Network-based methods with heterogeneous data to identify severe COVID immune-related genes

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

Several global health incidents and evidences show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio-temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (Formula presented.) (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio-temporal datasets and reduces the delay in predicting health status compared to the existing studies. © 2022 John Wiley & Sons Ltd.

Data Management in EpiGraph COVID-19 Epidemic Simulator

The transmission of COVID-19 through a population depends on many factors which model, incorporate, and integrate many heterogeneous data sources. The work we describe in this paper focuses on the data management aspect of EpiGraph, a scalable agent-based virus-propagation simulator. We describe the data acquisition and pre-processing tasks that are necessary to map the data to the different models implemented in EpiGraph in a way that is efficient and comprehensible. We also report on post-processing, analysis, and visualization of the outputs, tasks that are fundamental to make the simulation results useful for the final users. Our simulator captures complex interactions between social processes, virus characteristics, travel patterns, climate, vaccination, and non-pharmaceutical interventions. We end by demonstrating the entire pipeline with one evaluation for Spain for the third COVID wave starting on December 27th of 2020. © 2022, Springer Nature Switzerland AG.

PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series

Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model;(2) parallelised neural networks to enable flexibility and avoid information over-whelming;and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.

Heterogeneous DBSCAN for Emergency Call Management: A Case Study of COVID-19 Calls Based on Hospitals Distribution in Saudi Arabia

Unsupervised machine learning and especially unshaped clustering approaches like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) remain at the center of researchers’ attention. Many DBSCAN improvements have been recently proposed, but none of them involve the data heterogeneity that we may find in real-life problems. To tackle this issue, in this paper, a novel DBSCAN-based approach is proposed and applied to real data of Saudi Arabia (KSA), in order to cluster the COVID-19 calls based on hospitals distribution for the resolution of the ambulances dispatching problem and COVID-19 emergency calls covering. The designed approach is called Heterogeneous DBSCAN (HDBSCAN). It considers different data types: Statically distributed hospitals over Saudi Arabia and dynamic COVID-19 incoming calls. The experiments show that the proposed HDBSCAN greatly speeds up the original DBSCAN and provides an efficient classification for the application problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Learning from heterogeneous data via contrastive learning: An application in multi-source covid-19 radiography

The ongoing COVID-19 pandemic has overloaded current healthcare systems, including radiology systems and departments. Machine learning-based medical imaging diagnostic approaches play an important role in tracking the spread of this virus, identifying high-risk patients, and controlling infections in real-time. Researchers aggregate radiographic samples from different data sources to establish a multi-source learning scheme to mitigate the insufficiency of COVID-19 samples from individual hospitals, especially in the early stage of the disease. However, data heterogeneity across different clinical centers with various imaging conditions is considered a significant limitation in model performance. This paper proposes a contrastive learning scheme for the automatic diagnosis of COVID-19 to effectively mitigate data heterogeneity in multi-source data and learn a robust and generalizable model. Inspired by advances in domain adaptation, we employ contrastive training objectives to promote intra-class cohesion across different data sources and inter-class separation of infected and non-infected cases. Extensive experiments on two public COVID-19 CT datasets demonstrate the effectiveness of the proposed method for tackling data heterogeneity problems with boosted diagnosis performance. Moreover, benefiting from the contrastive learning framework, our method can be generalized to solve data heterogeneity problems under a broader multi-source learning setting. © 2021 IEEE

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm.

Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular KNN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel KNN variant (KNNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The KNNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The KNNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three KNN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.