Categorical misalignment: Making autism(s) in big data biobanking.

The opaque relationship between biology and behavior is an intractable problem for psychiatry, and it increasingly challenges longstanding diagnostic categorizations. While various big data sciences have been repeatedly deployed as potential solutions, they have so far complicated more than they have managed to disentangle. Attending to , this article proposes one reason why this is the case: Datasets have to instantiate clinical categories in order to make biological sense of them, and they do so in different ways.

A machine learning strategy for the identification of key descriptors and prediction models for IgG monoclonal antibody developability properties.

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages.

Design of a mucin-selective protease for targeted degradation of cancer-associated mucins.

Targeted protein degradation is an emerging strategy for the elimination of classically undruggable proteins. Here, to expand the landscape of targetable substrates, we designed degraders that achieve substrate selectivity via recognition of a discrete peptide and glycan motif and achieve cell-type selectivity via antigen-driven cell-surface binding. We applied this approach to mucins, O-glycosylated proteins that drive cancer progression through biophysical and immunological mechanisms.