BIOSTATISTICS AND BIOINFORMATICS

Dennis Wang helms A*STAR IHDP’s Bioinformatics platform, which integrates expertise in computer science with biology and mathematics to support analytics with large and/or multi-dimensional data. Additionally, he holds the Academy of Medical Sciences Professorship (Chair in Data Science) at Imperial College London.

Wang’s research focuses on translating patterns in the human genome into actionable information that accelerates the development of treatments for complex diseases. Specialising in big data analysis, drug development (small molecules and biologics), algorithmic development, software design, web service development, genomic profiling, and statistical inference, his experience includes applying machine learning and statistical approaches to identify patterns from large genomic data sets and providing clinically actionable biomarkers to drug development teams.

Having worked in both academia and industry, Wang enjoys mentoring junior bioinformaticians and clinicians wanting to apply genomics to patient care and marrying research and teaching to promote data driven approaches for personalising medicines. Among his most significant projects are a crowd-sourcing machine learning challenge to predict drug combinations involving partners from pharma and technology industries; improving whole genome sequencing methods for the diagnosis of complex diseases, such as dementia, motor neurone disease and COVID-19; and developing computational methods for integrating omics data from patients to advance precision medicine for treating lung cancer and pulmonary hypertension.

He has received numerous awards, including the EPSRC Healthcare Technologies grant in 2021, the Rosetrees Seedcorn Award and Academy of Medical Sciences Springboard Award in 2019, the MRC Proximity to Discovery Award in 2017, and the AstraZeneca Innovative Medicines Award in 2016.

Wang obtained his Bachelor of Science in Computer Science, Microbiology and Immunology from The University of British Columbia, and both his Master of Philosophy in Computational Biology and PhD in Biostatistics from the University of Cambridge.

Find out more about him here.


PUBLICATIONS

• Genomic loci susceptible to systematic sequencing bias in clinical whole genomes.
  Genome Res. 2020 Mar;30(3):415-426. doi: 10.1101/gr.255349.119. Epub 2020 Mar 10.

• A statistical framework for assessing pharmacological response and biomarkers using uncertainty estimates.
  Elife. 2020 Dec 4;9:e60352. doi: 10.7554/eLife.60352.

• Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.
  Nat Commun. 2019 Jun 17;10(1):2674. doi: 10.1038/s41467-019-09799-2.

Maria De Iorio is a joint principal investigator and senior biostatistics consultant at A*STAR IHDP, a professor of science at Yale-NUS College, and a professor of biostatistics in the Department of Statistical Science at the University College London. Her previous appointments were as a postdoctoral research assistant at the University of Oxford and lecturer/senior lecturer at the Imperial College London.

De Iorio’s research interests include Bayesian statistics and how to apply Bayesian methods to analyse data, Bayesian nonparametrics, biostatistics and computational methods, medical statistics, genomics, and metabolomics. She boasts an impressive track record in modelling complex biomedical data and analysing high throughput data in genomics and metabolomics.

She obtained her undergraduate degree in economics from Bocconi University, Italy, and her MSc and PhD in statistics from Duke University in the U.S.

Additionally, De Iorio and her team at A*STAR IHDP provide customised statistical solutions for research projects, from planning and data collection to analysis and presentation. Find out more here.

PUBLICATIONS

• Monte Carlo co-ordinate ascent variational inference. 
  Stat Comput 30, 887–905 (2020). https://doi.org/10.1007/s11222-020-09924-yA

• Simulation Approach for Change-Points on Phylogenetic Trees.
  J Comput Biol. 2015 Jan 1; 22(1): 10–24. doi: 10.1089/cmb.2014.0218.

• Bayesian inference for duplication-mutation with complementarity network models.
  J Comput Biol. 2015 Nov;22(11):1025-33. doi: 10.1089/cmb.2015.0072. Epub 2015 Sep 10.

Chen Li's research interests lie in genetics, DNA methylation analysis, lipidomics, metabolomics, gene expression analysis, single nucleotide polymorphism (SNP), copy number variation analysis, next-generation sequencing (NGS) data analysis and clinical data analysis. In particular, she focuses on the multi-omics studies of gestational diabetes-related research, homocysteine pathway, and telomere length.

Her scientific work includes the pre-processing of genetics, DNA methylation andlipidomics data in the GUSTO and S-PRESTO birth cohort studies – making them ready for multi-omics analysis; and genome-wide association studies of numerous plasma micronutrients such as vitamins B12 and D and homocysteine, and clinical biomarkers like insulin-like growth factors (IGF), insulin-like growth factor-binding proteins (IGFBP), adiponectin, leptin and C-reactive protein (CRP).

Chen obtained her bachelor and master's degrees in engineering from Tianjin University, and her PhD from the National University of Singapore.

Find out more about her here.


PUBLICATIONS

• Analysis of two birth tissues provides new insights into the epigenetic landscape of neonates born preterm.
  Clin Epigenetics. 2019 Feb 11;11(1):26. doi: 10.1186/s13148-018-0599-4.

• The early care environment and DNA methylome variation in childhood.
  Dev Psychopathol. 2018 Aug;30(3):891-903. doi: 10.1017/S0954579418000627.

• DNA methylome variation in a perinatal nurse-visitation program that reduces child maltreatment: a 27-year follow-up.
  Transl Psychiatry 8, 15 (2018). doi: 10.1038/s41398-017-0063-9.

Andrea Cremaschi’s field of expertise is in statistical modelling for biomedical applications, developing methodologies in the field of Bayesian Statistics (often including nonparametric aspects), graphical models, and analysis of datasets involving variables of diverse nature.

His research interests include both the methodological and applied aspects of statistics – particularly within the Bayesian framework, Bayesian nonparametrics, the use of flexible mixture models for inference in scenarios where the data present peculiar features, concern model-based cluster analysis, and the use of graphical models for inference in dataset characterised by a sparse dependency. Besides analysing data from GUSTO in the neuroscience and metabolic fields, he also works on manuscripts with collaborators to develop novel methodologies in the field of Bayesian nonparametric statistics with applications. He received an honourable mention for the Lindley Prize by the International Society for Bayesian Analysis (ISBA) in 2019.

Cremaschi obtained his Bachelor and Master’s degrees in Mathematical Engineering from the Polytechnic University of Milan, and his PhD in Statistics from the University of Kent in the U.K. He did his postdoctoral fellowship in at the Yale-NUS College.


PUBLICATIONS

• An in vitro assay for biomarker discovery and dose prediction applied to ibrutinib plus venetoclax treatment of CLL.
  Leukemia. 2020 Feb;34(2):478-487. doi: 10.1038/s41375-019-0569-7. Epub 2019 Aug 30.

• Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.
  Bayesian Anal. 2019 Dec;14(4):1271-1301. doi: 10.1214/19-ba1153. Epub 2019 Mar 28.

• Single cell profiling of phospho-protein levels in chronic lymphocytic leukemia.
  Oncotarget. 2018 Jan 4;9(10):9273-9284. doi: 10.18632/oncotarget.23949. eCollection 2018 Feb 6.

Varsha Gupta is a researcher in the Bioinformatics and Biostatistics group, with joint appointments at A*STAR IHDP and the A*STAR Bioinformatics Institute (A*STAR BII). 

Her research has focused on understanding longitudinal clinical health trajectories, growth patterns in children, and population lifestyle patterns derived from smart wearables, with an emphasis on their epidemiological associations and causal relationships.  Her work has led to several publications in high-impact international journals, project fundings, research collaborations and granted patents.

Gupta is dedicated to generating data-driven insights from clinical data and integrating these findings with artificial intelligence systems to advance healthcare delivery. She is passionate about developing healthcare applications that provide comprehensive daily life health journeys and optimise population well-being.

She collaborates with multidisciplinary teams of clinicians, biologists, physicists, mathematicians, and engineers, and is deeply committed to continuous learning and mentoring, fostering knowledge sharing within her research community.

Gupta obtained her Bachelor of Science, Master of Science and PhD in Physics from the University of Delhi, India.

Find out more about her here.


PUBLICATIONS

• Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes.
  Int J Obes (Lond). 2025 Feb;49(2):340-347. doi: 10.1038/s41366-024-01679-0.

• Unsupervised machine learning investigates trajectory patterns of COVID-19 symptoms and physical activity measured via the Myheartcounts app and smart devices.
  NPJ Digit Med. 2023 Dec 22;6(1):239. doi: 10.1038/s41746-023-00974-w.

• Longitudinal characterization of etiological factors associated with obesogenic growth patterns in early childhood.
  Int J Epidemiol. 2023 Apr 19;52(2):426-439. doi: 10.1093/ije/dyac177.

Huang Jian is a Senior Scientist under the Biostatistics and Bioinformatics platform at A*STAR IHDP. He is also an Adjunct Senior Scientist at the A*STAR Bioinformatics Institute (BII) and an Adjunct Assistant Professor at the NUS Yong Loo Lin School of Medicine.

His experiences encompass a broad spectrum of epidemiological research, ranging from early development to ageing. He navigates diverse omics data and employs advanced statistical methods in research across multiple birth and population-based cohorts. Building upon these multifaceted experiences, his current research focuses on uncovering the causal relationships between environmental and lifestyle factors and human health throughout the lifespan, as well as how molecular signatures in early development influence long-term health trajectories. He is leading early-life proteomics research in an Asian birth cohort, integrating genomics and proteomics to understand the biological mechanisms underlying child health and development, with a focus on shared pathways of cardiometabolic, neurodevelopmental, and psychological health. He also leads a project within Singapore’s national population cohort, leveraging electronic health records and causal inference methods to advance drug repurposing and pharmacovigilance. Ultimately, his research integrates multi-omics, longitudinal data, and causal inference to enable early intervention and inform public health policy.

Huang was awarded the Singapore National Medical Research Council, Open Fund - Young Individual Research Grant (NMRC OF-YIRG) in 2022 and the Human Health and Potential - Prenatal / Early Childhood Grant in 2024 to investigate the relationship between parental and early-life metabolic risk and child and adolescent development.

He obtained a Bachelor of Science in Pharmacy from Jinan University, and his Master of Public Health and PhD in Epidemiology and Biostatistics from The University of Hong Kong. He completed a postdoctoral fellowship at Imperial College London, investigating the causal relationships between lifestyle factors and molecular biomarkers and the risk of Alzheimer's disease.

Find out more about him here. 


PUBLICATIONS

• Linking obesity-associated genotype to child language development: the role of early-life neurology-related proteomics and brain myelination.
  EBioMedicine. 2025 Mar:113:105579. doi: 10.1016/j.ebiom.2025.105579. Epub 2025 Feb 11.

• Parental and child genetic burden of glycaemic dysregulation and early-life cognitive development: an Asian and European prospective cohort study.
  Transl Psychiatry. 2024 Jan 4;14(1):2. doi: 10.1038/s41398-023-02694-x.

• Inflammatory Diseases, Inflammatory Biomarkers, and Alzheimer Disease: An Observational Analysis and Mendelian Randomization.
  Neurology. 2023 Feb 7;100(6):e568-e581. doi: 10.1212/WNL.0000000000201489. Epub 2022 Nov 16.

PUBLICATIONS

• Genome-Wide Pleiotropy Analysis Identifies Shared and Opposing Pathways Influencing Coronary Artery Disease and Cancer.
  Arteriosclerosis, Thrombosis, and Vascular Biology. 2025 Oct;45(10):1945-56. doi: 10.1161/ATVBAHA.125.322433.
  

• Proteogenomic and observational evidence implicate ANGPTL4 as a potential therapeutic target for colorectal cancer prevention.
  JNCI: Journal of the National Cancer Institute. 2025 Jun 13:djaf137. doi: 10.1093/jnci/djaf137.
  

• Multi-output prediction of dose–response curves enables drug repositioning and biomarker discovery.
  npj Precision Oncology. 2024 Sep 20;8(1):209. doi: 10.1038/s41698-024-00691-x.

Christine Maria Lim is a Scientist with the Bioinformatics platform at A*STAR IHDP.

Her research interests lie in network approaches to systems biology, target identification, and drug response and resistance,

Lim obtained her Bachelor of Engineering (Bioengineering) from the Nanyang Technological University, Singapore, and her PhD in Chemistry from the University of Cambridge.

PUBLICATIONS

• Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation.
  Proc. Natl Acad. Sci. September 29, 2023. 120 (40) e2300215120. https://doi.org/10.1073/pnas.2300215120.

• AlphaFold2-based prediction of the co-condensation propensity of proteins.
  Proc. Natl Acad. Sci. August 12, 2024. 121 (34) e2315005121. https://doi.org/10.1073/pnas.2315005121.

Pan Hong's research involves identifying molecular signatures related to health and development for women and children. In addition to studying high-throughput genome wide measures such as genetics, epigenetics, transcriptomics, lipidomics and metabolomics for Singaporean cohort studies, she also investigates the correlations and interactions among multiple omics for in-depth understanding and novel discovery through association studies and quantitative trait loci (QTL) analyses.

Possessing a background in computer science, Pan has a keen interest in cellular gene regulation from both computational methods and single cell technologies, and optimised computing and machine learning algorithms. She developed a R software package for efficiently computing the relations between gene, environment and methylation (GEM tool suites). Her other notable achievements include developing an integrative multi-omics database (iMOMdb) for Asian pregnant women and lipid quantitative trait loci (LipidQTL) of the longitudinal lipidomics for Asian mothers and children.

Prior to joining A*STAR IHDP, Pan was with A*STAR Genome Institute of Singapore and A*STAR Institute for Infocomm Research where she received honours, awards, patents and publications for informatics and computational biology. She also boasts industry experience in Eli Lilly ‘s now defunct Singapore Centre for Drug Discovery where she was involved in award-winning projects for disease ontology and precision medicine.

Pan obtained her Bachelor of Engineering and Master of Signal processing from Anhui University in China, and her Master of Computer Vision and PhD in Computer Science from the Nanyang Technological University, Singapore.

Find out more about her here.


PUBLICATIONS  

• Long-chain polyunsaturated fatty acids, gestation duration, and birth size: a Mendelian randomization study using fatty acid desaturase variants.
  Am J Clin Nutr. 2018 Jul 1;108(1):92-100. doi: 10.1093/ajcn/nqy079.

• Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment.
  BMC Bioinformatics. 2016 Aug 2;17:299. doi: 10.1186/s12859-016-1161-z.

• Comparison of Methyl-capture Sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples.
  Epigenetics. 2016;11(1):36-48. doi: 10.1080/15592294.2015.1132136. Epub 2016 Jan 19.

Willem van den Boom is a Scientist with the Biostatistics platform at A*STAR IHDP. He previously did postdoctoral fellowships with the NUS Yong Loo Lin School of Medicine, the Division of Science at the Yale-NUS College, and the Department of Statistics and Data Science at the National University of Singapore (NUS). 

Van den Boom's research interests are in Bayesian statistics, more specifically applications, scalable computation, and Gaussian graphical models. He aims to impact health by working on data from A*STAR IHDP and medical records in close collaboration with clinicians. His most impactful work to date has been analysis of medical records – first in collaboration with clinicians from Duke University, and more recently with the National University Hospital. At Duke, he focused on blood glucose control of patients undergoing elective surgery. While at NUH, the team he worked with cautioned against the overly liberal administration of oxygen in the ICU. Both projects have been included in treatment guidelines around the world.

• The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis.
  Biometrics. 2024 Jul 1;80(3):ujae075. doi: 10.1093/biomtc/ujae075.

• The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models.
  J. Comput. Graph. Stat. 2022. 31 (4): 1215-1224. https://doi.org/10.1080/10618600.2022.2050250.

• Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation.
  Biometrika. 2021 Jun;108(2):269-282. doi: 10.1093/biomet/asaa068. Epub 2020 Aug 26.