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Foundation for Open Access Statistics (FOAS) - since 2019

Association for Computing Machinery (ACM) - since 2013

Institute of Electrical and Electronics Engineers (IEEE) - since 2004

New approach to estimate macro and micronutrients in potato plants based on foliar spectral reflectance

SARS-CoV-2 IgG Seroprevalence among Blood Donors as a Monitor of the COVID-19 Epidemic, Brazil

Phylogeny-aware linear B-cell epitope predictor detects candidate targets for specific immune responses to Monkeypox virus

Dr. Campelo's current research focuses on the development of integrated solution frameworks for prescriptive data analysis, developing and adapting optimisation and machine learning techniques for the solution of complex problems in several areas of science and engineering, with a particular interest for bioinformatics and health informatics. 

He is also involved with the development of methodologically and statistically sound protocols for the experimental comparison of optimisation and machine learning algorithms.

He is currently a Senior Lecturer and Deputy Head of Computer Science at Aston University. He received his BSc in Electrical Engineer from Universidade Federal de Minas Gerais (UFMG, Brazil) in 2003. He was then awarded the prestigious Monbusho scholarship from the Japanese government to pursue his postgraduate studies in Hokkaido University, Japan, where he obtained first a Research MSc (Information Science and Technology, 2006) and then a PhD (Systems Science and Informatics, 2009). After returning to Brazil in 2009, he held a postdoctoral position at UFMG between 2009-10, before joining their Department of Electrical Engineering as an Assistant (later Associate) Professor in August 2010. While at UFMG he supervised over 15 postgraduate students and acted as deputy head of department between 2013 and 2017.

Optimisation

Computational Intelligence

Data Mining

Data Science

University Profile

Google Scholar Citations

aston-university-develops-software-to-untangle-genetic-factors-linked-to-shared-characteristics-among-different-species

Senior Lecturer, Computer Science

• Has potential to help geneticists investigate vital issues such as antibacterial resistance 
•	Will untangle the genetic components shared due to common ancestry from the ones shared due to evolution
•	The work is result of a four-year international collaboration.

 Aston University has worked with international partners to develop a software package to help scientists answer key questions about genetic factors associated with shared characteristics among different species.
Called CALANGO (comparative analysis with annotation-based genomic components), it has the potential to help geneticists investigate vital issues such as antibacterial resistance and improvement of agricultural crops.
This work CALANGO: a phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species has been published in the journal Patterns. It is the result of a four year collaboration between Aston University, the Federal University of Minas Gerais in Brazil and other partners in Brazil, Norway and the US.
Similarities between species may arise either from shared ancestry (homology) or from shared evolutionary pressures (convergent evolution). For example, ravens, pigeons and bats can all fly, but the first two are birds whereas bats are mammals.
This means that the biology of flight in ravens and pigeons is likely to share genetic aspects due to their common ancestry. Both species are able to fly nowadays because their last common ancestor – an ancestor bird - was also a flying organism.
In contrast, bats have the ability to fly via potentially different genes than the ones in birds, since the last common ancestor of birds and mammals was not a flying animal.
Untangling the genetic components shared due to common ancestry from the ones shared due to common evolutionary pressures requires sophisticated statistical models that take common ancestry into account.
So far, this has been an obstacle for scientists who want to understand the emergence of complex traits across different species, mainly due to the lack of proper frameworks to investigate these associations.
The new software has been designed to effectively incorporate vast amounts of genomic, evolutionary and functional annotation data to explore the genetic mechanisms which underly similar characteristics between different species sharing common ancestors.
Although the statistical models used in the tool are not new, it is the first time they have been combined to extract novel biological insights from genomic data.
The technique has the potential to be applied to many different areas of research, allowing scientists to analyse massive amounts of open-source genetic data belonging to thousands of organisms in more depth.
Dr Felipe Campelo from the Department of Computer Science in the College of Engineering and Physical Sciences at Aston University, said: “There are many exciting examples of how this tool can be applied to solve major problems facing us today. These include exploring the co-evolution of bacteria and bacteriophages and unveiling factors associated with plant size, with direct implications for both agriculture and ecology.” 
“Further potential applications include supporting the investigation of bacterial resistance to antibiotics, and of the yield of plant and animal species of economic importance.”
The corresponding author of the study, Dr Francisco Pereira Lobo from the Department of Genetics, Ecology and Evolution at the Federal University of Minas Gerais in Brazil, said: “Most genetic and phenotypic variations occur between different species, rather than within them. Our newly developed tool allows the generation of testable hypotheses about genotype-phenotype associations across multiple species that enable the prioritisation of targets for later experimental characterization.”
For more details about studying computer since at Aston University visit https://www.aston.ac.uk/eps/informatics-and-digital-engineering/computer-science 

Aston University develops software to untangle genetic factors linked to shared characteristics among different species



aston-university-ai-expertise-helps-estimate-daily-transmission-rates-of-infections-such-as-covid

Model used antibody data collected at blood donation centres  Data obtained allowed academics to estimate the proportion of people who were going undiagnosed  Current epidemiological models tend not to be as effective at estimating hidden variables such as daily infection rates.  Aston University researchers have helped develop a mathematical model which can estimate daily transmission rates of infections such as Covid by testing for antibodies in blood collected at blood donation centres. 

Current epidemiological models that are usually used tend not to be as effective at adjusting quickly to changes in infection levels. 

Working with researchers at the Universidade Federal de Minas Gerais in Brazil they conducted a large longitudinal study applying a compartmental model, which is a general modelling technique often applied to the mathematical modelling of infectious diseases, to results obtained from Brazilian blood donor centres. The testing was done by Fundacao Hemominas, one of the largest blood services in Brazil, which covers an area similar to that of continental France. 

They used the reported number of SARS-CoV-2 cases along with serology results (diagnostic methods used to identify antibodies and antigens in patients’ samples) from blood donors as inputs and delivered estimates of hidden variables, such as daily values of transmission rates and cumulative incidence rate of reported and unreported cases. 

The model discussed in the paper SARS-CoV-2 IgG Seroprevalence among Blood Donors as a Monitor of the COVID-19 Epidemic, Brazil gave the experts the ability to have a more refined view of the infection rates and relative rate of immunity compared to official measurements. 

The testing started at the beginning of the pandemic and involved 7,837 blood donors in seven cities in Minas Gerais, Brazil during March–December 2020. At that point testing wasn’t widely available and there was a high proportion of undetected asymptomatic or light symptomatic cases. The data obtained allowed the experts to estimate the proportion of people who were going undiagnosed. 

Dr Felipe Campelo, senior lecturer in computer science at Aston University, said: 

“Public communication about the COVID-19 epidemic was based on officially reported cases in the community, which strongly underestimates the actual spread of the disease in the absence of widespread testing. 

“This difference underscores the convenience of using a model-based approach such as the one we proposed, because it enables the use of measured data for estimating variables such as the total number of infected persons.

“Our model delivers daily estimates of relevant variables that usually stay hidden, including the transmission rate and the cumulative number of reported and unreported cases of infection.”

In Brazil in July 2020 there was a sharp increase in the number of people tested as new infrastructure became available, which allowed the experts to further validate their methodology by observing how officially recorded data became closer to the model predictions once testing became more widespread, including for asymptomatic or mildly symptomatic people.

They applied the model to antibodies found in blood given by donors and used it to estimate the proportion of undiagnosed cases, and to analyse changes in the infection rate, that is,  how many people each case infected on average. Previously this has been viewed as a fixed value or a fixed value over a long duration of time, but the dynamics of the spread of Covid change much faster than that. This aspect was very important in early days of the pandemic and could also be applied to similar diseases.

Looking forward, the experts aim to improve the accuracy of the model by introducing changes to account for vaccination effects, waning immunity and the potential emergence of new variants.

The paper SARS-CoV-2 IgG Seroprevalence among Blood Donors as a Monitor of the COVID-19 Epidemic, Brazil has been published in Volume 28, Number 4—April 2022 of Emerging Infectious Diseases.
 


Aston University AI expertise helps estimate daily transmission rates of infections such as Covid

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