Contact Dr Fady Mohareb
- Tel: +44 (0) 1234 750111 x2805
- Email: firstname.lastname@example.org
- Linkedin: https://www.linkedin.com/pub/fady-mohareb/39/50b/478
Areas of expertise
- Computing, Simulation & Modelling
- Digital Agriculture
- Drug Discovery & Development
- Food Quality
- Food Safety
- Food waste
- Plants and microbes
- Dr Mohareb is Senior Lecturer and Head of the Bioinformatics Group in AgriFood at. He has ~12 years of experience in the field, and his research focuses on genome and transcriptome informatics, machine learning, data science, data visualization and cloud technologies. Dr Mohareb is leading the NGS and 3GS informatics work at Cranfield University on a number of national and international consortia, such as “Oats for the future” - BB/P001432/1, “Controlling dormancy and sprouting in potato and onion” - BB/K02065X/, and “Genomics-assisted selection of Solanum chilense introgression lines for enhancing drought resistance in tomatoes” - BB/L011611/11. Dr Mohareb also has an established track record in the development and of bioinformatics software tools for managing and analysing high throughput genomes, proteomics or metabolic data; such as SorfML, PhyTB and Mrmaid. His research includes the application of machine learning and pattern recognition to reveal hidden patterns in multivariate and high throughput datasets, as well as the application of the latest Web technologies and Cloud Computing to provide means for managing and visualising large datasets. I haveextensive knowledge and experience in developing Web-based interactive frameworks for managing high-throughput experimental datasets.
1. Next-Generation Sequencing informatics: leading the NGS data analysis work at Cranfield University and have been involved in a number of publicly and commercially-funded projects in this research area. This includes “Controlling dormancy and sprouting in potato and onion” - BB/K02065X/, and “Genomics-assisted selection of Solanum chilenseintrogression lines for enhancing drought resistance in tomatoes” - BB/L011611/11. Previous experiencerelated to Camelia sinensisincludes the development of data analysis pipeline for RNA-Seq de-novoassembly, abundance estimation and functional enrichment for tea transcriptome (Directly funded by Unilever), and variant calling of parental lines from a recombinant in-bred line (RIL) population (Solanum pimpinellifoliumX S. lycopersicum var.cerasiforme).
2. Machine learning and pattern recognition: Development of novel methodologies to improve meat quality: Together with the Agricultural University of Athens, we are working on the development of classification and regression models for predicting quality and safety indices in meat products stored under different storage conditions, packaging and temperature.
3. Data Visualisation and cloud computing: I have developed, and continue to refine and maintain, several web-based software tools, such as Mrmaid (www.mrmaid.info); an online tool for mining millions of publicly available peptide spectra to help researchers design targeted proteomics assays for selected reaction monitoring experiments, and SORF (www.sorfml.com)an online research framework for food science research.
- The European Bioinformatics Institute (EBI)
- London School of Hygiene and Tropical Medicine (LSHTM)
- The Wellcome Sanger Institute.
- Rothamsted Research
Articles In Journals
- Silva Ferreira D, Kevei Z, Kurowski T, de Noronha Fonseca ME, Mohareb F, Boiteux LS & Thompson A (2018) BIFURCATE FLOWER TRUSS: a novel locus controlling inflorescence branching in tomato contains a defective MAP kinase gene, Journal of Experimental Botany, 69 (10) 2581-2593. Dataset/s: 10.17862/cranfield.rd.4721560
- Balani J, Hyer SL, Shehata H & Mohareb F (2017) Visceral fat mass as a novel risk factor for predicting gestational diabetes in obese pregnant women, Obstetric Medicine, Early online.
- Anastasiadi M, Mohareb FR, Redfern SP, Berry M, Simmonds MS & Terry LA (2017) Biochemical profile of heritage and modern apple cultivars and application of machine learning methods to predict usage, age, and harvest season, Journal of Agricultural and Food Chemistry, 65 (26) 5339-5356.
- Estelles-Lopez L, Ropodi A, Pavlidis D, Fotopoulou J, Gkousari C, Peyrodie A, Panagou E, Nychas GJ & Mohareb F (2017) An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling, Food Research International, 99 (1) 206-215.
- Diez Benavente E, Ward Z, Chan W, Mohareb FR, Sutherland CJ, Roper C, Campino S & Clark T (2017) Genomic variation in Plasmodium vivax malaria reveals regions under selective pressure, PLoS ONE, 12 (5).
- Nychas GJ, Panagou EZ & Mohareb FR (2016) Novel approaches for food safety management and communication, Current Opinion in Food Science, 12 (December) 13-20.
- Mohareb F, Papadopoulou O, Panagou E, Nychas G & Bessant C (2016) Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data, Analytical Methods, 8 (18) 3711-3721.
- Motawi TMK, Sadik NAH, Shaker OG, El Masry MR & Mohareb F (2016) Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women, Gene, 590 (2) 210-219.
- Ropodi AI, Pavlidis DE, Mohareb F, Panagou EZ & Nychas G (2015) Multispectral Image Analysis approach to detect adulteration of beef and pork in raw meats, Food Research International, 67 12-18.
- Benavente E, Coll F, Furnham N, McNerney R, Glynn J, Campino S, Pain A, Mohareb F & Clark T (2015) PhyTB: Phylogenetic tree visualisation and sample positioning for M. tuberculosis, BMC Bioinformatics, 16 (1).
- Mohareb F, Iriondo M, Doulgeraki A, Van Hoek A, Aarts H, Cauchi M & Nychas G (2015) Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling, Food Control, 57 (Nov) 152-160.
- Kevei Z, King RC, Mohareb F, Sergeant MJ, Awan SZ & Thompson AJ (2015) Resequencing at ≥ 40-fold depth of the parental genomes of a Solanum lycopersicum × S. pimpinellifolium recombinant inbred line population and characterisation of frame-shift InDels that are highly likely to perturb protein function, G3, 5 (5) 971-981.
- Papadopoulou OS, Efstathios P, Mohareb FR & Nychas GJE (2013) Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis, Food Research International, 50 (1) 241-249.
- Fan J, Mohareb F, Jones AM & Bessant C (2012) MRMaid: The SRM Assay Design Tool for Arabidopsis and Other Species, Frontiers in Plant Science, 3 Article No. 164.
- Fan J, Mohareb F, Jones AME & Bessant C (2012) MRMaid: the SRM assay design tool for Arabidopsis and other species, Frontiers in Plant Science, 3.
- Fan J, Mohareb F, Bond NJ, Lilley KS & Bessant C (2012) MRMaid 2.0: Mining PRIDE for Evidence-Based SRM Transitions, OMICS, 16 (9) 483-488.
- Panagou EZ, Mohareb FR, Argyri AA, Bessant CM & Nychas G-JE (2011) A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints, Food Microbiology, 28 (4) 782-790.
- Argyri A, Mohareb F, Panagou EZ, Bessant C & Nychas GJ (2009) An artificial neural networks approach for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectroscopy data. In: 6th Annual Conference on Predictive Modelling in Foods (ICPMF), Washington DC, 1 January 2009.
- Mohareb F, Bessant C & Nychas GJ (2009) Symbiosis: a Web-based framework for managing and analysing high throughput metabolomic data. In: 17th Annual Internationa Conference on Intelligent Systems for Molecular Biology (ISMB) & 8th European Conference on Computational Biology (ECCB), Stockholm, 1 January 2009.
- Mohareb F, Sattlecke M, Argyri A, Panagou EZ, Nychas E & Bessant C (2009) Identification of Meat Spoilage from FTIR Data Using the Symbiosis Data Analysis Framework. In: Euroanalysis 2009, Vienna, 1 January 2009.