PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA–lncRNA interaction prediction

2020 ◽  
Vol 36 (10) ◽  
pp. 2986-2992 ◽  
Author(s):  
Qiang Kang ◽  
Jun Meng ◽  
Jun Cui ◽  
Yushi Luan ◽  
Ming Chen
Keyword(s):  
Biological Activities ◽  
Learning Model ◽  
Supplementary Information ◽  
Fuzzy Decision ◽  
Interaction Prediction ◽  
Machine Learning Model ◽  
Plant Mirna ◽  
Mechanism Of Interaction ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Motivation The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA–lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. Results This article proposes a new method for fulfilling plant miRNA–lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA–lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time–polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. Availability and implementation The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Noha E. El-Attar ◽  
Mohamed K. Hassan ◽  
Othman A. Alghamdi ◽  
Wael A. Awad
Keyword(s):  
Deep Learning ◽  
Essential Oil ◽  
Biological Activities ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Chemical Compositions ◽  
Linear Processes ◽  
Learning Techniques ◽  
Important Trend ◽  
Deep Learning Model

AbstractReliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

scholarly journals Explanation-Based Human Debugging of NLP Models: A Survey

2021 ◽  
Vol 9 ◽  
pp. 1508-1528
Author(s):  
Piyawat Lertvittayakumjorn ◽  
Francesca Toni
Keyword(s):  
Learning Model ◽  
Training Data ◽  
Three Dimensions ◽  
Future Research ◽  
Experimental Setting ◽  
Open Problems ◽  
Research Directions ◽  
Machine Learning Model ◽  
Future Research Directions ◽  
Deep Learning Model

Abstract Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions.

scholarly journals Development of Machine Learning Model for Diagnostic Disease Prediction Based on Laboratory Tests

2021 ◽  
Author(s):  
Dong Jin Park ◽  
Min Woo Park ◽  
Homin Lee ◽  
Young-Jin Kim ◽  
Yeongsic Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Linear Models ◽  
Confusion Matrix ◽  
Learning Model ◽  
Disease Classification ◽  
Machine Learning Model ◽  
Tree Models ◽  
Hidden Layer ◽  
Deep Learning Model

Abstract Artificial intelligence is a concept that includes machine learning and deep learning. The deep learning model used in this study corresponds to DNN (deep neural network) by utilizing two or more hidden layers. In this study, MLP (multi-layer perceptron) and machine learning models (XGBoost, LGBM) were used. An MLP consists of at least three layers: an input layer, a hidden layer, and an output layer. In general, tree models or linear models using machine learning are widely used for classification. We analyzed our data by applying deep learning (MLP) to improve the performance, which showed good results. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Here, we present a protocol to confirm that the use of deep learning can show good performance in disease classification using hospital numerical structured data (laboratory test).

scholarly journals COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Madallah Alruwaili ◽  
Abdulaziz Shehab ◽  
Sameh Abd El-Ghany
Keyword(s):  
Deep Learning ◽  
Computational Cost ◽  
Learning Model ◽  
Negative Effect ◽  
Chest X Ray ◽  
Medical Image Recognition ◽  
Diagnosis Accuracy ◽  
Polymerase Chain ◽  
Deep Learning Model ◽  
Low Computational Cost

The COVID-19 pandemic has a significant negative effect on people’s health, as well as on the world’s economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers’ attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.

scholarly journals DeeReCT-PolyA: a robust and generic deep learning method for PAS identification

2018 ◽  
Vol 35 (14) ◽  
pp. 2371-2379 ◽  
Author(s):  
Zhihao Xia ◽  
Yu Li ◽  
Bin Zhang ◽  
Zhongxiao Li ◽  
Yuhui Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gene Expression Regulation ◽  
Learning Model ◽  
Supplementary Information ◽  
Single Model ◽  
Sequence Logos ◽  
Underlying Mechanisms ◽  
Deep Learning Model ◽  
Human And Mouse ◽  
Human Specific

Abstract Motivation Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts’ end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. Results In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets. Availability and implementation https://github.com/likesum/DeeReCT-PolyA Supplementary information Supplementary data are available at Bioinformatics online.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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