ReviewComputational and structural biotechnology journal2020
Assessment of vector-host-pathogen relationships using data mining and machine learning.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Multi-omics of cockroaches infected with Salmonella Typhimurium identifies molecular signatures of vector colonization.BMC genomics · 2025Article
- Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging.Computational and structural biotechnology journal · 2024Article
- T4Seeker: a hybrid model for type IV secretion effectors identification.BMC biology · 2024Article
- Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting novel mosquito-associated viruses from metatranscriptomic dark matter.NAR genomics and bioinformatics · 2024Article
- Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.Veterinary research · 2024Article
- Genetic structure and Rickettsia infection rates in Ixodes ovatus and Haemaphysalis flava ticks across different altitudes.PloS one · 2024Article
- Using machine learning to predict protein-protein interactions between a zombie ant fungus and its carpenter ant host.Scientific reports · 2023Article
- Editorial: Advances in the diagnosis and genomic research of surveillance-response activities in emerging, re-emerging, and unidentified infectious diseases.Frontiers in public health · 2023Article
- A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases.Tropical medicine and infectious disease · 2022Review
- Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review.Sustainable cities and society · 2022Review
- Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2022Review
- Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis.Frontiers in public health · 2022Review
- Mechanisms Underlying Host Range Variation in Flavivirus: From Empirical Knowledge to Predictive Models.Journal of molecular evolution · 2021Review
- Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears.Risk management and healthcare policy · 2021Article
- Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder.Computational and structural biotechnology journal · 2021Article
- Lactitol Supplementation Modulates Intestinal Microbiome in Liver Cirrhotic Patients.Frontiers in medicine · 2021Article
- Assessment of vector-host-pathogen relationships using data mining and machine learning.Computational and structural biotechnology journal · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases, including vector-borne diseases transmitted by arthropods, are a leading cause of morbidity and mortality worldwide. In the era of big data, addressing broad-scale, fundamental questions regarding the complex dynamics of these diseases will increasingly require the integration of diverse datasets to produce new biological knowledge. This review provides a current snapshot of the systematic assessment of the relationships between microbial pathogens, arthropod vectors and mammalian hosts using data mining and machine learning. We employ PRISMA to identify 32 key papers relevant to this topic. Our analysis shows an increasing use of data mining and machine learning tasks and techniques, including prediction, classification, clustering, association rules mining, and deep learning, over the last decade. However, it also reveals a number of critical challenges in applying these to the study of vector-host-pathogen interactions at various systems biology levels. Here, relevant studies, current limitations and future directions are discussed. Furthermore, the quality of data in relevant papers was assessed using the FAIR (Findable, Accessible, Interoperable, Reusable) compliance criteria to evaluate and encourage reproducibility and shareability of research outcomes. Although shortcomings in their application remain, data mining and machine learning have significant potential to break new ground in understanding fundamental aspects of vector-host-pathogen relationships and their application in this field should be encouraged. In particular, while predictive modeling, feature engineering and supervised machine learning are already being used in the field, other data mining and machine learning methods such as deep learning and association rules analysis lag behind and should be implemented in combination with established methods to accelerate hypothesis and knowledge generation in the domain.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.