BME100 s2015:Group12 12pmL6

BME 100 Spring 2015	Home People Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3 Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6 Course Logistics For Instructors Photos Wiki Editing Help
OUR COMPANY Name: Corey Soto Name: Kyla Richardson Name: Waseem Aljaid Name: Syeda Rizvi Name: William Chmielewski LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System An assembly of 34 teams, each composing of around 5 to 6 research team members, were created. Each team was given two patients for data analysis, totaling 68 patients. The use of PCR allowed the amplification of DNA, ensuring that the research conducted was able to use the proper amounts of DNA needed. During the study, each sample collected from the patient was analyzed for SNP, or single nucleotide polymorphism. This was to determine whether each sample was suspectible to disease or if there was a lack of an affliction. The use of the Bayesian equation was critical in providing the conclusions made from the study. It was apparent that some of the teams were unable to secure the data needed for analysis. Due to the high probability of data inaccuracy, the Bayesian method provided assistance in increasing credibility and reducing ambiguity. One suggestion may be that the technical method of collecting the study's data (the fluorimeter setup, the ImageJ calibration, etc.) may have played a key role in the inconsistencies found during the research. Providing a more stable method of data analysis would be strongly recommended for future studies. What Bayes Statistics Imply about This Diagnostic Approach The first two calculations accomplished with Bayesian analysis show how reliable the methods employed during the study were for determining which patient was susceptible to disease. The Bayes values themselves were near the middle of the range which approached 1.00 (100%). Based on this discovery, the study's conclusions were hampered by data inaccuracy. There may be several reasons why this was the case. The research team believes the biggest contributing factor for such a wide range of disparity could be found in the fluorimeter setup. The smartphone & cradle used had to be positioned the exact same distance each time a sample was placed on the slide, which reduced mobility during the experiment. The camera also had to be calibrated a certain way, which may suggest some of the teams did not do that procedure. A second reason may be from the result of inferior quality pictures taken. Data analysis through using ImageJ requires that the pictures be of the best quality possible. Blurry or poorly exposed images of each sample could have given way to possible errors. Lastly, calculations through Excel may have been done incorrectly. Even though the process was relatively straightforward, human error is not dismissed as a possibility here. Computer-Aided Design TinkerCAD Our Design Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter