Biomedical late-stage post-doc or early-stage junior faculty level investigators having research questions about the uses and application of multi-omic data types, which would benefit from novel data science analytics, considerations, and approaches. 

Multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome (i.e., a meta-genome and/or meta-transcriptome, depending upon how it is sequenced); in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multi-omics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association.  Adding a “spatial” component to multi-omics implies that the location of the -omic datatype in the body is a fundamental aspect to understanding its impact on health, how best to characterize and understand the influence of those -omes in the trajectory of an illness, how it might best be treated, etc.

With advances in high-throughput biology, machine learning applications to biomedical data analysis are flourishing with a particular focus on multi-omic analyses, where the integration of multi-omics data analysis and machine learning has led to the discovery of new biomarkers. For example, one of the methods of the mixOmics project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers). A unified and flexible statistical frameworks are needed to enable identifying such putative biomarkers. Exploring the relationships between -omic dimensions necessitates advanced statistical considerations, effective modeling methods, as well as unique approaches to data visualization.

In this context:

"Biomedical" expertise can involve but are not limited to:

Young investigators working at all levels of basic, experimental, and clinical genomics should consider applying.  Their work across the -omic spectrum will provide biological and clinical expertise in gene form and expression as well as metabolomics, epigenomics, etc.  The richness of these datatypes are an important element in data science applications.   Importantly, people with the following areas of expertise are of interest:

"Quantitative" expertise can involve but are not limited to:

Data and computationally-focused late-stage post-doc or early-stage junior faculty level investigators having research questions in novel quantitative methods, data analytics, statistical modeling, machine learning, and data visualization.   This is important since spatial multi-omics depends upon massive quantities of regionally-specific data from the body. Facility with advanced mathematics, programming languages, and high-performance computing are particular areas of strength we wish to engage.  These areas also include:

Applied Mathematics

Investigators with mathematical approaches applicable to issues in biomedical applications. For instance, the development and deployment of novel PDE methods, complex geometry, non-linear dynamics, as well as their applications in clinical biomedical systems for patient monitoring, the meta-analytic examination of medical records, and electronic health records.

Artificial Intelligence

Those working to develop artificial intelligent systems for clinical decision making. For instance, generative models, convolutional neural networks (CNNs), clinical support systems, agent-based applications, algorithm designs, Large-Language Models (LLMs), decision trees, emergent networks, etc. 

Biostatistics

Investigators with experience in statistical methodologies, multivariate analysis, Bayesian inference, distributional theory, calculus, and approaches to improve project direction and statistical significance. Investigators with experience in clinical trials and developing projects based at the population level are encouraged. 

Computer Science

Investigators with computational expertise and research questions that involve developing novel software tools and workflows to harness big data capabilities, especially biomedical data types.

Data Science

Investigators with the knowledge and expertise to apply appropriate machine learning models and visualization of results. 

Natural Language Processing

Those working in the process of converting written or spoken language to useful, digitized data and on tasks like information extraction and machine translation.

Pure Mathematics

Investigators with a solid foundation in advanced mathematics and interest in approaching problems from a theoretically-driven point of view seeking work with biomedical investigators in creating novel mathematical models that will be tested and further developed based. 

Quantification of Health Disparities

Investigators with the knowledge to account for disparities, provenance and metadata annotations that are crucial to link datasets that span rural/urban communities, geography, and environments.