Biological & Chemical Sciences News
Find out about the latest research and news from the Department of Biological & Chemical Sciences at NYIT.
Research Activities: Leonidas Salichos, Ph.D., Assistant Professor
Leonidas Salichos is a computational biologist. His research focuses on evolving systems, such as the spread of infectious diseases, cancer evolution and human genomics, as well as viral and microbiome prevalence across human tissues. To address these questions, he works on developing and implementing novel tools and methods based on phylogenetics, machine learning, statistics and deep learning.
Salichos has a strong background in evolutionary and computational biology. During his early studies in Agricultural Biotechnology at the Agricultural University of Athens, he developed a method based on graph theory that maps viral outbreaks using a directed minimum spanning tree. During his Master Thesis in Bioinformatics at Katholieke Universiteit Leuven, he continued working on viruses by genotyping HIV strains. He earned his PhD from Vanderbilt University in 2014. For his PhD thesis, he developed several computational tools, including machine learning metrics to measure the internode and phylogenetic tree certainty based on conflicting phylogenetic signal. As a researcher at Yale University, he worked on developing algorithms that calculate the impact of driver mutations in cancer by estimating growth patterns using variant allele frequencies. He also worked on the identification of mutational patterns and signatures, tumor subclonal architecture and expressional profiles in 2800 cancer tumors. Meanwhile he was collaborating on the analysis and characterization of HCV strains in Italy. As an Associate Research Scientist and Lab collaborator at Yale University, Salichos has been studying the epidemiology of COVID-19 in USA and the detection of infectious diseases across human tissues using next generation sequencing techniques.
Recent Projects & Research
- Modeling the spread of infectious diseases
- Quantifying music evolution in popular and non-popular culture
- Studying microbial populations across human tissues
- Determining genomic regions that impact tumor progression and immunotherapy treatments
The study of Evolving Systems in Computational Biology
The goal of my research is to develop novel tools and frameworks in order to study important questions in computational biology. From tumor progression to music evolution and from the modeling of infectious diseases to immune response, my lab mainly focuses on the study of evolving systems. Using machine learning, phylogenetics, statistical and deep learning methods our work extends on determining the spread of infectious diseases like covid-19 or HCV, identifying the impact of driver mutations during tumor progression, studying the evolution of musical chords and melodies and modeling the innate and adaptive immune responses in cancer (e.g., immunotherapy treatments) or microbial infections.
- Warrell, J*., Salichos L*. & Gerstein. Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution. bioRχiv (2020).
- Salichos, L., Meyerson, W., Warrell, J. & Gerstein, M. Estimating growth patterns and driver effects in tumor evolution from individual samples. Nature Communications 11, (2020).
- Kumar, S., Warrell, J., Li, S., McGillivray, P. D., Meyerson, W., Salichos L et al. Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences. Cell 180, 915-927.e16 (2020).
- Catia Sias, Salichos L, Daniele Lapa, Franca Del Nonno, Andrea Baiocchini, Maria Rosaria Capobianchi, Anna Rosa Garbuglia (2019) Alpha, Beta, gamma human PapillomaViruses (HPV) detection with a different set of primers in oropharyngeal swabs, anal and cervical samples. Virology Journal volume 16, Article number: 27
- Salichos L, A. Stamatakis and A. Rokas (2014) Novel information theory-based measures for quantifying incongruence among phylogenetic trees. Mol. Biol. Evol. 31(5):1261-71.
- Salichos L, Rokas A. (2013) Inferring ancient divergences requires genes with strong phylogenetic signals. Nature 497(7449):327-31.
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