) ◽

pp. 584-593

Author(s):

Naufal Hilmiaji ◽

Kemas Muslim Lhaksmana ◽

Mahendra Dwifebri Purbolaksono

Keyword(s):

Neural Network ◽

Neural Networks ◽

Performance Evaluation ◽

Deep Learning ◽

Text Classification ◽

Classification Model ◽

Learning Methods ◽

Expected Performance

especially with the advancement of deep learning methods for text classification. Despite some effort to identify emotion on Indonesian tweets, its performance evaluation results have not achieved acceptable numbers. To solve this problem, this paper implements a classification model using a convolutional neural network (CNN), which has demonstrated expected performance in text classification. To easily compare with the previous research, this classification is performed on the same dataset, which consists of 4,403 tweets in Indonesian that were labeled using five different emotion classes: anger, fear, joy, love, and sadness. The performance evaluation results achieve the precision, recall, and F1-score at respectively 90.1%, 90.3%, and 90.2%, while the highest accuracy achieves 89.8%. These results outperform previous research that classifies the same classification on the same dataset.

Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network

Mathematics ◽

10.3390/math9020189 ◽

2021 ◽

Vol 9 (2) ◽

pp. 189

Author(s):

Feng Liu ◽

Xuan Zhou ◽

Xuehu Yan ◽

Yuliang Lu ◽

Shudong Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

State Of The Art ◽

Image Steganalysis ◽

Process Information ◽

Effective Network ◽

Convolution Filters

Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance.

Image recognition algorithms based on deep learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2137/1/012056 ◽

2021 ◽

Vol 2137 (1) ◽

pp. 012056

Author(s):

Hongli Ma ◽

Fang Xie ◽

Tao Chen ◽

Lei Liang ◽

Jie Lu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Development Process ◽

Research Direction ◽

Learning Technology ◽

Convolutional Network ◽

Important Research Direction

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

Dog Breed Prediction using Convolutional Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d8058.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 318-322

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Image Classification ◽

Graphical Representation ◽

Outcome Analysis ◽

Dog Breed ◽

The One

Deep learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks (CNNs) gives single often used method for image classification and detection. In this exertion, we define a CNNbased approach for spotting dogs in per chance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm (CNN) gives good analysis accuracy for all the tested datasets

Small aircraft detection using deep learning

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-11-2020-0259 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Emre Kiyak ◽

Gulay Unal

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Transfer Learning ◽

Fine Tuning ◽

Deep Convolutional Neural Networks ◽

Content Type ◽

Training Time

Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.

Using local convolutional neural networks for genomic prediction

10.1101/2020.05.12.090118 ◽

2020 ◽

Author(s):

Torsten Pook ◽

Jan Freudenthal ◽

Arthur Korte ◽

Henner Simianer

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Genomic Prediction ◽

Linear Models ◽

Predictive Ability ◽

High Heritability ◽

Fully Connected

ABSTRACTThe prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. With increasing computational power and more and more data to potentially utilize, Machine Learning and especially Deep Learning have risen in popularity over the last few years. In this study, we are proposing the use of local convolutional neural networks for genomic prediction, as a region specific filter corresponds much better with our prior genetic knowledge of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000) and real Arabidopsis data (n = 2,039) for a variety of traits with the local convolutional neural network outperforming both multi layer perceptrons and convolutional neural networks for basically all considered traits. Linear models like the genomic best linear unbiased prediction that are often used for genomic prediction are outperformed by up to 24%. Highest gains in predictive ability was obtained in cases of medium trait complexity with high heritability and large training populations. However, for small dataset with 100 or 250 individuals for the training of the models, the local convolutional neural network is performing slightly worse than the linear models. Nonetheless, this is still 15% better than a traditional convolutional neural network, indicating a better performance and robustness of our proposed model architecture for small training populations. In addition to the baseline model, various other architectures with different windows size and stride in the local convolutional layer, as well as different number of nodes in subsequent fully connected layers are compared against each other. Finally, the usefulness of Deep Learning and in particular local convolutional neural networks in practice is critically discussed, in regard to multi dimensional inputs and outputs, computing times and other potential hazards.

Klasifikasi Citra Buah Pir Menggunakan Convolutional Neural Networks

Jurnal Telekomunikasi dan Komputer ◽

10.22441/incomtech.v11i1.10185 ◽

2021 ◽

Vol 11 (1) ◽

pp. 65

Author(s):

Syauqani Juliansyah ◽

Arif Dwi Laksito

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Sample Data

Buah Pir (Pyrus) adalah salah satu buah yang kaya akan nutrisi, seperti vitamin, niasin, asam pantotenat, dan folacin. Pir salah satu buah favorit dan banyak digemari diindonesia. Sebab, rasa yang khas dan identik dengan banyak air, masir, dan manis. Setiap jenis buah pir memiliki karakteristik yang berbeda, tentu setiap jenisnya mempunyai rasa yang khusus sehingga menghasilkan harga dan pengistimewaan berbeda dari setiap orang. Para petani buah pir tentu memiliki tempat penyimpanan untuk mengumpulkan hasil dari panen yang didapat. Sehingga para petani memisahkan jenis buah secara manual yang tentu akan membutuhkan waktu, kebosanan dan biaya tinggi. Pada penelitian ini bertujuan untuk mengatasi permasalahan klasifikasi buah secara manual tersebut dengan menggunakan salah satu algoritma Deep Learning dalam klasifikasi suatu gambar yaitu Convolutional Neural Network. Studi ini melakukan uji akurasi pada dua proses yaitu training dan testing dengan akurasi yang didapatkan yaitu 100% untuk training dan testing menggunakan 100 sample data baru dengan nilai akurasi 98%.