Biomarker Discovery Essay

Decision support systems are ideally interactive systems that allow the decision making physician to come to the conclusion based on a host of information pulled from data bases, personal knowledge, predesigned modals etc. Decision support systems have many benefits such as; patient-time efficiency, speed up process of decision making, promotes learning and training, reveals new approaches in thought process, generates new evidence in support of a decision and encourages exploration and discovery of the decision maker (Bosworth, York, Kotansky, & Berman, 2011). Although these systems require end user expertise, correct inputs and appropriate modals, they also require vast and exstenive information. Immunoinformatics are used to compile vast amounts of data for the immunology field (De Groot A. , Immunomics: discovering new targets for vaccines and therapeutics , 2006). This data includes genetic mapping, protein structures, cytometry data and many other data pools needed by immunologists to make correct decisions. Immunoinformatics face to challenge of compiling this enormous amounts of data in an organised and correct way. This data needs to be mapped into correct diagnostic modals for the physician to use in their decision support system (Barh, Misra, & Kumar, 2010). This data leads to new ways of hypothesis testing for immune responses

Doctors long have been able to crudely predict a person’s future illness. By studying disease patterns, for example, they can say that heavy cigarette smokers have 10 times the risk of developing lung cancer as nonsmokers and that middle-aged men with high blood cholesterol levels have higher-than-normal risk of heart attacks. Geneticists also look at family medical pedigrees to determine the chances of children inheriting any of the 3,000 known genetic disorders.

Through multiple experiments, promoter elements were examined to see how they control gene expression. The purpose was to learn how a gene is regulated in bacteria, how timing affects activity, how positive and negative regulation control gene expression, how E. coli mutants respond in plate assays, and how concentrations of sucrose affects gene expression. ß-galactosidase enzyme, also known as Beta-gal or ß-gal, was the assay in the experiments. ß-galactosidase is an enzyme that breaks up lactose into galactose and glucose. Furthermore, it is a hydrolase enzyme that catalyzes the hydrolysis of ß-galactosides into monosaccharides.

In addition to this, there are biomarkers that are used for serious diseases such as cardiovascular diseases, renal failure and liver dysfunction.

In this report, I will be describing a hypothetical independent bioinformatics group project with Dr. Ian Watson (McGill University). I have discussed with him previously about potential post-graduate projects I could take on. Dr. Watson’s research focuses on melanoma genomics which include sequencing the exomes and genomes of patient samples (of varying severity) and biomarker responses to immunotherapy (Watson, 2016). I will choose to focus on a project that will work on previously sequenced patient samples for brevity and practicality. Specifically, I will choose to analyze the pharmacological responses from the supplied tumour data via analyzing their genetic profiles. Melanoma-targeted therapies have shown promise with many drugs currently undergoing (Food and Drug Administration) FDA trial (Cancer Research Institute, 2016). Dr. Watson has access to several clinical trial phase biopsies whose genetic profiles can be analyzed. Ideally, I will aim to differentiate and distinguish groups of poor- and high-responders by their genetic signatures. For instance, immunocytokines are more effective in patients with killer immunoglobulin-like receptor (KIR)/KIR-ligand mismatch genotypes (Delgado et al., 2010). Another example is vemurafenib which inhibits mutant BRAF (a common mutation in melanoma patients which activates the RAS pathway, see Figure 1). Indeed, vemurafenib has a higher survival rate than traditional chemotherapy in patients with a

With the positive coefficients, we will see an increase in one unit of each variable separately compared with the advancement in diabetes. With a 0.05 parameter, the linear regression model selects 5 predictor variables with significance, age, tc, ldl, tch, and glu. To validate the assumption, we can plot the residuals versus the fitted values to see if there are any indications of signs of random distributions. For the residual plot, we see there are no indications or violations of random distribution and can calculate the MSE of the model, which is 3111.265. Next, we will leverage the best subset method to select the predictor variables that are truly impactful to the model.

Hands-on experience in the identification and validation of disease-associated mutations and single nucleotide polymorphisms (SNPs) using genomics and cytogenetics data

One 's genetic information, to include blood samples, family medical history, physicals, and personal medical records, will provide scientists what is needed to find if someone has increased chances of developing cancer. This information is usable throughout all stages of life (from before

Our approach to classification of user’s cancer risks will use a larger number of dimensions to calculate risk of different types of cancers than other similar analytic techniques. The data used for this project will come from the Surveillance, Epidemiology, and End Results (SEER) data from the National Cancer Institute. SEER's extensive datasets allow many different analyses to be done from general population cancer statistics (Siegel) to specific medical decision making applications such as whether a certain treatment for prostate cancer would be beneficial (Culp). Other approaches have utilized SEER datasets and supervised classification methods to develop survival prediction models for colon cancer (Al-Bahrani) and chart survival curves for different treatments for lung cancer (Owonikoko). However, these previous approaches have focused on one type of cancer and are therefore less comprehensive than the tool we are seeking to develop. We also plan to implement an interactive, user-friendly visualization tool that allows for quick interpretation of the results. The user would receive a list of cancer they are most

To solve Q1 and Q3 in Figure 1, we evaluated the performance of each tool in a variety of genes associated with lysosomal diseases. To evaluate the false negative rate of each tool, we submitted all 385 known disease-associated missense mutations of IDUA, IDS and GLB1 genes into these tools. Significant concordance was observed between the functional consequences of missense mutations predicted by various combinations of the tools. Out of 385 known disease-causing mutations, 155 (40.3%) were predicted to be ‘damaging’ by all 7 tools and 197 (51.2%) were predicted to be ‘damaging’ by at least 6 tools. As shown in Figure 2, PROVEAN and PolyPhen turned out to be the most

Stakeholders will include clinical informatics and bioinformatics experts, providers (both specialists such as genetic counselors and geneticists, and generalists), pharmacists, information security and privacy personnel, and laboratorians.

In Chapter 2, four machine learning classifiers are used to find the likelihood of having BRCA mutation based on detailed personal and family history of cancer information. The data used for validation of the models emerges from a recent nation-wide survey study (ABOUT) of those who requested BRCA genetic testing through one of the commercial health insurance companies in the United States. This is the first study evaluating existing well-known BRCA risk estimation models using data on general population in the United States. The models considered were gradient boosting model (GBM), random forest, support vector machines, and

A biomarker work plan should be prepared before the beginning of assay to describe the study purpose and requirements. This plan can help the timely identification of reagents, controls, and experimental samples. In early-phase clinical tests, preclinical studies and literature reviews can provide background information for a specified population, that allow the setting up of appropriate precision requirements for the assay. This plan can also specify the level of rigor to be applied to the assay validation and sum up purposes of study and the intended use of assay data. (Lee, J. W., et al. (2006). "Fit-for-purpose method development and validation for successful biomarker measurement." Pharm

Our study undelines the importance of a standardized protocol, based on some specific procedure of sample collection and storage, in order to have a successful biomarkers analysis. Moreover, it has been highlighted how MALTI TOF MS analysis of specific proteins could suggest the selection of the best pool of samples for a powerful biomarker study.

Complex diseases are caused by multiple genetic and environmental factors working in combination with each other and thus, it is difficult to characterise the contribution of any one factor to the disease1. However, the widespread adoption of genome-wide association studies (GWAS) has greatly accelerated the rate at which these factors are discovered and characterised. These studies genotype individuals with different phenotypes (for example, those who are affected or unaffected by a complex disease) at hundreds of thousands of single nucleotide polymorphisms (SNPs). If a SNP is statistically more common in one phenotypic group then it is said to be associated with that phenotype2.