New machine learning algorithm improves estimation and integration of single-cell data

Like a game of Wheel of Fortune, where you have to fill in missing letters to guess the hidden phrase, analyzing data sometimes requires estimating missing data points by relying on available information in order to get the full picture of what’s being studied.

Stem cell project to create new model to study brain development and Down syndrome

Waisman Center researchers are creating a new approach to study how changes to brain development in the womb result in intellectual disability in people with Down syndrome.

Daifeng Wang, PhD – Slide of the Week

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements.

Daifeng Wang, PhD – Slide of the Week

The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks.