Sandeep Singhal

AI and Multi-Omics Integration for Cancer Diagnosis

Cancer remains a major clinical challenge due to high recurrence, molecular heterogeneity, and difficulty in early detection. My research develops an integrated AI and multi-omics framework for cancer risk prediction, combining spatial, immunological, and genomic profiling to improve detection and treatment stratification.

Key objectives include: 1) Integrating high-resolution imaging, AI-driven spatial analysis, and multi-omics data to quantify tumor-infiltrating lymphocytes, immune cell distribution, stromal interactions, and tissue architecture, generating robust patient-specific predictors; 2) Combining spatial and immune biomarkers with multi-omics profiles for comprehensive tumor classification; and 3) Applying an ecosystem-based approach that incorporates environmental exposures and tumor-intrinsic features to enhance risk assessment and prediction.

We investigated Kaiso (ZBTB33), a transcriptional regulator linking autophagy proteins (LC3A/B) to the tumor immune microenvironment in a racially diverse cohort, revealing novel mechanisms influencing tumor progression and survival [3]. Elevated gp78 E3-ligase levels were also found to predict poor survival, impacting pathways in cell cycle, metabolism, and stress response [4].

Collectively, this work demonstrates that AI-driven multi-omics integration, spatial and immune profiling, and environmental contextualization can improve cancer diagnosis, prognostication, and treatment personalization.

Key publications include:

1. Peer-reviewed article Corresponding-author Sandeep Singhal A novel spatial framework for gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology (Nature – Scientific Report)
2. First and Co-Corresponding-author Sandeep Singhal Topology-Based Biomarkers Accurately Predict Breast Cancer Outcome and Survival in Racially Diverse Patient Cohorts. (Science Advance)
3. Peer-reviewed article first-authored by Dr. Singhal, “Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival.” Nature - Communications biology 4 (1), 1-132021. 2021PMC7851134
4. Peer-reviewed article first-authored by Dr. Singhal. Protein Expression of the gp78 E3-ligase Predicts Poor Breast Cancer Outcome Based on Race. (JCI Insight, 2022, PMID: 35639484)

 Biomarkers Docovery

Breast cancer is a highly heterogeneous disease, and although molecular classification into four primary subtypes has improved treatment strategies, clinical outcome disparities persist within each subtype. Notably, Triple Negative Breast Cancer (TNBC) occurs nearly twice as frequently in women of African ancestry in the United States, highlighting the need for novel biomarkers to guide precision therapy.

Immune components, particularly CD4+ T cells, play a critical role in modulating the tumor microenvironment (TME), influencing both adaptive and innate immunity. This has driven the development of immune-based biomarkers for prognostication and therapy response prediction [1].

In one of my key contributions, we developed an advanced predictive model to identify novel biomarkers for chemotherapy response across breast cancer subtypes. This model links gene expression modules to underlying biological processes, revealing subtype-specific molecular pathways associated with pathological complete response (pCR). These insights provide a framework for early-stage prediction of treatment efficacy [2].

We also addressed challenges in younger breast cancer patients, whose tumors exhibit unique biological complexities independent of subtype. Our work identified proliferation-related prognostic gene signatures and suggested targeting mammary stem cell function and RANKL-signaling pathways through specific inhibitors. This approach informs personalized treatment strategies that minimize long-term toxicity risks from adjuvant chemotherapy in younger women [3].

Furthermore, we conducted quantitative protein profiling of tumors from a racially diverse cohort. Using digital immunohistochemistry (IHC) analysis, we uncovered gene regulators and regulatory networks that differentially predict survival based on race. African American women face higher mortality from breast cancer compared to European American women, even within clinically similar tumors. By examining luminal differentiation pathways, we identified race-specific differences in clinical outcomes, enabling the development of population-specific predictive biomarkers for more precise prognostication [4].

Collectively, these studies advance our understanding of breast cancer biology, treatment response, and population-specific disparities, providing tools for precision medicine and biomarker-driven therapy selection across diverse patient populations.

Key publications include:

1. Peer-reviewed article co-authored by Dr. Singhal, “CD4+ follicular helper T cell infiltration predicts breast cancer survival,” The Journal of Clinical Investigation, 2013 & a testimonial letter confirming Dr. Singhal’s substantial contribution PMC3696556
2. Peer-reviewed article co-first-authored by Dr. Singhal, “Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis,” Journal of Clinical Oncology, 2012 J Clin Oncol (JCO) 30:1996-2004, 2012, PMID22508827
3. Peer-reviewed article co-authored by Dr. Singhal, “Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling.” Clinical Cancer Research, 2012, PMID22261811
4. Peer-reviewed article co-first-authored by Dr. Singhal, “Racial Differences in the Association Between Luminal Master Regulator Gene Expression Levels and Breast Cancer Survival,” Clinical Cancer Research 26 (8), 1905-1914, PMC8051554

 Prediction of Toxicity After Radiation Therapy in Prostate Cancer Patients

Radiotherapy is a cornerstone of cancer treatment, used curatively or palliatively in approximately 50% of cancer patients. However, radiation can damage surrounding healthy tissues, causing a spectrum of adverse effects-from mild symptoms that impair quality of life to severe, potentially life-threatening complications. Early prediction of normal tissue toxicity enables personalized treatment strategies, minimizing long-term adverse outcomes [1].

One rare but significant complication in prostate cancer patients treated with brachytherapy (BXT) is urethral stricture (US). We developed a predictive model for US, incorporating clinical and dosimetric parameters specific to BXT. This model identified factor combinations with high predictive potential for radiation-induced toxicity, offering a tool for personalized risk stratification [2].

Another common complication is radiation-induced proctitis, leading to rectal bleeding. We developed a validated risk prediction model integrating clinical factors, radiation dose, and high-throughput genomic data from GWAS studies. To support this work, we established a specialized GWAS pipeline, pooling datasets from multiple international cohorts in the United States, United Kingdom, and Spain. Data were partitioned into training and testing sets to rigorously evaluate model performance and reproducibility.

In a large collaborative study, we further refined candidate genetic variants, significantly improving the proportion of familial prostate cancer risk explained by these susceptibility regions. These findings underscore the value of fine-mapping approaches in enhancing clinical risk profiling and improving the translational relevance of genomic discoveries for prostate cancer patients undergoing radiotherapy [3–4].

Collectively, this research demonstrates that integrating clinical, dosimetric, and genomic data can generate accurate, individualized toxicity risk predictions, informing treatment planning and patient counseling while mitigating adverse effects associated with prostate cancer radiotherapy.

Key publications include:

1. Peer-reviewed article co-authored by Dr. Singhal, “The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism-Based Models: A Step Toward Prevention,” Seminars in Radiation Oncology, 2015
2. Peer-reviewed article first-authored by Dr. Singhal, “Clinical factors and dosimetry associated with development of prostate brachytherapy-related urethral strictures: A matched case-control study,” Brachytherapy, 2017
3. Peer-reviewed article co-authored by Dr. Singhal, “Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci,” Nature Genetics, 2018
4. Peer-reviewed article co-authored by Dr. Singhal, “Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants,” Nature Communications, 2018

 Establishing a Genomic-Epigenomic and Radiation-Age Association for Space and Air Exploration

The biological effects of ionizing radiation (IR) remain a critical area of research, influenced by variables such as dose, dose-rate, radiation type, age of individuals, tissue type, underlying health conditions, nutrition, lifestyle factors, and the calibration of radiation measurements. While the study of radiation’s impact on workers handling nuclear materials continues, there has been a renewed focus on its applications in space exploration. Ionizing radiation has been implicated in the acceleration of aging, contributing to diseases commonly associated with aging, such as cardiovascular diseases, cancers, autoimmune disorders, cognitive impairments, cataracts, and shortened lifespan[1,2].

Our research focuses on leveraging large, publicly available datasets from studies that have exposed blood samples to gamma radiation. We hypothesize that combining genomic and epigenomic data will provide a foundation for understanding the association between ionizing radiation exposure, aging processes, and related diseases[]3,4. By exploring this intersection, we aim to uncover insights that will help mitigate the long-term effects of radiation exposure in both terrestrial and space exploration contexts.

Key publications include:

1. Peer-reviewed article corresponding authored by Dr. Singhal, “Translating genetic findings to epigenetics: identifying the mechanisms associated with aging after high-radiation exposure on earth and in space” Frontiers in Public Health, Feb 2024 PMC38584916
2. Peer-reviewed article correspondingauthored by Dr. Singhal, “A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging” Biology2024, 13(2) PMC10886797
3. Peer-reviewed article correspondingauthored by Dr. Singhal, “Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation” Frontiers in Public Health, May 2023 PMC10213902
4. Peer-reviewed article first-authored by Dr. Singhal, “Towards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression data,” Oncotarget, Oncotarget 7:3002-17, 2016 PMC4823086

 Tumor Ecosystem Cloud-Based Multi-Omics Pipelines for Teaching and Research Support

Beyond research, I developed platforms serve as educational tools, providing students, postdoctoral fellows, and junior researchers with hands-on experience in multi-omics integration, AI-based biomarker discovery, and translational cancer analysis. By linking innovative computational research with structured mentorship, this work cultivates the skills and expertise needed for the next generation of precision oncology scientists.

on the development of cloud-based multi-omics pipelines to study the tumor ecosystem while simultaneously providing computational frameworks for training and research support. These pipelines integrate transcriptomics, epigenetics, spatial profiling, AI-driven analysis, and environmental risk modeling to generate mechanistic insights into tumor biology, support patient outcome prediction, and enable scalable analysis of large, multi-institutional datasets.

1. Peer-reviewed article Corresponding-author by Sandeep Singhal Transcriptomics and epigenetic data integration learning module on Google Cloud. Journal: Briefings In Bioinformatics, Oxford University Press (BIB-23-2346, DOI: 10.1093/bib/bbae352)
2. Peer-reviewed article Corresponding-author by Sandeep Singhal Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context 2025 arXiv preprint arXiv:2506.12683
3. Peer-reviewed article Corresponding-author by Sandeep Singhal Identification and validation of arsenic-associated genes and risk model for predicting lung cancer. Journal of Clinical Oncology 43 (16_suppl), e13631-e13631

Complete List of Published Work in MyBibliography: https://www.ncbi.nlm.nih.gov/myncbi/1N_5isx6NcRAN/bibliography/public/

1. Corresponding-author Sandeep Singhal A novel spatial framework for gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology (2025 Oct 30;15(1):37925., PMID: 41168438)
2. First and Co-Corresponding-author Sandeep Singhal Topology-Based Biomarkers Accurately Predict Breast Cancer Outcome and Survival in Racially Diverse Patient Cohorts. (Cancer Research, 2026)
3. Corresponding-author Sandeep Singhal Transcriptomics and epigenetic data integration learning module on Google Cloud. Journal: Briefings In Bioinformatics, Oxford University Press (BIB-23-2346, DOI: 10.1093/bib/bbae352)
4. Corresponding-author Sandeep Singhal Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context arXiv preprint arXiv:2506.12683, 2025
5. Corresponding-author Sandeep Singhal RESILIENT CD133+CD24+ KIDNEY PROGENITOR CELLS DRIVE HYPOXIC INJURY RECOVERY VIA HIF1A, EGFR, AND EDN1 EXPRESSION ( J. Mol. Sci. 2025, 26(6), 2472;)
6. Corresponding-author Sandeep Singhal. Translating Genetic Findings to Epigenetics: Identifying the Mechanisms Associated to Aging After High-Radiation Exposure on Earth and Space. (Front. Public Health Sec. Radiation and Health, Volume 12 – 2024, doi: 10.3389/fpubh.2024.1333222)
7. Corresponding-author Sandeep Singhal. A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging. Biology (Basel) 2024 Feb 4;13(2):98. PMID: 38392316
8. Contributing-author Sandeep Singhal Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants. (Nature genetics 2023 Nov 09) (PMID: 37945903)
9. Corresponding-author Sandeep Singhal. Establishing a Genomic Association Between Chronological Aging and Ionizing Radiation Exposure for Human PMBC Studies Supplements Lung Disease Differentiation (Frontiers in Public Health, sec. Radiation and Health, May 2023) (PMC10213902)
10. Contributing-author Sandeep Singhal Immune Profile of Exosomes in African American Breast Cancer Patients Is Mediated by Kaiso/THBS1/CD47 Signaling (Cancers, April 2023) (PMCID: PMC10136634)
11. Corresponding-author Sandeep Singhal..Arsenite Exposure to Human RPCs (HRTPT) Produces a Reversible Epithelial Mesenchyma Transition (EMT): In-vitro and In-silico study (PMID: 36982180 , International Journal of Molecular Sciences, March 2023)
12. First-author Sandeep Singhal.. Schlafen 12 slows TNBC tumor growth, induces luminal markers and predicts favorable survival (Cancers Jan. 2023, 15, 402) PMCID: PMC9856841
13. Contributing-author Sandeep Singhal. al.. RNA Sequencing of Intestinal Enterocytes Pre- and Post-Roux-en-Y Gastric Bypass Reveals Alteration in Gene Expression Related to Enterocyte Differentiation, Restitution, and Obesity with Regulation by Schlafen 12 (Cells September 2022, PMC9601224)
14. First-author Sandeep Singhal. Protein Levels of the gp78 Ligase Predict Poor Breast Cancer Outcome Based on Race (The Journal of Clinical Investigation (JCI) Insight, May 2022, PMC9310521)
15. Corresponding-author Sandeep Singhal et al Gene expression and functional analysis of Asian population exposed to a range of Arsenic levels and its association with cancer cell lines and bladder tumors (Oxidative Medicine and Cellular Longevity, 2022, Volume 2022 |Article ID 3459855 PMCID: PMC8760535
16. Corresponding-author Sandeep Singhal. et al Role of HRTPT in kidney proximal epithelial cell regeneration: Integrative differential expression and pathway analysis using microarray and scRNA-seq (Journal of Cellular and Molecular Medicine, 09 October 2021, PMCID: PMC8581341)
17. First-author SK Singhal, et al Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival, Nature Communications biology. 4 (1), 1-13, 2021 org/10.1038/s42003-021-01651-y
18. Contributing-author Sandeep Singhal..et al Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction, Nature genetics 53 (1), 65-75, PMCID: PMC8148035
19. Contributing-author Sandeep Singhal,.. et al Elevated glucose represses lysosomal and mTOR-related genes in renal epithelial cells composed of progenitor CD133+ cells (PloSone March 25, 2021, PMCID: PMC7993790)
20. Contributing-author LS Bisogno,..,Sandeep Singhal et al. Ancestry-dependent gene expression correlates with reprogramming to pluripotency and multiple dynamic biological processes. Science advances 6 (47), eabc3851, 2020, PMCID: PMC7679169
21. Contributing-author S Al-Marsoummi,.. SK Singhal, ..et al Schlafen 12 Is Prognostically Favorable and Reduces C-Myc and Proliferation in Lung Adenocarcinoma but Not in Lung Squamous Cell Carcinoma. Cancers 12 (10), 2738, 2020, PMCID: PMC7650563
22. Contributing-author K Blommel,..SK Singhal, et al. Meta-analysis of gene expression profiling reveals novel basal gene signatures in MCF-10A cells transformed with cadmium Oncotarget 11 (39), 3601, 2020, PMCID: PMC7533076
23. First-author Jung S, Singhal S .et al Racial Differences in the Association between Luminal Master Regulator Expression and Breast Cancer Survival (Clinical Cancer Research, 2019), CCR-19-0875 April 2020, PMCID: PMC8051554
24. Contributing-author Singhal S….et al Radiogenomics Consortium Genome Wide Association Study Meta-analysis of Late Toxicity after Prostate Cancer Radiotherapy. (JNCI February 2020, Pages 179–190, PMCID: PMC7019089
25. Contributing-author Singhal S. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nature Genetics 2018/6/11, PMCID: PMC6568012
26. Contributing-author Singhal S. Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. Nature Communications, 9 (2018), p. 2256, 10.1038/s41467-018-04109 PMCID: PMC5995836
27. Contributing-author Singhal S HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2017 Nov 11. doi: 10.1093/nar/gkx1089 PMCID: PMC5753273
28. First-author Sandeep K Singhal et al. Clinical factors and dosimetry associated with development of prostate brachytherapy related urethral strictures: A matched case control study. Brachytherapy journal, 31 May 2017 https://doi.org/10.1016/j.brachy.2017.04.242
29. First-author Sandeep K. Singhal, et al, Towards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression data. Oncotarget. 2016 Jan 19;7(3):3002-17. doi: 10.18632/oncotarget.6503.)
30. Contributing-author Sandeep Singhal. Does Location of Prostate Cancer By Sextant Biopsies Predict for Relapse After Prostate Brachytherapy With I-125 Seeds? Brachytherapy 14, June 30, 2015 S95-S96.
31. Contributing-author SK Singhal, The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism (SNP)-Based Models: A Step Towards Prevention. Seminars in Radiation Oncology October 2015 Volume 25, Issue 4, Pages 281–291
32. Contributing-author Sandeep K. Singhal CXCL13-producing follicular helper CD4+ T cells infiltrating tumors signal an organized immune response and predict survival in breast cancer. Running Title: Tfh cells and organized immunity in breast cancer. J Clin Invest. June 17, 2013, 7(123), 2873-2892, doi:10.1172/JCI67428.
33. Co-First-author Singhal S# Association between SPARC mRNA Expression, Prognosis and Response to Neoadjuvant Chemotherapy in Early Breast Cancer: A Pooled in-silico Analysis. PLoSONE 8(12): (2013). e62451, doi:10.1371/annotation/3d5a5933-791f-4191-98f5-f559a872e404
34. Co-First-author Sandeep K. Singhal#, Gene modules and response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Journal of Clinical Oncology JCO. (2012) 2011.39. 5624, doi:10.1200/JCO.2011.39.5624. April 16, 2012
35. Contributing-author Singhal, S. K., Urokinase-type plasminogen activator gene (PLAU) to predict clinical outcome in invasive lobular carcinoma. Journal of Clinical Oncology (Vol. 30, No. 15), 2012, May.
36. Contributing-author Sandeep K Singhal, PIK3CA Genotype and a PIK3CA Mutation-Related Gene Signature and Response to Everolimus and Letrozole in Estrogen Receptor Positive Breast Cancer. PloS one 8 (1), e53292, January 02, 2013, DOI: 10.1371/journal.pone.0053292
37. Contributing-author, SK Singhal, Genomic grade adds prognostic value in invasive lobular carcinoma. Ann Oncol. 2013 Feb;24(2):377-84. doi: 10.1093/annonc/mds280. Epub 2012 Oct
38. Contributing-author Sandeep K. Singhal, Characterization and clinical evaluation of CD10+ stroma cells in the breast cancer microenvironment. Clinical Cancer Res. 2012 18(4) 1004-1014, Feb 15, 2012; doi: 10.1158/1078-0432.CCR-11-0383 PMID: 2223510
39. Contributing-author Sandeep Singhal Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling. Clin Cancer Res. 2012 18(5): 1341-51 January 18, 2012; doi: 10.1158/1078-0432.CCR-11-2599 PMID: 22261811
40. Contributing-author Sandeep K. Singhal,. DNA methylation profiling reveals a predominant immune component in breast cancers. EMBO Mol Med. 2011 Dec, 3(12), 726-41. doi: 10.1002/emmm.201100801 PMID: 21910250
41. Co First-author Sandeep K. Singhal Low residual proliferation after short-term letrozole therapy is an early predictive marker of response in high proliferative ER-positive breast cancer. Endocr Relat Cancer. 2011 Nov 14;18(6):721-30. doi: 10.1530/ERC-11-0180. PMID: 21984694,
42. Contributing-author Singhal SK, HER2-positive circulating tumor cells in breast cancer. PLoS One. 2011 Jan 10;6 (1):e15624. PMID: 21264346

HIGH IMPACT CONFERENCE PAPERS_____________________________________

Following is the list of abstracts published in Journal of Clinical Oncology (Impact factor 42.3):

1. A set of molecular biomarkers as an indicator of cancer risk. 2024 Journal of Clinical Oncology 42 (16_suppl), e12563-e12563, 2024/6/1
2. LC3AB protein-based gene (PbG) signatures as an indicator of relapse even after achieving pathological complete response. Journal of Clinical Oncology 42 (16_suppl), e12552-e12552, 2024/6/1
3. Gene modules in association with Kaiso and LC3 regulatory pathways to predict survival and response to therapy (2022 ASCO Annual Meeting, e13573, 2022/6/1).
4. Meta-analysis of arsenic exposed genes expression profiles to develop a bladder cancer predictor (2021 ASCO Annual Meeting, e16523 2021/5/20).
5. A gp78/AMFR protein-driven gene signature that predicts breast cancer outcome. (2021 ASCO Annual Meeting, 2021/5/20, 553)
6. Subcellular partitioning of Kaiso (ZBTB33) as a biomarker to predict overall breast cancer survival (2020 ASCO Annual Meeting, 3534 2020/5/20).

CONFERENCE TALKS_____________________________________

1. Invited speaker “17th American Association for Cancer Research Conference on The Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved at LA” (AACR October 2024)
2. Invited speaker “15th American Association for Cancer Research Conference on The Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved at Philadelphia” (AACR September 2022)
3. Biomedical - 4th ND Symposium (September 2021), Risk, Racial disparity and Outcomes: Patients with Breast Cancer.
4. A gp78/AMFR protein-driven gene signature that predicts breast cancer outcome (ASCO (May-June 2021)).
5. 2021 IDeA CENTRAL REGION: Racial disparity and molecular biology of breast cancer: A multi-omics approach
6. 2021 DaCCoTA Annual Symposium Evaluation Invited talk on multi-omics data and artificial intelligence (June 2021)
7. Subcellular partitioning of Kaiso (ZBTB33) as a biomarker to predict overall breast cancer survival. ASCO (May-June 2020)
8. Invited talk: 3rd Novel Methodologies in Health Disparities Symposium at UPR Medical Sciences Campus (Jan 2020).
9. Oral presentation “Integrative modeling of omics data to identify role of Multi-omics Biomarkers to predict disease, treatment-response and toxicity in cancer patients” @ Third Annual Joint NDSU UND Biomedical Engineering Symposium’ at Fargo September 30. 2019
10. Molecular analysis to deliver personalized health insights and custom actions plans for better treatment. 2018 Alberta Epigenetics Network Summit. March 25 - March 27, 2018
11. Integrative modeling of omics data to identify role of Genomics Biomarkers to predict treatment response and toxicity in cancer patients. at AEN-PIMS Bioinformatics & Computational Biology Workshop, Calgary, AB, Canada Nov. 24 2017
12. Validated risk predictive model for radiation proctitis after prostate radiotherapy using clinical, dosimetric and genetic variables. RADIOGENOMICS CONSORTIUM MEETING, Vall D’Hebron Institute of Oncology, Barcelona, Spain 19th June 2017
13. “Combining genome-wide association studies (GWAS) to predict the likelihood of radiation toxicity of prostate cancer patient after treatment” APCaRI Fall Symposium 2015 “Knowledge, Action, Impact”. Oct. 23-24, 2015 Kananaskis, Alberta
14. “Exploratory model building for radiation proctitis” 15th June 2015. RADIOGENOMICS CONSORTIUM MEETING, Montpellier, France.
15. “A validated model to predict the likelihood of post therapy radiation toxicity of a prostate cancer patient considering genomic, clinical and dosimetric variables” (November 8-9, 2014). The Alberta Prostate Cancer Research Initiative (APCaRI), Canmore, Alberta, Canada
16. “Biomarkers as predictors of response to cancer treatment.” Invited talk. August 6, 2013, The Genome Institute of Singapore (GIS), Singapore.
17. “Statistical issues in the analysis of genome-wide methylation arrays as compared to gene expression data, with breast cancer as an example” (link: http://www.bio-itworldexpo.com/Cancer-Informatics). April 24-26, 2012, Bio-IT World Conference & Expo, Boston, MA, USA
18. “Statistical issues in the analysis of genome-wide methylation arrays as compared to gene expression data, with breast cancer as an example” presented at “Statistical and dynamical models in biology and medicine workshop” October 27-28, 2011, University Göttingen, Germany. (http://www.ams.med.uni-goettingen.de/modelling_workshop_2011/)

Google Scholar Link: http://scholar.google.ca/citations?user=NY5nrrYAAAAJ&hl=en

• Postdoctoral Fellow, Cross Cancer Institute, University of Alberta, Edmonton, Canada
• Postdoctoral Fellow, Department of Biomedical Informatics, University of Pittsburgh,
• Doctorate (Bioinformatics), Université libre de Bruxelles, Belgium

• Adjunct Faculty, Pathology and Cell Biology, Columbia University Medical Centre, NY
• Editor | Scientific Reports
• Assistant Professor, Department of Biomedical Engineering, College of Engineering & Mines, University of North Dakota, Grand Forks, ND, USA
• Director, North Dakota INBRE, Bioinformatics division, ND, USA
• Research Scientist, Columbia University Medical Center, New York, NY, USA
• Editorial board member of Journal of Current Trends in Translational and Research, IL, USA.
• Scientific Advisor, BioMark Diagnostics Inc. Vancouver, British Columbia
• Bioinformatician researcher, Jules Bordet Institute, ULB Bruxelles, Belgium