Education

  • Ph.D., Computational & Functional Genomics, University of Quebec, Canada Summer 2014

  • M.Sc., Genetics & Molecular Biology, University of Henri Poincaré, Nancy, France Winter 2009

  • B.Sc., Behavioural Genetics & Parasitology, University of Lebanon, Beirut, Lebanon 2005 - 2006

  • Premedical studies, University of Lebanon, Beirut, Lebanon 2004

Professional Employment

Peer reviewed Publications

  • Bassim, S., Allam, B. SNP hot-spots in the clam parasite QPX. BMC Genomics, 19:486, 2018. doi:10.1186/s12864-018-4866-8

  • Bassim, S., Chapman, W. R., Tanguy, A., Moraga, D., Tremblay, R. Predicting growth and mortality of bivalve larvae using gene expression and supervised machine learning. Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics. 16, 2015, 59-72. doi:10.1016/j.cbd.2015.07.004

  • Bassim, S., Tanguy, A., Génard, B., Moraga, D., Tremblay, R. Identification of Mytilus edulis genetic regulators during early development. Gene, 555(1), 65–78, 2014. doi:10.1016/j.gene.2014.08.042

  • Bassim, S., Genard, B., Gauthier-Clerc, S., Moraga, D., Tremblay, R. Ontogeny of bivalve immunity: assessing the potential of next-generation sequencing techniques. Reviews in Aquaculture, 6, 1–21, 2014. doi:10.1111/raq.12064

Under review

  • Bassim, S., Allam, B. Network analysis of dual RNA-seq reveals conserved virulence mechanisms of the protistan parasite QPX. BMC Genomics.

  • Bassim, S., Allam, B. Host-parasite interactions in non-model species: From RNA-seq assembly to network inference using machine learning and high-performance computing BioRXiv.

  • Bassim, S., Kridel, R. Transcriptional polygenic score for diffuse large B cell lymphoma identify individuals with risk of central nervous system relapse BioRXiv.

  • Bassim, S., Allam, B. Espinosa, E. Cross-tissue network analysis of Crassostrea virginica neuropeptide gene expression and their association to gill functions. eLife.

Invited Talks

  • Bassim, S. Invited talk: Detection of high-risk lymphoma patients using machine learning at the Vector institute for artificial intelligence, Toronto. July 2018.

  • Bassim, S. Scott, D., Kridel R., Poster: Determinants of central nervous system relapse in diffuse large B-cell lymphoma, Accelerating precision medicine, UHN, Toronto, Canada. January 2018.

  • Bassim, S., Allam, B. A transcriptomic approach to characterize host-parasite interactions, National Shellfisheries Association (NSA), Invited speaker in Comparative Genomics, Tennessee, March 2017.

  • Bassim, S. Introduction to computational genomics Invited lecturer, Stony Brook University. New York, June 2015.

  • Bassim, S. Genomic and physiological processes of the blue mussel Mytilus edulis (L., 1758) during early development and the impact of eicosanoid precursors. Invited speaker & Poster: ECOBim, International workshop on environmental assessment. Montreal, Canada. May 20–24, 2013.

  • Bassim, S. Transcriptomic analyses and physiological effectors of Mytilus edulis during larval ontogeny. Invited speaker & Poster: ECOBim, International workshop on environmental assessment. Reims, France, May 10–15, 2012.

  • Bassim, S., Murad, H., Devignes, MD., Ndiaye, B., Becuwe, P., Domenjoud, L. Discovery of new PPAR direct target genes in the glioblastoma cell line T98G. Poster: Communication: Conference of the Grand-East region in oncology research. Faculty of sciences, Nancy 1, France. 2009c. Poster abstract.

  • Bassim, S., Devignes, MD., Ndiaye, B., Becuwe, P., Domenjoud, L. Successful in silico application of immunoprecipitated chromatine (ChIP) for the analysis of the promoter region of PPAR target genes. Master Thesis, University of Lorraine Nancy 1, Faculty of Sciences, France, 2008.

  • Bassim, S., Guédon, G., Study of the accretion and mobilization of integrative and conjugative elements (ICEs) and their genomic variants in Streptococcus thermophilus. Graduate Thesis, University of Lorraine Nancy 1, Faculty of Sciences, France, 2007.

Projects & Experience

Machine learning & risk prediction in Lymphoma (1037 hours) Winter 2018 Developed machine and deep learning pipeline to classify patients with Lymphoma into low or high risk. Classifier back-end on expression data, genetic networks, regularization, and mutation data from exome sequencing. Github link

Host and parasite cross-talk and infection mechanisms (846 hours) Spring 2017 Developed pipeline that separates and de novo assembles dual RNA-seq pathogen and host transcriptomes, classify and cluster functions. Inferred cross-species interaction networks. Github link

Genetic interactions that link neuropeptides to feeding behavior (1853 hours) Summer 2016 Developed pipeline for de novo assembly and regulatory networks from RNA-seq data. Output: gene-gene interactions, pathway clustering, annotation, and machine learning comparison of cell lines with neural networks. Github link

Shotgun metagenomic analyses (56 hours) Spring 2016 Constructed a classification pipeline to phylogenetically classify DNA shotgun bacteria. Characterized pathogenic interactions with host from metagenomic sequenced samples of oyster mucus. Github link

Nature and origin of parasite in RNA-seq host/pathogen samples (226 hours) Winter 2016 Constructed pipeline for de novo assembly and parasite classification from dual RNA-seq infected host tissues for comparative genomics analysis.

Viral genome assemblies in high throughput sequencing samples (100 hours) Winter 2016 Developed batch pipelines that assemble DNA shotgun virus genomes for pathogen classification (virus, bacteria, archae, plasmids) and their affecting abundance on host protein synthesis.

Structured database for genetic interrogation and discovery (20 hours) Summer 2015 Constructed a MySQL database for genetic variants, RNA-seq gene expression, annotations from 20 public libraries, gene-gene interactions, ontologies, and metabolic pathways.

New genes for parasite virulence and aggressiveness (625 hours) Summer 2015 Developed technique to call genetic mutations in different parasitic strains without annotated genome. Identified genomic hotspots from RNA-seq data. Github link

Supervised prediction of Parkinson’s disease (307 hours) Summer 2014-Fall 2015 Designed new back-end pipeline built on a machine learning model with elastic net and SVM to classify and correlate mutations with common psychiatric disorders within different world populations. Github link

Machine learning predicting cellular growth and mortality (605 hours) 2012-14 Developed new machine learning ensemble model with lasso, ANN, boosted-GLM, and bagging for classification of cellular mechanisms. Isolated genes responsible in growth and mortality from RNA-seq and microarray timeseries data. Github link

New transcription factors that regulate growth and metamorphosis (1020 hours) 2010-11 Developed RNA-seq and microarray batch & biostatistics pipelines. First time implementation on marine invertebrate larvae of Bayesian model to infer gene-gene interactions. Identified networked genes that trigger metamorphosis, lower immune defense, and increase metabolism. Github link

Role of PPARs in glioblastoma and breast cancer Fall 2009 Designed a gene-selection predictive model with a mining kernel of protein affinity databases. Validated PPAR-regulated genes by protein-DNA (ChIP) essays and their function in cell migration function \& potential targeting in cancer therapy.

Designing analyses that estimate mutation affinity Fall 2008 Developed a pattern recognition technique in collaboration with Laboratoire Lorrain de Recherche en Informatique et Ses Applications, (PI: Dr. Marie-Dominique Devignes). Isolated 7 cancer-causing genes (PPARs) & validated their high-affinity binding sites in the human genome.

Lowering rheumatoid arthritis severity in mouse Spring 2008 Constructed bacterial vectors with microRNA. Carried targeted gene silencing of specific interleukins in E. coli and murine macrophages.

In vitro bacteria transformation Spring 2007 Constructed vectors and transformed bacteria with antibiotic resistance genes. Validated in vitro. Optimized the yield of bacteria conjugation for the yogurt and cheese industry.

Identify genes for transfer of antibiotic resistance Fall 2007 Constructed bacterial vectors. Isolated, extracted, and targeted genes that transpose during bacterial conjugation (pre-CRISPR).

Meetings & Workshops

  • Deep & Reinforcement Learning summer school hosted by the Canadian Institute For Advanced Research (CIFAR) & Vector. Special invitation. Participation by Google Brain, DeepMind, Microsoft research, Alberta Machine Intelligence Institute & Institut québécois d’intelligence artificielle (MILA). Supported by Drs. Yoshua Bengio & Richard Sutton, Toronto, (10 days) July 2018

  • Meeting for machine learning & genomics, CIFAR & GETx Brave new world: Genetic networks to decode complex disease, Toronto, June 2018

  • Meeting for transcriptomics and cancer research, Terry Fox Research Institute symposium. Deconstructing tumor complexity, Toronto, December 2017

  • Xsede workshop on Hadoop and Spark with Bridges, March 2016

  • Grant winning grant proposals, October 2015 (Dr. John D. Robertson)

  • Workshop on Machine Learning, Stanford University, USA, February – April, 2014 (Drs. Trevor Hastie & Robert Tibshirani).

  • Workshop on Intellectual property and Patents, July 2013 (Dr. Louise G. Bernier).

  • ECOBim, Ecotoxicology in marine ecosystems, Montreal, Quebec, Canada, May 2013.

  • Workshop on Bayesian Networks, June, Paris, France, 2012 (Dr. Daniel Gianola).

  • ECOBim, Ecotoxicology in marine ecosystems, Reims, France, May 2012.

  • ECOBim, Ecotoxicology in marine ecosystems, Saguenay, Quebec, Canada, May 2010.