Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases.

ABSTRACT: The coronavirus disease that outbreak in 2019 has caused various health issues. According to the WHO, the first positive case was detected in Bangladesh on 7^th March 2020, but while writing this paper in June 2021, the total confirmed, recovered, and death cases were 826922, 766266 and 13118, respectively. Due to the emergence of COVID-19 in Bangladesh, the country is facing a major public health crisis. Unfortunately, the country does not have a comprehensive health policy to address this issue. This makes it hard to predict how the pandemic will affect the population. Machine learning techniques can help us detect the disease's spread. To predict the trend, parameters, risks, and to take preventive measure in Bangladesh; this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory. Here, we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh. We extracted the data for daily confirmed, recovered, and death cases from March 2020 to August 2021. The obtained Root Mean Square Error (RMSE) values of confirmed, recovered, and death cases indicates that our result is more accurate than other contemporary techniques. This study indicates that the LSTM model could be used effectively in predicting contagious diseases. The obtained results could help in explaining the seriousness of the situation, also mayhelp the authorities to take precautionary steps to control the situation.

SUBMITTER: Absar N

PROVIDER: S-EPMC8712463 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.

Project description:COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pandemic. Various time-dependent factors including the number of daily confirmed cases, reproduction number, policy measures, mobility and flight numbers were collected. A deep-learning model using Bidirectional Long-Short Term Memory (Bi-LSTM) architecture was trained on data from 22nd Jan 2020 to 8 Jan 2021 to forecast the new daily number of COVID-19 cases 14 days in advance across 190 countries, from 9 to 31 Jan 2021. A second model with fewer variables but similar architecture was developed. Results were summarised by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and total absolute percentage error and compared against results from a classical ARIMA model. Median MAE was 157 daily cases (IQR: 26-666) under the first model, and 150 (IQR: 26-716) under the second. Countries with more accurate forecasts had more daily cases and experienced more waves of COVID-19 infections. Among countries with over 10,000 cases over the prediction period, median total absolute percentage error was 33% (IQR: 18-59%) and 34% (IQR: 16-66%) for the first and second models respectively. Both models had comparable median total absolute percentage errors but lower maximum total absolute percentage errors as compared to the classical ARIMA model. A deep-learning approach using Bi-LSTM architecture and open-source data was validated on 190 countries to forecast the daily number of cases in the early stages of the COVID-19 outbreak. Fewer variables could potentially be used without impacting prediction accuracy.

| S-EPMC10589260 | biostudies-literature

Deep learning via LSTM models for COVID-19 infection forecasting in India.

Project description:The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

| S-EPMC8797257 | biostudies-literature

A Deep Learning based Efficacy Prediction System for Drug Discovery

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-03-31 | GSE165175 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

Project description:Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.

2023-03-31 | GSE165173 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

2023-03-31 | GSE165174 | GEO

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.

Project description:COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.

| S-EPMC7437542 | biostudies-literature

A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

2023-03-31 | GSE165171 | GEO

Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.

Project description:Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.

| S-EPMC9313278 | biostudies-literature

Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting.

Project description:Accurate geographical traffic forecasting plays a critical role in urban transportation planning, traffic management, and geospatial artificial intelligence (GeoAI). Although deep learning models have made significant progress in geographical traffic forecasting, they still face challenges in effectively capturing long-term temporal dependencies and modeling heterogeneous dynamic spatial dependencies. To address these issues, we propose a novel deep transformer-based heterogeneous spatiotemporal graph learning model for geographical traffic forecasting. Our model incorporates a temporal transformer that captures long-term temporal patterns in traffic data without simple data fusion. Furthermore, we introduce adaptive normalized graph structures within different graph layers, enabling the model to capture dynamic spatial dependencies and adapt to diverse traffic scenarios, especially for the heterogeneous relationship. We conduct comprehensive experiments and visualization on four primary public datasets and demonstrate that our model achieves state-of-the-art results in comparison to existing methods.

| S-EPMC11302005 | biostudies-literature

A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning.

Project description:BackgroundThe novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the epidemic. We used a long short-term memory (LSTM) model and the improved hybrid model based on ensemble empirical mode decomposition (EEMD) to predict COVID-19 trends; Methods: The data were collected from the Harvard Dataverse. Epidemic data from 21 January 2020 to 25 April 2021 for California, the most severely affected state in the United States, were used to develop an LSTM model and an EEMD-LSTM hybrid model, which is an LSTM model combined with ensemble empirical mode decomposition. In this study, ninety percent of the data were adopted to fit the models as a training set, while the subsequent 10% were used to test the prediction effect of the models. The mean absolute percentage error, mean absolute error, and root mean square error were used to evaluate the prediction performances of the models; Results: The results indicated that the number of confirmed cases in California was increasing as of 25 April 2021, with no obvious evidence of a sharp decline. On 25 April 2021, the LSTM model predicted 3666418 confirmed cases, whereas the EEMD-LSTM predicted 3681150. The mean absolute percentage errors for the LSTM and EEMD-LSTM models were 0.0151 and 0.0051, respectively. The mean absolute and root mean square errors were 5.58 × 104 and 5.63 × 104 for the LSTM model and 1.9 × 104 and 2.43 × 104 for the EEMD-LSTM model, respectively; Conclusions: The results showed the advantage of an EEMD-LSTM model over a single LSTM model, and the established EEMD-LSTM model may be suitable for monitoring and evaluating the epidemic situation and providing quantitative analysis evidence for epidemic prevention and control.

| S-EPMC9819685 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data