Biological Networks And The Biomedical And Genomic Fields

The same translational barriers that apply to clinical research more generally apply to personalized medicine in particular. Discoveries in the lab still take years to be widely adopted in practice. One stepping stone to truly personalized medicine that arguably has been achieved, and continues to evolve, is genomic medicine, also called stratified medicine (Trusheim et al 2007; Ginsburg and Willard 2009). Stratified medicine involves clinical are that is based on specific qualities of an individual, often derived from molecular fingerprints. Subjects may be divided into different classes based on their genetics or some other high dimensional omicscale biomarker. Fig 25.3. This classification can then be used to guide clinical decision making.

Recent advancements in data collection is producing big complex data in many areas which can be modeled as graphs - entities (vertices/nodes) and their relationships (edges). They are increasingly being encountered in various fields of study. With this burst in data creation and collection, effective analysis of medium to large graphs is becoming increasingly popular in many fields such as social network analysis, biology, education, etc. With this increasing demand in large graph analytics, visual analysis of graphs has gained a lot of attention.

Medicine in the 21st Century has continued to push the boundaries of science to unthinkable limits, often fusing complex technological elements and concepts together to achieve great feats. Two major contributing factors that have increased the use of high-technology in modern day medicine are Evidence-based Medicine and Health-Informatics. One study completed at Ohio State University (2016) proved how beneficial combining high-technologies such as Health Informatics Tools (HIT) and Evidence-Based Medicine can be. "Achieving a vision of 'interventional informatics ' will requires us to re-think how we study HIT tools in order to generate the necessary evidence-base that can support and justify their use as a primary means of improving the human condition" (Embi et al, 2016). This synergistic effect is just one of many countless examples of the successful advancements in medicine today. These strings of successful advancements in medicine can almost certainly be linked to the progression of specific

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

There was a broad consensus for five priority areas for future research, to integrate approaches across different technologies and to include diverse populations for future human studies. There is a need for creating new collaborations with versatile teams that combine bioinformatics, genetics, molecular biology, and cell physiology.

Thus an interdisciplinary approach should be utilized as a broad range of scientific discipline from biochemistry to informatics which will help us to identify and understand the mechanism for cancer progression and its prognosis. With the rapid changes in the technology, there has been various challenges which have come into the picture due to regulatory oversight and legislation. One of the major challenges is to manage large amount of data generated via assays thus it requires massive computational resources for data storage, processing and interpretation. Certain informatics resources such as ClinGen and ClinVar database of the National Center for Biotechnology Information (NCBI) has provided us with easily accessible data. The identification and routine deployment of molecular markers to improve classification of indeterminate lesions will be of great benefit to patients and surgeons and will reduce costs to the health care system by limiting unnecessary surgical procedures. More multidisciplinary research, closer collaborations is required between drug and cancer biomarker developers, clinicians, biologists, biostaticians and IT professionals, so to develop cancer biomarkers to improve patient outcome as well as to help the clinician in rapid clinical decision

Graduated from Nova Southeastern University, January, 2016. Master of Science in Biomedical Informatics (MSBI), Extensive experience working with the Epic System, data analysis and data management. HIPAA Certified.

Tempus' analytics platform enabled doctors to gain invaluable and comprehensive insights and understanding of a patients cancer situation by taking in huge amounts of genomic data and analyzing it using Tempus' algorithms.

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

They can aid in identifying biological pathways affected by mutated genes. By projecting lists consisting of deleted, amplified and mutated genes onto biological pathways, a statistical improbable subset of unrelated genes would turn out to be closely clustered in ‘reaction space’. Pathway analysis can therefore reveal valuable insight into the underling biological causes of a disease. [Wu, Feng and Stein, 2010]

One very important issue of biomarker discovery is that the models have to easily interpretable, i.e., integrative models have to be not only predictive of the disease, but also interpretable enough so that domain experts can infer useful knowledge from the obtained patterns. In one such effort to make models interpretable, domain information about disease relationships was used as prior knowledge during model development. In addition, a novel metric called I-score was proposed using medical literature to quantify the interpretability of obtained

While living organisms are interesting and exciting to think about, yet they have baffled scientists since the beginning. Now interdisciplinary groups are working together address questions in these systems. I want to work on complexity of biological systems using dynamical systems theory and network analysis. These approaches in systems biology give an insights into intricate events such as cell functions and diseases.

HOW CAN NGS FORWARD RESEARCH IN POLYGENIC DISEASES?By Joymariel Melecio-Viñales Recently, I was asked how Next Generation Sequencing technology may lead to significant advances in the study of polygenic diseases. As not being Biology my area of expertise, this question led to a lot of research. The first thing I did was to study the term polygenic, from which my first guess it was to mean “many genes” as poly- derives from the Greek meaning “much or many” and “genic” from gene, either the production of or to be related to genes (YourDictionary, 2013). Therefore, there was a chance of the term polygenic disease to describe a disease or illness related to a disorder in the gene or influenced by many genes. Scientific literature defines a polygenic disease as an affliction that runs in the family, however, only a few members of the family exhibit that phenotype (Alberts, et al., 2008). But what is a phenotype? Well, Alberts, et al. defines phenotypes as a characteristic that can be observed from an individual or a living organism (cells). What is really interesting is that these characteristics can be either physical or behavioral (Alberts, et al., 2008) (T, Jasuja, & Sehgal, 2016)and that these by be affected by either environmental factors or genetic disorders (Kitchen, 2010). This means that those characteristics that define you such as your hair color, eye color, height, skin color and weight, among others, and if you are bipolar or suffer severe anxiety, including any

I will conduct a comparative analysis of multiple force-directed algorithms used to identify clusters in biological networks. The analysis will consider topics such as the algorithm process, amount of preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K-Means, SPICi, Markov Clustering, RNSC, and PBD will be used for the comparison. I will identify the best algorithm according to my analysis for each type of input data studied.

The field of bioinformatics is the process of technology by the use of computers (BioPlanet, 2015). This field manages biological information (BioPlanet, 2015). We all use computers on a regular basis, but computers are capable of much more they can store biological information, gather this information, analyze and integrate genetic information as well (BioPlanet, 2015). All these things can be applied towards drug discoveries like gene- based development (BioPlanet, 2015). Bioinformatics just recently became more popular since the availability of genomic information (BioPlanet, 2015). Now, because of the popularity of bioinformatics it has become a very important part of biology in many areas (BMIRC, 2015). In a type of biology called molecular biology bioinformatics is used in the extraction of results and data (BMIRC, 2015). Take another field for example, genetics and genomics, it sequences genomes and observes their mutations (BMIRC, 2015). It also plays an important part in organizing biological and gene data (BMIRC, 2015). Both of these are examples of how bioinformatics is in a lot of areas of biology (BMIRC, 2015).The tools in bioinformatics help in the comparison of data such as genetic and genomic (BMIRC, 2015). It also helps you receive a better understanding of molecular biology (BMIRC, 2015). At a deeper level