Bioinformatics Seminar

Computational cell biology is evolving from descriptive atlases to predictive, actionable models — from mapping what cells are to simulating what they do.

Complex diseases often arise from diverse genetic mechanisms acting through interconnected pathways and frequently encompass multiple hidden subtypes that share similar diagnostic features but have distinct genetic origins.

Deep learning is fueling a revolution in genomics, enabling the development of a new generation of analysis tools that offer unprecedented accuracy.

Imaging has been the primary means of diagnosing as well as tracking the progression of many diseases for decades but has largely been collected in isolation.

Biological replicators are locked in deeply intertwined genetic conflicts with each other.

Recent years have seen dramatic advances in both experimental determination and computational prediction of macromolecular structures.

In the last decade there has been a data revolution in biology with the advent of high-throughput high dimensional data modalities such as single-cell RNA-sequencing, fMRI data, molecular structure data and other modalities.

How can multimodality improve representations of proteins? Foundation models have shown promise in building powerful representations for many domains.

As we get a more complete picture of how proteins accomplish myriad complex tasks in cells, from catalysis to mechanical force generation, we are more inspired to engineer similarly “smart” molecules for specific needs.

Non-coding RNAs (ncRNAs) play central roles in maintaining, regulating and defending the genomes of all organisms. In fact, their regulatory versatility and ubiquity have made ncRNAs increasingly important tools for engineering synthetic biological systems.