scholarly journals Predicting Successes and Failures of Clinical Trials With Outer Product–Based Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Sangwoo Seo ◽  
Youngmin Kim ◽  
Hyo-Jeong Han ◽  
Woo Chan Son ◽  
Zhen-Yu Hong ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Drug Discovery ◽  
Convolutional Neural Network ◽  
Area Under The Curve ◽  
Good Prediction ◽  
Outer Product ◽  
Drug Candidates ◽  
Good Prediction Performance ◽  
Data Driven Approach

Despite several improvements in the drug development pipeline over the past decade, drug failures due to unexpected adverse effects have rapidly increased at all stages of clinical trials. To improve the success rate of clinical trials, it is necessary to identify potential loser drug candidates that may fail at clinical trials. Therefore, we need to develop reliable models for predicting the outcomes of clinical trials of drug candidates, which have the potential to guide the drug discovery process. In this study, we propose an outer product–based convolutional neural network (OPCNN) model which integrates effectively chemical features of drugs and target-based features. The validation results via 10-fold cross-validations on the dataset used for a data-driven approach PrOCTOR proved that our OPCNN model performs quite well in terms of accuracy, F1-score, Matthews correlation coefficient (MCC), precision, recall, area under the curve (AUC) of the receiver operating characteristic, and area under the precision–recall curve (AUPRC). In particular, the proposed OPCNN model showed the best performance in terms of MCC, which is widely used in biomedicine as a performance metric and is a more reliable statistical measure. Through 10-fold cross-validation experiments, the accuracy of the OPCNN model is as high as 0.9758, F1 score is as high as 0.9868, the MCC reaches 0.8451, the precision is as high as 0.9889, the recall is as high as 0.9893, the AUC is as high as 0.9824, and the AUPRC is as high as 0.9979. The results proved that our OPCNN model shows significantly good prediction performance on outcomes of clinical trials and it can be quite helpful in early drug discovery.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249404
Author(s):  
Jeongtae Son ◽  
Dongsup Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Discovery ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Docking Simulation ◽  
Computational Time ◽  
Binding Affinities

Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented as a graph of multiple adjacency matrices whose entries are affected by distances, and a feature matrix that describes the molecular properties of the atoms. We evaluated the predictive power of GraphBAR for protein-ligand binding affinities by using PDBbind datasets and proved the efficiency of the graph convolution. Given the computational efficiency of graph convolutional neural networks, we also performed data augmentation to improve the model performance. We found that data augmentation with docking simulation data could improve the prediction accuracy although the improvement seems not to be significant. The high prediction performance and speed of GraphBAR suggest that such networks can serve as valuable tools in drug discovery.

Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure

2020 ◽  
pp. 147592172096544
Author(s):  
Aravinda S Rao ◽  
Tuan Nguyen ◽  
Marimuthu Palaniswami ◽  
Tuan Ngo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Area Under The Curve ◽  
Network Models ◽  
Condition Assessment ◽  
Neural Network Models ◽  
Data Set ◽  
Inspection Method

With the growing number of aging infrastructure across the world, there is a high demand for a more effective inspection method to assess its conditions. Routine assessment of structural conditions is a necessity to ensure the safety and operation of critical infrastructure. However, the current practice to detect structural damages, such as cracks, depends on human visual observation methods, which are prone to efficiency, cost, and safety concerns. In this article, we present an automated detection method, which is based on convolutional neural network models and a non-overlapping window-based approach, to detect crack/non-crack conditions of concrete structures from images. To this end, we construct a data set of crack/non-crack concrete structures, comprising 32,704 training patches, 2074 validation patches, and 6032 test patches. We evaluate the performance of our approach using 15 state-of-the-art convolutional neural network models in terms of number of parameters required to train the models, area under the curve, and inference time. Our approach provides over 95% accuracy and over 87% precision in detecting the cracks for most of the convolutional neural network models. We also show that our approach outperforms existing models in literature in terms of accuracy and inference time. The best performance in terms of area under the curve was achieved by visual geometry group-16 model (area under the curve = 0.9805) and best inference time was provided by AlexNet (0.32 s per image in size of 256 × 256 × 3). Our evaluation shows that deeper convolutional neural network models have higher detection accuracies; however, they also require more parameters and have higher inference time. We believe that this study would act as a benchmark for real-time, automated crack detection for condition assessment of infrastructure.

scholarly journals DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

2021 ◽  
Author(s):  
Harrison Green ◽  
David Ryan Koes ◽  
Jacob D Durrant
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Lead Optimization ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Computer Aided

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is...

scholarly journals Prediction of Air pollution in Al-Hmadi City using Artificial Neural Network (ANN)

2020 ◽  
Vol 8 (4) ◽  
pp. 1390-1399
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Good Prediction ◽  
Air Velocity ◽  
Air Quality Prediction ◽  
Good Prediction Performance ◽  
Engineering Problems ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Carbon Dioxide Co2

Neural networks application (ANNs) has been used at recent years in several technology areas. Applying artificial neural networks (ANN) has too many environmental engineering problems provide remarkable success. The objective of this project is to predict and show the accuracy of using neural network in air quality prediction in Al Ahmadi city. Several parameters such as carbon dioxide (CO2), ammonia, benzene, relative humidity, temperature, air velocity and air direction are used in this study, for period of four years. The correlation coefficient was used to indicate the performance of ANN. The networks provide a good prediction performance with R value 0.93308 for CO2, 0.81805 and 0.923 for Ammonia and Benzene, respectively.

scholarly journals Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 48 ◽  
Author(s):  
Akiyoshi Hizukuri ◽  
Ryohei Nakayama
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Area Under The Curve ◽  
Computer Aided Diagnosis ◽  
Previous Method ◽  
Breast Lesions ◽  
Computer Aided ◽  
Invasive Carcinomas ◽  
Fully Connected ◽  
Aided Diagnosis

It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid.

A predictive deep-learning approach for homogenization of auxetic kirigami metamaterials with randomly oriented cuts

2020 ◽  
pp. 2150033
Author(s):  
Tongwei Liu ◽  
Shanwen Sun ◽  
Hang Liu ◽  
Ning An ◽  
Jinxiong Zhou
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Data Driven ◽  
Learning Approach ◽  
Good Prediction ◽  
The Finite Element Method ◽  
Input Output ◽  
Implicit Mapping ◽  
Mechanical Metamaterials ◽  
Data Driven Approach

This paper describes a data-driven approach to predict mechanical properties of auxetic kirigami metamaterials with randomly oriented cuts. The finite element method (FEM) was used to generate datasets, the convolutional neural network (CNN) was introduced to train these data, and an implicit mapping between the input orientations of cuts and the output Young’s modulus and Poisson’s ratio of the kirigami sheets was established. With this input–output relationship in hand, a quick estimation of auxetic behavior of kirigami metamaterials is straightforward. Our examples indicate that if the distributions of training and test datasets are close to each other, a good prediction is achievable. Our efforts provide a fast and reliable way to evaluate the homogenized properties of mechanical metamaterials with various microstructures, and thus accelerate the design of mechanical metamaterials for diverse applications.

scholarly journals Targeting transcription factors in cancer drug discovery

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Partha Mitra
Keyword(s):  
Clinical Trials ◽  
Transcription Factors ◽  
Drug Discovery ◽  
Clinical Research ◽  
Cancer Drug ◽  
Potential Drug ◽  
Drug Candidates ◽  
Cancer Drug Discovery ◽  
Clinical Level ◽  
Advanced Technologies

Cancer drug discovery is currently dominated by clinical trials or clinical research. Several potential drug candidates have been brought into the pipeline of drug discovery after showing very promising results at the pre-clinical level and are waiting to be tested in human clinical trials. Interestingly, among the potential drug candidates, a few of them have targeted transcription factors highlighting the fundamental undruggable nature of these molecules. However, using advanced technologies, researchers were recently successful in partly unlocking this undruggable nature, which was considered as a ‘grey area’ in the early days of drug discovery, and as a result, several potential candidates have emerged recently. The purpose of the review is to highlight some of the recently reported studies of targeting transcription factors in cancer and their promising outcomes.

scholarly journals Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [18F]FP-CIT Positron Emission Tomography

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1557
Author(s):  
Byung Wook Choi ◽  
Sungmin Kang ◽  
Hae Won Kim ◽  
Oh Dae Kwon ◽  
Huy Duc Vu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Area Under The Curve ◽  
Maximum Intensity Projection ◽  
Final Diagnosis ◽  
Maximum Intensity ◽  
Operating Characteristics ◽  
Perfect Agreement ◽  
Rater Reliability

The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [18F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [18F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [18F]FP-CIT PET, and its performance was comparable to that of NM physicians.

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