Dr Felipe Campelo

Senior Lecturer, Computer Science Aston University

  • Birmingham

Dr Campelo works in data science. He creates solution pipelines integrating data mining, optimisation and multicriteria decision support.

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3 min

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

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

Dr Felipe Campelo

3 min

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.

Dr Felipe Campelo

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Biography

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.

Areas of Expertise

Optimisation
Computational Intelligence
Data Mining
Data Science

Education

Hokkaido University

PhD

Systems Science and Informatics

2009

Hokkaido University

MS

Information Science and Technology

2006

Universidade Federal de Minas Gerais

BEng

Electrical Engineering

2003

Affiliations

  • Foundation for Open Access Statistics (FOAS) - since 2019
  • Association for Computing Machinery (ACM) - since 2013
  • Institute of Electrical and Electronics Engineers (IEEE) - since 2004

Articles

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

Computers and Electronics in Agriculture

2022

Tissue testing used to assess the chemical contents in potato plants is considered laborious, time-consuming, destructive, and expensive. Ground-based sensors have been assessed to provide efficient information on nitrogen using leaf canopy reflectance. In potatoes, however, the main organ required for tissue testing is the petiole to estimate the elements of all nutrients. This research aims to assess whether there is a correlation between the chemical contents of potato petioles and leaf spectrum, and to examine whether the spectrum of dried or fresh leaves have higher correlation values. Petiole chemical contents of all elements were tested as a reference point. Leaves were split equally into dried and fresh groups for spectral analysis (400–2500 nm). Lasso Regression models were built to estimate concentrations in comparison to actual values. The performances of the model were tested using the Ratio of (standard error of) Prediction to (standard) Deviation (RPD). All elements showed reasonable to excellent RPD values except for sodium. All elements showed higher correlation in the dried testing mode except for nitrogen and potassium. The models showed that the most significant wavebands were in the visible and very near infrared range (400–1100 nm) for all macronutrients except magnesium and sulfur, while all micronutrients had the most significant wavebands in full range (400–2500 nm) with a common significant waveband at 1932 nm. The results show high potentials of a new approach to estimate potato plant elements based on foliar spectral reflectance.

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

Emerging Infectious Diseases

2022

During epidemics, data from different sources can provide information on varying aspects of the epidemic process. Serology-based epidemiologic surveys could be used to compose a consistent epidemic scenario. We assessed the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG in serum samples collected from 7,837 blood donors in 7 cities of Brazil during March–December 2020. Based on our results, we propose a modification in a compartmental model that uses reported number of SARS-CoV-2 cases and serology results from blood donors as inputs and delivers estimates of hidden variables, such as daily values of SARS-CoV-2 transmission rates and cumulative incidence rate of reported and unreported SARS-CoV-2 cases. We concluded that the information about cumulative incidence of a disease in a city’s population can be obtained by testing serum samples collected from blood donors. Our proposed method also can be extended to surveillance of other infectious diseases.

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

bioRxiv

2022

Monkeypox is a disease caused by the Monkeypox virus (MPXV), a double-stranded DNA virus from genus Orthopoxvirus under family Poxviridae, that has recently emerged as a global health threat after decades of local outbreaks in Central and Western Africa. Effective epidemiological control against this disease requires the development of cheaper, faster diagnostic tools to monitor its spread, including antigen and serological testing. There is, however, little available information about MPXV epitopes, particularly those that would be effective in discriminating between MPXV infections and those by other virus from the same family. We used the available data from the Immune Epitope Database (IEDB) to generate and validate a predictive model optimised for detecting linear B-cell epitopes (LBCEs) from Orthopoxvirus, based on a phylogeny-aware data selection strategy. By coupling this predictive approach with conservation and similarity analyses, we identified nine specific peptides from MPXV that are likely to represent distinctive LBCEs for the diagnostic of Monkeypox infections, including the independent detection of a known epitope experimentally characterised as a potential specific diagnostic target for MPXV. The results obtained indicate ability of the proposed pipeline to uncover promising targets for the development of cheaper, more specific diagnostic tests for this emerging viral disease. A full reproducibility package (including code, data, and outputs) is available at https://doi.org/10.5281/zenodo.7057489.

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