Shift tensor learning up

Author: Aaron Meyer

research/index.md

@@ -16,6 +16,15 @@ We are studying regulation of families like the Tyro3, AXL, MerTK (TAM) tyrosine
 
 Relevant publications: [Robinett et al](https://asmlab.org/publications/#FcgR), [Tan et al](https://asmlab.org/publications/#SysSerol), [VanDyke et al](https://asmlab.org/publications/#VanDyke2022)
 
+### Multidimensional Data Analysis
+
+Traditional data analysis methods organize data into matrix form—a two-dimensional (2D) grid of numbers wherein each column is a measurement and each row is an observation (e.g., genes by subjects). However, this approach overlooks how measurements are often systematically collected in biology. For example, measurements to understand the molecular response of cells to therapy might be collected over concentrations of drug, time, different sources of cells, and molecular features. In these cases, the data can be organized into a multidimensional (e.g., 4D) form. Generalization of statistical tools into these multidimensional/tensor forms exist, but their use has only begun to catch on in studies of biology and medicine because there is a lack of (1) knowledge about their benefits, (2) practical and useful implementations, and (3) algorithms for specific challenges that arise with biological data. By applying these techniques, developing new algorithms, and providing accessible implementations, we are [making these tools available in biomedical research](https://tensorly.org/stable/index.html).
+
+[Relevant publications](./tensors.html)
+
+> <img src="/public/images/tensor-summary.svg" width="600px" alt="Tensor summary" />  
+> Systems serology measurements can advance our understanding of humoral immunity. A data reduction method, “coupled matrix-tensor factorization”, effectively analyzes such data by recognizing conserved patterns and separating antigen from Fc property effects.
+
 ### Mapping Mechanisms of Resistance in Cancer
 
 Targeted therapies extend many cancer patients' lives but are limited in efficacy to a subset of patients and by the development of resistance. Efforts undertaken to identify mechanisms of resistance have uncovered numerous changes involving gene expression, post-translational regulation, and even tumor-extrinsic factors such as host-derived growth factors. Combination therapy can effectively combat resistance but requires accurate identification of the relevant resistance mechanism. Precision therapy must account for many genetic and non-genetic intrinsic and adaptive resistance mechanisms if it will accurately select these combinations.
@@ -26,12 +35,3 @@ Targeted therapies extend many cancer patients' lives but are limited in efficac
 Projects in the lab include mapping the common essential signaling events that drive resistance, quantifying single cell heterogeneity in drug response, and exploring how the extracellular matrix environment directs resistance development.
 
 Relevant publications: [Manole et al](https://asmlab.org/publications/#Manole5219), [Claas et al](https://asmlab.org/publications/#Claas2018), [Schwartz et al](https://asmlab.org/publications/#BarneyPeyton), [Creixell et al](https://asmlab.org/publications/#CreixellDDMC)
-
-### Multidimensional Data Analysis
-
-Traditional data analysis methods organize data into matrix form—a two-dimensional (2D) grid of numbers wherein each column is a measurement and each row is an observation (e.g., genes by subjects). However, this approach overlooks how measurements are often systematically collected in biology. For example, measurements to understand the molecular response of cells to therapy might be collected over concentrations of drug, time, different sources of cells, and molecular features. In these cases, the data can be organized into a multidimensional (e.g., 4D) form. Generalization of statistical tools into these multidimensional/tensor forms exist, but their use has only begun to catch on in studies of biology and medicine because there is a lack of (1) knowledge about their benefits, (2) practical and useful implementations, and (3) algorithms for specific challenges that arise with biological data. By applying these techniques, developing new algorithms, and providing accessible implementations, we are [making these tools available in biomedical research](https://tensorly.org/stable/index.html).
-
-[Relevant publications](./tensors.html)
-
-> <img src="/public/images/tensor-summary.svg" width="600px" alt="Tensor summary" />  
-> Systems serology measurements can advance our understanding of humoral immunity. A data reduction method, “coupled matrix-tensor factorization”, effectively analyzes such data by recognizing conserved patterns and separating antigen from Fc property effects.