Deep learning based DNA:RNA triplex forming potential prediction

Abstract Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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Deep learning based DNA:RNA triplex forming potential prediction

10.21203/rs.3.rs-41662/v2 ◽

2020 ◽

Author(s):

Yu ZHANG ◽

Yahui Long ◽

Chee Keong Kwoh

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Roc Curves ◽

Triplex Dna ◽

Triplex Formation ◽

Machine Learning Model ◽

Non Coding Rnas ◽

High Level ◽

Integrated Program ◽

Fold Cross Validation

Abstract Background: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and ii) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data.Results: In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the 5-fold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions: The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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Deep learning based DNA:RNA triplex forming potential prediction

10.21203/rs.3.rs-41662/v3 ◽

2020 ◽

Author(s):

Yu ZHANG ◽

Yahui Long ◽

Chee Keong Kwoh

Keyword(s):

Cross Validation ◽

Roc Curves ◽

Triplex Dna ◽

Triplex Formation ◽

Machine Learning Model ◽

Non Coding Rnas ◽

High Level ◽

Cis And Trans ◽

Integrated Program ◽

Fold Cross Validation

Abstract Background: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and ii) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results: In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the 5-fold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarise the cis and trans targeting of triplexes lncRNAs. Conclusions: The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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Deep learning based DNA:RNA triplex forming potential prediction

10.21203/rs.3.rs-41662/v1 ◽

2020 ◽

Author(s):

Yu ZHANG ◽

Yahui Long ◽

Chee Keong Kwoh

Keyword(s):

Machine Learning ◽

Deep Neural Networks ◽

Cross Validation ◽

Roc Curves ◽

Triplex Formation ◽

Machine Learning Model ◽

Non Coding Rnas ◽

High Level ◽

Integrated Program ◽

Fold Cross Validation

Abstract Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex forming lncRNAs, but the limited number of experimental verified triplex forming lncRNA indicate that maybe not all of them can from triplex in practice, and ii) their prediction only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex forming lncRNAs and DNA sites based on the experimentally verified data, where their high-level features are learned by the deep neural networks. In the 5-fold cross validation, its average values of Area Under the ROC curves and PRC curves for triplex forming lncRNA and DNA sites predictions are 0.9949 and 0.9999, 0.8775 and 0.9692, respectively. Besides, we also briefly summarized the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP can predict the most likely triplex forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities, and predict the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

10.26434/chemrxiv.9763094 ◽

2019 ◽

Author(s):

Siddhartha Laghuvarapu ◽

Yashaswi Pathak ◽

U. Deva Priyakumar

Keyword(s):

Machine Learning ◽

Density Functional ◽

Computational Cost ◽

Geometry Optimization ◽

Dft Methods ◽

Energy Prediction ◽

Machine Learning Model ◽

Equilibrium Structures ◽

High Level ◽

Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

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Generation of geometric interpolations of building types with deep variational autoencoders

Design Science ◽

10.1017/dsj.2020.31 ◽

2020 ◽

Vol 6 ◽

Author(s):

Jaime de Miguel Rodríguez ◽

Maria Eugenia Villafañe ◽

Luka Piškorec ◽

Fernando Sancho Caparrini

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Large Data ◽

Learning Model ◽

Large Data Sets ◽

Data Sets ◽

Connectivity Map ◽

Data Set ◽

3D Objects ◽

Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

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Breath can discriminate tuberculosis from other lower respiratory illness in children

Scientific Reports ◽

10.1038/s41598-021-80970-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Carly A. Bobak ◽

Lili Kang ◽

Lesley Workman ◽

Lindy Bateman ◽

Mohammad S. Khan ◽

...

Keyword(s):

Machine Learning ◽

Lower Respiratory Tract ◽

Cross Validation ◽

Pediatric Patients ◽

Respiratory Illness ◽

Health Crisis ◽

Machine Learning Model ◽

Childhood Tb ◽

Pediatric Tb ◽

Pediatric Tuberculosis

AbstractPediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred

Risks ◽

10.3390/risks7030095 ◽

2019 ◽

Vol 7 (3) ◽

pp. 95 ◽

Cited By ~ 1

Author(s):

Jacky H. L. Poon

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Life Insurance ◽

Predictive Power ◽

Real Life ◽

Model Framework ◽

Key Stakeholders ◽

Machine Learning Model ◽

Loss Reserving ◽

Risk Pricing

In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders and regulators. We present a granular machine learning model framework to jointly predict loss development and segment risk pricing. Generalising the Payments per Claim Incurred (PPCI) loss reserving method with risk variables and residual neural networks, this combines interpretable linear and sophisticated neural network components so that the ‘unexplainable’ component can be identified and regularised with a separate penalty. The model is tested for a real-life insurance dataset, and generally outperformed PPCI on predicting ultimate loss for sufficient sample size.

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Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.662749 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yuhong Huang ◽

Wenben Chen ◽

Xiaoling Zhang ◽

Shaofu He ◽

Nan Shao ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cross Validation ◽

Learning Model ◽

Training Dataset ◽

Tumor Shrinkage ◽

Clinicopathologic Characteristics ◽

Testing Dataset ◽

Machine Learning Model ◽

Fold Cross Validation

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

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CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Electronics ◽

10.3390/electronics9122200 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2200

Author(s):

Alireza Ghaffari ◽

Yvon Savaria

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Automatic Synthesis ◽

Software Libraries ◽

Learning Software ◽

High Level

Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms.

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