Sentence examples for models could identify from inspiring English sources

Specifically, Buolamwini and Raji tested to see whether machine learning models could identify faces as male or female from a test set of images.

In addition, studies using animal models could identify chromatin-regulatory mechanisms with significant effects on cognition and complex behaviors, thereby pointing to the therapeutic potential of epigenetic drug targets in the nervous system.

Such frailty models could identify the risk of metastases and the need for an adjuvant systemic treatment after a local recurrence.

In vitro assays using embryonic stem/iPS cells from the Collaborative Cross/Diversity Outbred mouse models could identify genetic factors in differential sensitivity to toxicants, which can then be followed by in vivo studies to demonstrate functional relevance.

For example, the Trump digital campaign using Cambridge Analytica's model could identify which specific micro-targeted ads were trending in small geographic regions among specific personalities of Facebook users, and then direct the on-the-ground campaign to reinforce those messages in the form of a Trump stump speech.

Specifically, for patients who received both surgery and radiation, radiation without surgery, or no treatment, the comprehensive model could identify patients at higher risk as candidates for adjuvant chemotherapy, whereas it might spare low-risk patients from unnecessarily aggressive treatment.

Patients with stage I cancers who were treated with surgery without radiation were extracted for a further analysis to determine whether the comprehensive model could identify early-stage patients who may benefit from a more aggressive therapy.

The binary model could identify only low antithrombin concentrations at baseline as an independent risk factor for low aFXa levels 4 h after injection of standard certoparin dosages.

Adding postoperative symptoms such as fever and parameters of organ dysfunction to the multivariate model, could identify patients requiring additional source control measures [ 5].

Our regression model could identify metastatic tumours with a sensitivity of 84%, a specificity of 79% and an accuracy of 82%.

We next aimed to determine if the C. elegans model could identify MAH mutants and lead to the understanding of bacterial components responsible for the colonization of the intestinal epithelium within the nematode.