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Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
View Article and Find Full Text PDFDevelopment of a Multiplex RT-PCR Detection for Six Viruses Infecting Strawberry.
Viruses
November 2024
Institute of Industrial Crops, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
Strawberry viruses are significant pathogenic agents in strawberry. The development and application of efficient virus detection technology can effectively reduce the economic losses incurred by virus diseases for strawberry cultivators. In order to rapidly identify strawberry virus species and prevent the spread of virus disease, a multiplex reverse transcription polymerase chain reaction system was established for the simultaneous detection and identification of strawberry mild yellow edge virus (SMYEV), strawberry vein banding virus (SVBV), strawberry mottle virus (SMoV), strawberry polerovirus 1 (SPV-1), strawberry pallidosis-associated virus (SPaV), and strawberry crinivirus 4 (SCrV-4).
View Article and Find Full Text PDFInnate Immune Response Against Batai Virus, Bunyamwera Virus, and Their Reassortants.
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November 2024
Institute for Parasitology, University of Veterinary Medicine Hannover, 30559 Hannover, Germany.
(OBVs) represent a diverse group of RNA viruses, encompassing a progressively increasing number of arboviruses that cause disease in both humans and livestock. Yet, studies investigating these viruses remain scarce despite the critical importance of such knowledge for assessing their zoonotic potential. In this study, we conducted an evaluation of the early immune response against the understudied Batai virus (BATV), as well as the influence of reassortment with the Bunyamwera virus (BUNV) on this response.
View Article and Find Full Text PDFEffect of High Temperature Exposure and Laboratory Processing Techniques on the Diagnostic Performance of Dry Swabs for the Detection of African Swine Fever Virus.
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November 2024
CSIRO, Australian Centre for Disease Preparedness (ACDP), Geelong, VIC 3220, Australia.
One of the key surveillance strategies for the early detection of an African swine fever (ASF) incursion into a country is the sampling of wild or feral pig populations. In Australia, the remote northern regions are considered a risk pathway for ASF incursion due to the combination of high numbers of feral pigs and their close proximity to countries where ASF is present. These regions primarily consist of isolated arid rangelands with high average environmental temperatures.
View Article and Find Full Text PDFChallenges of BTV-Group Specific Serology Testing: No One Test Fits All.
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November 2024
The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australian Animal Health Laboratory, Australian Centre for Disease Preparedness, 5 Portarlington Road, East Geelong, VIC 3219, Australia.
A newly formatted enzyme-linked immunosorbent assay (ELISA) for the detection of antibodies to bluetongue virus (BTV) was developed and validated for bovine and ovine sera and plasma. Validation of the new sandwich ELISA (sELISA) was achieved with 949 negative bovine and ovine sera from BTV endemic and non-endemic areas of Australia and 752 BTV positive (field and experimental) sera verified by VNT and/or PCR. The test diagnostic sensitivity (DSe) and diagnostic specificity (DSp) were 99.
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