CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles

Abstract Motivation Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but struggle when evaluated against others such as Matthew correlation coefficient (MCC), accuracy (ACC), positive predictive value (PPV) and specificity (SPE). Therefore, there is a need for a method that can efficiently aggregate information gathered from models based on different chemical representations and boost hERG toxicity prediction over a range of performance metrics. Results In this paper, we propose a deep learning framework based on step-wise training to predict hERG channel blocking activity of small molecules. Our approach utilizes five individual deep learning base models with their respective base features and a separate neural network to combine the outputs of the five base models. By using three external independent test sets with potency activity of IC50 at a threshold of 10 $$\upmu$$ μ m, our method achieves better performance for a combination of classification metrics. We also investigate the effective aggregation of chemical information extracted for robust hERG activity prediction. In summary, CardioTox net can serve as a robust tool for screening small molecules for hERG channel blockade in drug discovery pipelines and performs better than previously reported methods on a range of classification metrics.

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Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes

Acta Radiologica ◽

10.1177/0284185120983977 ◽

2021 ◽

pp. 028418512098397

Author(s):

Yang Li ◽

Hong Qiu ◽

Zhihui Hou ◽

Jianfeng Zheng ◽

Jianan Li ◽

...

Keyword(s):

Deep Learning ◽

Unstable Angina ◽

Fractional Flow Reserve ◽

Predictive Value ◽

Computed Tomographic Angiography ◽

Fractional Flow ◽

Computed Tomographic ◽

Flow Reserve ◽

Sensitivity Specificity ◽

Tomographic Angiography

Background Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR). Purpose To examine the ability of a DL-based CT-FFR in detecting hemodynamic changes of stenosis. Material and Methods This study included 73 patients (85 vessels) who were suspected of coronary artery disease (CAD) and received CCTA followed by invasive FFR measurements within 90 days. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristics curve (AUC) were compared between CT-FFR and CCTA. Thirty-nine patients who received drug therapy instead of revascularization were followed for up to 31 months. Major adverse cardiac events (MACE), unstable angina, and rehospitalization were evaluated and compared between the study groups. Results At the patient level, CT-FFR achieved 90.4%, 93.6%, 88.1%, 85.3%, and 94.9% in accuracy, sensitivity, specificity, PPV, and NPV, respectively. At the vessel level, CT-FFR achieved 91.8%, 93.9%, 90.4%, 86.1%, and 95.9%, respectively. CT-FFR exceeded CCTA in these measurements at both levels. The vessel-level AUC for CT-FFR also outperformed that for CCTA (0.957 vs. 0.599, P < 0.0001). Patients with CT-FFR ≤0.8 had higher rates of rehospitalization (hazard ratio [HR] 4.51, 95% confidence interval [CI] 1.08–18.9) and MACE (HR 7.26, 95% CI 0.88–59.8), as well as a lower rate of unstable angina (HR 0.46, 95% CI 0.07–2.91). Conclusion CT-FFR is superior to conventional CCTA in differentiating functional myocardial ischemia. In addition, it has the potential to differentiate prognoses of patients with CAD.

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Machine and Deep Learning Applied to Predict Metabolic Syndrome Without a Blood Screening

Applied Sciences ◽

10.3390/app11104334 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4334

Author(s):

Guadalupe O. Gutiérrez-Esparza ◽

Tania A. Ramírez-delReal ◽

Mireya Martínez-García ◽

Oscar Infante Infante Vázquez ◽

Maite Vallejo ◽

...

Keyword(s):

Metabolic Syndrome ◽

Deep Learning ◽

Positive Predictive Value ◽

Predictive Value ◽

Dietary Habits ◽

Smoking Habit ◽

Performance Model ◽

Whole Milk ◽

Corn Tortilla ◽

Blood Screening

The exponential increase of metabolic syndrome and its association with the risk impact of morbidity and mortality has propitiated the development of tools to diagnose this syndrome early. This work presents a model that is based on prognostic variables to classify Mexicans with metabolic syndrome without blood screening applying machine and deep learning. The data that were used in this study contain health parameters related to anthropometric measurements, dietary information, smoking habit, alcohol consumption, quality of sleep, and physical activity from 2289 participants of the Mexico City Tlalpan 2020 cohort. We use accuracy, balanced accuracy, positive predictive value, and negative predictive value criteria to evaluate the performance and validate different models. The models were separated by gender due to the shared features and different habits. Finally, the highest performance model in women found that the most relevant features were: waist circumference, age, body mass index, waist to height ratio, height, sleepy manner that is associated with snoring, dietary habits related with coffee, cola soda, whole milk, and Oaxaca cheese and diastolic and systolic blood pressure. Men’s features were similar to women’s; the variations were in dietary habits, especially in relation to coffee, cola soda, flavored sweetened water, and corn tortilla consumption. The positive predictive value obtained was 84.7% for women and 92.29% for men. With these models, we offer a tool that supports Mexicans to prevent metabolic syndrome by gender; it also lays the foundation for monitoring the patient and recommending change habits.

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De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

BioChem ◽

10.3390/biochem1010004 ◽

2021 ◽

Vol 1 (1) ◽

pp. 36-48

Author(s):

Ivan Jacobs ◽

Manolis Maragoudakis

Keyword(s):

Deep Learning ◽

Small Molecules ◽

Natural Product ◽

De Novo ◽

Viral Proteins ◽

Computer Assisted ◽

De Novo Drug Design ◽

Learning Techniques ◽

Latent Space ◽

Synthetic Molecule

Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.

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Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2018-313156 ◽

2018 ◽

Vol 103 (5) ◽

pp. 580-584 ◽

Cited By ~ 20

Author(s):

Travis K Redd ◽

John Peter Campbell ◽

James M Brown ◽

Sang Jin Kim ◽

Susan Ostmo ◽

...

Keyword(s):

Deep Learning ◽

Retinopathy Of Prematurity ◽

Predictive Value ◽

Severity Score ◽

Receiver Operating Curve ◽

Fundus Photography ◽

Image Analysis System ◽

Reference Standard ◽

Type 1 Rop

BackgroundPrior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.MethodsClinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1–9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.Results4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001).ConclusionThe i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.

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Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting

Diagnostics ◽

10.3390/diagnostics9030109 ◽

2019 ◽

Vol 9 (3) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Ukweh ◽

Ugbem ◽

Okeke ◽

Ekpo

Keyword(s):

Predictive Value ◽

Performance Metrics ◽

Area Under The Curve ◽

Youden Index ◽

Likelihood Ratios ◽

Diagnostic Efficacy ◽

Predictive Values ◽

Resource Setting ◽

Post Test ◽

Sensitivity Specificity

Background: Ultrasound is operator-dependent, and its value and efficacy in fetal morphology assessment in a low-resource setting is poorly understood. We assessed the value and efficacy of fetal morphology ultrasound assessment in a Nigerian setting. Materials and Methods: We surveyed fetal morphology ultrasound performed across five facilities and followed-up each fetus to ascertain the outcome. Fetuses were surveyed in the second trimester (18th–22nd weeks) using the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guideline. Clinical and surgical reports were used as references to assess the diagnostic efficacy of ultrasound in livebirths, and autopsy reports to confirm anomalies in terminated pregnancies, spontaneous abortions, intrauterine fetal deaths, and still births. We calculated sensitivity, specificity, positive and negative predictive values, Area under the curve (AUC), Youden index, likelihood ratios, and post-test probabilities. Results: In total, 6520 fetuses of women aged 15–46 years (mean = 31.7 years) were surveyed. The overall sensitivity, specificity, and AUC were 77.1 (95% CI: 68–84.6), 99.5 (95% CI: 99.3–99.7), and 88.3 (95% CI: 83.7–92.2), respectively. Other performance metrics were: positive predictive value, 72.4 (95% CI: 64.7–79.0), negative predictive value, 99.6 (95% CI: 99.5–99.7), and Youden index (77.1%). Abnormality prevalence was 1.67% (95% CI: 1.37–2.01), and the positive and negative likelihood ratios were 254 (95% CI: 107.7–221.4) and 0.23 (95% CI: 0.16–0.33), respectively. The post-test probability for positive test was 72% (95% CI: 65–79). Conclusion: Fetal morphology assessment is valuable in a poor economics setting, however, the variation in the diagnostic efficacy across facilities and the limitations associated with the detection of circulatory system anomalies need to be addressed.

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Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

10.1101/2020.03.12.20027185 ◽

2020 ◽

Cited By ~ 52

Author(s):

Chuansheng Zheng ◽

Xianbo Deng ◽

Qiang Fu ◽

Qiang Zhou ◽

Jiapei Feng ◽

...

Keyword(s):

Deep Learning ◽

Predictive Value ◽

Learning Algorithm ◽

Chest Ct ◽

Lung Region ◽

Deep Learning Algorithm ◽

High Negative Predictive Value ◽

Probability Threshold ◽

Weakly Supervised ◽

Learning Software

AbstractAccurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient’s CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and highperformance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

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Pansformers: Transformer-Based Self-Attention Network for Pansharpening

10.36227/techrxiv.17153228.v1 ◽

2021 ◽

Author(s):

Nithin G R ◽

Nitish Kumar M ◽

Venkateswaran Narasimhan ◽

Rajanikanth Kakani ◽

Ujjwal Gupta ◽

...

Keyword(s):

Deep Learning ◽

High Resolution ◽

Performance Metrics ◽

Satellite Image ◽

Image Data ◽

Landsat 8 ◽

Learning Models ◽

Attention Network ◽

Panchromatic Image ◽

Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

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Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Sensors ◽

10.3390/s21238009 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8009

Author(s):

Abdulmajid Murad ◽

Frank Alexander Kraemer ◽

Kerstin Bach ◽

Gavin Taylor

Keyword(s):

Deep Learning ◽

Air Quality ◽

Probabilistic Models ◽

Performance Metrics ◽

Current Data ◽

Data Driven ◽

Quality Data ◽

Empirical Performance ◽

Adversarial Training ◽

Scalable Methods

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.

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A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sensors ◽

10.3390/s21227543 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7543

Author(s):

Bogdan Ilie Sighencea ◽

Rareș Ion Stanciu ◽

Cătălin Daniel Căleanu

Keyword(s):

Deep Learning ◽

Performance Metrics ◽

Evaluation Process ◽

Surveillance Systems ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Trajectory Prediction ◽

Processing Technologies ◽

Practical Applications ◽

New Research

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

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Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4321131 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Kazi Nabiul Alam ◽

Md Shakib Khan ◽

Abdur Rab Dhruba ◽

Mohammad Monirujjaman Khan ◽

Jehad F. Al-Amri ◽

...

Keyword(s):

Deep Learning ◽

Language Processing ◽

Performance Metrics ◽

Short Term Memory ◽

Confusion Matrix ◽

Short Term ◽

Learning Techniques ◽

The World ◽

Long Short Term Memory ◽

Severe Anxiety

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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