President Obama Community College Concept Can Work By Offering A Genomics and Bioinformatic Technology Major:
Washington DC ( AP) ----- President Obama is on to something Congress Women Shelia Jackson Lee, Congressman Al Green, Texas Governor Gregg Abbott,
Houston Mayor Annise Parker and Houston Community College System. If HCC located in Houston Texas will offered a Genomics and Bioinformatic technical major, this innovative idea will create several thousand jobs and the Free Community College program will pay for itself. President Obama Community College Free For Millions Of Students
. On January 9, Obama announced a plan to make community college free for "all students if they attend classes at least half time and maintain a grade point average of 2.5 or better,"
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyze the function and structure of genomes (the complete set of DNA within a single cell of an organism).
President Obama Community College Concept Can Work By Offering A Genomics and Bioinformatic Technical Major Bioinformatic is the science of computer information systems. As an academic field it involves the practice of information processing, and the engineering of information systems.
Barron's Medical Journal Interview London England's Angel Biotechnology
The Genomics and Bioinformatic course should cover gene privacy administration; Gene data disseminate, management, and interpret of large, multi-scale cloud data.
Lets discuss the need for privacy, Female patients with breast cancer (n=100) completed a questionnaire assessing attitudes regarding concerns about privacy of genomic data.
Laboratory: Sunnyvale Center: Palo Alto Scientist Interview On Genomic & Infomatics
Results Most patients (83%) indicated that genomic data should be protected. However, only 13% had
significant concerns regarding privacy of such data. Patients expressed more concern about insurance
discrimination than employment discrimination (43% vs 28%, p<0.001). They expressed less concern about
research institutions protecting the security of their
molecular data than government agencies or drug
companies 
(20% vs 38% vs 44%; p<0.001). Most did
not express concern regarding the association of their
genomic data with their name and personal identity
(49% concerned), billing and insurance information
(44% concerned), or clinical data (27% concerned).
Significantly fewer patients were concerned about the
association with clinical data than other data types
(p<0.001). In the absence of direct benefit, patients
were more willing to consent to sharing of deidentified
than identified data with researchers not involved in
their care (76% vs 60%; p<0.001). Most (85%)
patients were willing to consent to DNA banking.
Discussion While patients are opposed to
indiscriminate release of genomic data, privacy does not
appear to be their primary concern. Furthermore, we did
not find any specific predictors of privacy concerns.
Conclusions Patients generally expressed low levels of concern regarding privacy of genomic data, and many expressed willingness to consent to sharing their genomic data with researchers.
Cancer therapy is increasingly personalized to the molecular characteristics of a particular patient and his/her tumor.1 The National Cancer Institute (NCI) defines personalized cancer therapy as the ‘application of genomic and molecular data to tailor medical care to individuals’.
2 Personalized cancer therapy has the potential to improve treatment response, reduce adverse effects, and reduce cost of care.3 In this paper, we refer to personalized, precision and genomically informed cancer therapy interchangeably. Similarly, although the terms ‘genetic’ and ‘genomic’ have distinct scientific meanings,3a we favored the more familiar ‘genetic’ in the questionnaire administered to patients and were not strict about the distinction.
Although patients and providers express an interest in genomically informed therapy,4 concerns regarding the privacy of genomic data have been raised, particularly in the context of research.5 6 Genomic data cannot be completely ‘deidenti- fied’, 7 8 thus these data pose a serious privacy risk. As a result, the storage and sharing of genomic data in the context of research is presently a topic of much debate.9
Data from a Gallup poll10 showed that medical privacy is important to people in the general population, and privacy concerns related to genetic testing and hereditary cancers have been raised.11 However, previous studies have asked general questions about medical privacy, such as ‘Who do you think should be allowed to see your medical records without your permission?’. 10 Such questions are not representative of current research being considered or conducted where identified data are collected for specific, explicitly defined purposes. Furthermore, many studies have focused almost exclusively on healthy participants, who did not have an established relationship with a research organization where they were receiving care. Thus, previous studies may not have considered the specific privacy concerns of patients with cancer, for whom molecular testing and genetic research may have direct and indirect benefits.
Finally, little is known about patients’ privacy concerns related to molecular testing in personalized cancer therapy, which mainly focuses on information related to somatic (as opposed to germline) mutations.12 In contrast with germline mutations, somatic mutations are not heritable. Thus, from a privacy perspective, they may be less concerning to patients.
Understanding patients’ privacy concerns regarding genomic data may help researchers and clinicians better address patient concerns, and may encourage participation in genomic studies.13 Further, patients rather than the general public are the most relevant population. Our results may also help to align the public policy debate with the concerns of patients, rather than the general public.
Cloud Data and The challenges posed by the need to disseminate, manage, and interpret large, multi-scale data pervade efforts to advance understanding of cancer biology and apply that knowledge in the clinic. For several years, the volume of data routinely generated by high-throughput research technologies has grown exponentially. The storage, transmission, and analysis of these data have become too costly for individual laboratories and most small to medium research organizations to support. For optimal progress to occur, access to large, valuable data collections and advanced computational capacity must be readily available to the widest possible audience.
“ On April 7, 2013, Dr. Harold Varmus and other members of the Institute's senior leadership issued a letter to NCI grantees seeking input on these and other computational challenges they encounter on an almost daily basis. Dr. Varmus stated that the NCI, as part of its ongoing investigations into next-generation computational capabilities to serve the research community, has begun exploring the possibility of creating one or more public "cancer knowledge clouds" in which data repositories would be co-located with advanced computing resources, thereby enabling researchers to bring their analytical tools and methods to the data. Reactions to this informal request for information were generally positive, with respondents focusing on six general themes: data access; computing capacity and infrastructure; data interoperability; training; usability; and governance.”
Based in part on this information, Dr. George Komatsoulis, then interim director of the Center for Biomedical Bioinformatic and Information Technology (CBIIT), which administers the National Cancer Informatics Program (NCIP), led the creation of a concept document describing a project to develop up to three cancer genomics cloud pilots for review by the cancer-research community. Dr. Komatsoulispresented the concept (time reference 05:58:00) at a joint meeting of the NCI Board of Scientific Advisors (BSA) and the National Cancer Advisory Board (NCAB) on June 24, 2013, where it received unanimous approval.
One great example of the type Bioinformatic technology is the Very Gene project. VeryGene is developed as a curated, web-accessible centralized database for the annotation of tissue-specific/enriched genes. It currently contains entries for 3960 human genes covering 128 normal tissue/cell types compiled from the expression profiling of two large microarray data sets [ref1, ref2]. It brings together much-needed information on preferred tissue/subcellular localization, functional annotation, pathway, mammalian phenotype, related diseases and targeting drug associated with any of these genes as a result of data integration from multiple sources. Information can be searched through gene, tissue and disease views and search result can be downloaded easily. We commit our best efforts to update and expand VeryGene as new and relevant information emerges
As an initiative toward systems biology, the VeryGene web server was developed to fill this gap. A significant effort has been made to integrate TSGs from two large-scale data analyses with respective information on subcellular localization, Gene Ontology, Reactome, KEGG pathway, Mouse Genome Informatics (MGI) Mammalian Phenotype, disease association, and targeting drugs. The current release carefully selected 3,960 annotated TSGs derived from 127 normal human tissues and cell types, including 5,672 gene-disease and 2,171 drug-target relationships. In addition to being a specialized source for TSGs, VeryGene can be used as a discovery tool by generating novel inferences. Some inherently useful but hidden relations among genes, diseases, drugs, and other important aspects can be inferred to form testable hypotheses.
Two interactive matrix views have been developed to provide users with intuitive, high-level summaries of expression data and from where they can easily move to levels of greater detail. The tissue-by-developmental stage matrix provides global overviews of the spatio-temporal expression patterns of genes. The tissue-by-gene matrix enables a comparison of expression patterns between genes. Both types of matrices can be expanded (and collapsed) along the tissue axis, based on the hierarchical organization of the anatomy. Rows and columns in the matrices can be selected to refine the data set. The matrices have been added as new tabs to the gene expression data summaries. These matrices can be accessed by searches using the Gene Expression Data Query and via links from the Mouse Developmental Anatomy Browser.
The Gene Expression Data Matrix is now accessible from the Access Data section of http://www.informatics.jax.org/expression.shtml It returns all of GXD's expression data in a tissue-by-developmental stage matrix. When using this matrix, users start with a high-level overview of GXD's data and then can interactively view and select expression data for the specific tissues and / or developmental stages of interest.
The standard Gene Expression Data Query now lets users define gene sets based on genomic location. One can, for example, search for disease candidate genes that have been mapped to a genomic region and are expressed in tissues affected by the disease.
Texas can be the leader Barron’s Medical Journal.
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