Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Puzzle-based versus traditional lecture: comparing the effects of pedagogy on academic performance in an undergraduate human anatomy and physiology II lab.

BACKGROUND: A traditional lecture-based pedagogy conveys information and content while lacking sufficient development of critical thinking skills and problem solving. A puzzle-based pedagogy creates a broader contextual framework, and fosters critical thinking as well as logical reasoning skills that can then be used to improve a student's performance on content specific assessments. This paper describes a pedagogical comparison of traditional lecture-based teaching and puzzle-based teaching in a Human Anatomy and Physiology II Lab. METHODS: Using a single subject/cross-over design half of the students from seven sections of the course were taught using one type of pedagogy for the first half of the semester, and then taught with a different pedagogy for the second half of the semester. The other half of the students were taught the same material but with the order of the pedagogies reversed. Students' performance on quizzes and exams specific to the course, and in-class assignments specific to this study were assessed for: learning outcomes (the ability to form the correct conclusion or recall specific information), and authentic academic performance as described by (Am J Educ 104:280-312, 1996). RESULTS: Our findings suggest a significant improvement in students' performance on standard course specific assessments using a puzzle-based pedagogy versus a traditional lecture-based teaching style. Quiz and test scores for students improved by 2.1 and 0.4% respectively in the puzzle-based pedagogy, versus the traditional lecture-based teaching. Additionally, the assessments of authentic academic performance may only effectively measure a broader conceptual understanding in a limited set of contexts, and not in the context of a Human Anatomy and Physiology II Lab. CONCLUSION: In conclusion, a puzzle-based pedagogy, when compared to traditional lecture-based teaching, can effectively enhance the performance of students on standard course specific assessments, even when the assessments only test a limited conceptual understanding of the material.

Fold change and p-value cutoffs significantly alter microarray interpretations.

BACKGROUND: As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis. RESULTS: The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient. CONCLUSIONS: Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.