Plasma proteome of growing tumors.

Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

Relationships between Prediction Accuracy, Metacognitive Reflection, and Performance in Introductory Genetics Students.

Cognitive scientists have previously shown that students' perceptions of their learning and performance on assessments often do not match reality. This process of self-assessing performance is a component of metacognition, which also includes the practice of thinking about one's knowledge and identifying and implementing strategies to improve understanding. We used a mixed-methods approach to investigate the relationship between students' perceptions of their performance through grade predictions, their metacognitive reflections after receiving their grades, and their actual performance during a semester-long introductory genetics course. We found that, as a group, students do not display better predictive accuracy nor more metacognitive reflections over the semester. However, those who shift from overpredicting to matching or underpredicting also show improved performance. Higher performers are overall more likely to answer reflection questions than lower-performing peers. Although high-performing students are usually more metacognitive in their reflections, an increase in a student's frequency of metacognitive responses over time does not necessarily predict a grade increase. We illustrate several example trends in student reflections and suggest possible next steps for helping students implement better metacognitive regulation.

Applying data mining techniques to library design, lead generation and lead optimization.

Many data mining techniques have been applied to activity and ADMET datasets and the resulting models are being used to understand quantitative structure-activity relationships and design new libraries. This review summarizes data mining concepts and discuss their application to library design, lead generation (particularly for sequential screening) and lead optimization (specifically for generating and interpreting QSAR models). Also, this review discusses recent comparative studies between data mining techniques and draws some conclusions about the patterns emerging in the drug discovery data mining field.