Abstract: Techniques are disclosed relating to weakly supervised machine learning, which may be employed when there is a limited amount of labeled data available. A computer system may generate respective sets of synthetic labels for unlabeled data for a classification problem, where a given set of synthetic labels is produced by a corresponding one of a plurality of different label models. The computer system may then fit a set of supervised models, where each supervised model is fitted with one of the respective sets of synthetic labels to produce a respective set of predictions. The computer system may then evaluate the set of supervised models based on their respective set of predictions and using a set of labeled data for the classification problem. The evaluation may be used to select a particular supervised model and its corresponding label model.

Abstract: Provided are chimeric antigen receptor (CAR) molecules comprising an extracellular domain comprising an antigen binding domain, a transmembrane domain, and an intracellular signaling domain, wherein an affinity of said binding domain to said antigen is characterized by a KD higher than 150 nM. Also provided are isolated polynucleotides and nucleic acid constructs comprising same, cells transduced with same and methods of using same.