Exercise Genomics (Molecular and Translational Medicine)


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Skip to main content. Login HP Staff Students. Search form Ricerca. Struttura Courses Departments Libraries. Servizi Services. Gene expression data extracted from microarray experiments could generate high-dimensional feature space for discriminating between different classes when thousands of genes are measured simultaneously. Yana et al [15] investigated the feature selection methods that evaluate the "informativeness" of a set of genes. They demonstrated that including gene-gene interactions have better classification power in gene expression analysis.


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Li et al [16] presented a novel framework to select feature subsets from all the newly extracted components from PCA and PLS. They demonstrated that their feature selection scheme improved generalization performance of classifier according to the evaluation on several typical datasets. Zhang et al. Xu et al [18] developed a novel approach combining both supervised learning with unsupervised learning techniques to generate discriminative gene clusters.

Their experiments on both simulated and real datasets exhibited that their method could produce a series of robust gene clusters with good classification performance compared with existing approaches. They showed the methods could improve upon supervised approaches and shed new light on the functions of unclassified ORFs and their co-regulation. Goh et al. They constructed a set of biocomputing tools that include relational database design and utilization of disorder prediction algorithms. Their exercise provides an example showing how the combined use of intrinsic disorder predictions and relational databases provides an improved understanding of the functional and structural behaviour of viral proteins.

Sudden death syndrome of soybean is an ec onomically important disease. Yuan et al [21] reported their analyses of microarrays measuring transcript in whole plants after A. They found significant variations in transcript abundances which leaded to identification of a putative resistance pathway involved in responding to the pathogen infection in A. They presented detailed analysis and explanation to justify the statement and proposed an equivalent radio-sensitivity model. They conclude that radio sensitivity is a sophisticated function over tumor volumes.

Hewett et al. They found that using the classification rankings from MDR could achieved effective tumor classifications from cancer gene expression data. When the prostate cancer cell transfer from androgen-dependent to androgen-independent, androgen ablation, the most commonly-used therapy for progressive prostate cancer, is ineffective. Wang et al [24] developed a modelbased computational approach to identify transcription factors and microRNAs influencing the progression of androgen-dependent prostate cancer to androgen-inde-pendent prostate cancer.

This result suggested that the capability of transcription factors to initiate transcription and microRNAs to facilitate mRNA degradation are both decreased in androgen-independent prostate cancer. Wu et al. They showed that stringent statistical analysis, combined with hierarchical clustering and pathway analysis may offer deeper insight into the biological processes reflected from a set of expression array data. Extracting information from batch BLAST can be consuming, insufficient, and inaccurate for large dataset. The application generates a tab delimited text file that can be easily imported into any statistical package such as Excel or SPSS for further analysis.

The software is open access, free available to public. Lieutaud et al. MeDor provides a HCA plot and runs a secondary structure prediction, a prediction of signal peptides and transmembrane regions and a set of disorder predictions.

Individualized genomics and the future of translational medicine

This free available software tool could offer fast, simultaneous analysis of a query sequence by multiple predictors and provides a graphical interface with a unified view of the outputs. Genomics, bioinformatics and personalized medicine are upcoming emerging fields, and the resonance and synergy of these fields are enormous, which will have profound influence on the advances of science and medicine. The location of the conference Harvard Medical School was strategically chosen to attract the participation of renowned researchers who work in Cambridge - Boston, Massachusetts area which is an international biomedical research center radius of 50 miles.

By doing so, both World-comp in Las Vegas and the IEEE 7th Bioinformatics and Bioengineering at Harvard Medical School have achieved the goal of addressing challenges in the emerging contemporary Bioinformatics and Bioengineering fields that would not further exist "as is" in separate silos as isolated fields, but instead spanning a wide spectrum of knowledge and research focusing on the synergy of these two fields to solve important but difficult biological and medical problems.

Not many conferences would have such multilateral cooperative efforts, with the help from ISIBM and the Worldcomp, the IEEE 7th Bioinformatics and Bioengineering was so unique to help to pass the hurdles and break the invisible academic barriers between these two different but both very important disciplines. Most updated cutting-edge technologies and breaking though ideas had brought into the conference at Harvard Medical School in the keynote and tutorial lectures, and the open discussions and scientific exchanges among attendees, which inspired innovations, novel ideas and scientific discoveries.

The IEEE 7th Bioinformatics and Bioengineering provided such unique infrastructure and platform to promote interdisciplinary and multidisciplinary research and education. The conference brought together top researchers from the United States and around the world to exchange research results and address open issues in all aspects of bioinfor-matics and bioengineering. The IEEE conference hosted a number of cutting-edge research workshops and special interest research sessions in collaboration with leading scientists from the National Human Genome Research.

Built on the great success of Biocomp - Worldcomp, the aim of the IEEE 7th Bioinformatics and Bioengineering was to assemble a spectrum of affiliated research workshops, distinguished keynote and tutorial lectures and special interest research sessions into a coordinated research meeting. Due to the broad knowledge and scopes that IEEE 7th Bioinformatics and Bioengineering encompassed, the conference received more than high-quality research papers.

The papers covered broad range of research fields including genomics, bioinformatics and bioengineering that created the theme of the IEEE flagship conference: genomics, molecular imaging, bioinformat-ics, and bio-nano-info integration are synergistic components of translational and personalized medicine research. For that reason, it is the only meeting whose components are also defined dynamically in response to specific needs of largest number of keynote lectures, cutting-edge research tutorial lectures, special interest research workshops and special sessions with academic supports and contributions from leading scientists at NIH, national laboratories and research universities.

Each proposal and nomination has been reviewed and voted by the IEEE 7th Bioinformatics and Bioengineering committee to ensure that participants would be benefited significantly from the academic event. Brian D. Hamid R. Ferenc A. Laura L. Department of Health and Human Services ,. Michelle M. Obviously, the conference would not have achieved such a great success without the hard work and voluntary efforts by many contributors. Organizing such a major academic event in the fields is not possible without contributions from members of program and scientific review committee.

Thanks must be given to them for their professionalisms. Uversky, Yunlong Liu and the pro-. We must extend our sincere thanks to all chairs, organizers and committee members names and affiliations are listed below for their dedications and professional services. In particular, Hamid R. Arabnia, Walker Land, Jr. Zhu managed the paper submission system and handled various important organizing and academic affairs; Youping Deng handled not only registration and finance but also reviewed a large number of papers; Jonathan Jesneck and Pengyu Hong helped local arrangements; Qingzhong Liu, Mehdi Pirooznia, Zejin Jason Ding, and Bingxin Shen maintained the website.

Walker Land, Jr. Arabnia, Youp-ing Deng, Michelle M. Zhu and Jack Y. Yang helped Scientific Review Committee Co-Chairs in the overall systematic evalutions of all reviewers' comments and ranks. Zhu, Heng Huang, Nikolaos G. Bour-bakis and Jun Ni helped in the overall organization of the conference and Michelle M.

Zhu arranged the scientific presentations and program schedules; Jack Y. Mary Qu Yang and Jack Y. Yang initiated not only the journal issues but also the NSF proposals that provided funds to support students' travel; this is the first time that IEEE Bioinformatics and Bioengineering is now not only offering leading scientific journal issues such as BMC Genomics impact factor: 4. Yang initiated and arranged the organization of cutting-edge research workshops, tutorial lectures, special sessions and poster presentations in addition to the traditional keynote lectures.

Yang took the initiatives and invested significant efforts to expand the size of IEEE Bioinformatics and Bioengineering from traditional papers each year for past 6 years to more than full-length regular research paper submissions this year.

Translational Medicine MRes, MSc, PgDip, PgCert

The theme of the IEEE 7th Bioinformatics and Bioengineering at Harvard Medical School was to promote the genomics, molecular imaging, bioinformatics, and bio-nano-info integration research that are important compo-. The great learning outcomes have achieved, and. So the more students and scientists attended, the more benefits in research knowledge they would obtain. To increase participation of underrepresented groups such as minorities, women, and disabled individuals, Drs.

A committee chaired by Prof. Arabnia, and Co-Chaired by Prof. Homayoun Valafar, Prof. Yunlong Liu, Prof. Zhu, Prof. Jonathan Jesneck, Prof. Yuehui Chen, Prof. Yufang Jin, Prof.

Translational genetics

Alex Zelikovsky, Prof. Chung-Kuan Cheng and Prof. Anu Bourgeois was formed to select the NSF student travel fellowships, best papers and best posters. The winners determined by the committee are listed below:. Keith Dunker, Christopher J. Yang, Zoran Obradovic and Vladimir N. The IEEE 7th Bioinformatics and Bioengineering as a large IEEE flagship international conference with 7 years of tradition and leading reputation provided an important platform at the conference center of Harvard Medical School for scientific discussions and collaborations.

Because the IEEE 7th Bioinformatics and Bioengineering received more than papers in more diverse fields, the committee had decided to review and rank the papers in different categories in accordance to the enormous expansion of the conference. Nik Bourbakis, Prof. Michael Raymer and Prof. George Karypis determined the following Outstanding Achievement Awards:. Department of Health of Human Services.

Bioinformatics, genomics and bioengineering play fundamental roles in our understanding and designs of biological systems and therapy medicine at all levels of organization, from molecular biology, life sciences to engineering and computer sciences. Bioinformatics is a "bourgeoning out" field that studies the development of algorithms, computational and statistical techniques, and theories to solve formal and practical biomedical problems.

It also refers to hypothesis-driven investigations of specific biological problems using computer simulations that carry out with experimental or simulated data, with the primary goal of discovery and the advancement of biological knowledge. Bioinformatics deals with the information and hypotheses. It is a technique-driven research that utilizes many engineering and computer science methods. Bioinformatics focus on the developments of mathematical and computational tools to extract useful information from data produced by high-throughput biological techniques such as genome sequencing, protein sequences, gene regulation, gene networks, ChIP-on-chip and DNA microarrays data as well as mass spectrometry and MRI.

Bioinformatics broadly includes systems biology and computational biology. Bioengineering is a broad-based engineering discipline that studies engineering biological processes involving product design, sustainability and analysis of biological systems. Bioengineering deals with all the biological, medical, pharmaceutical the agricultural fields using engineering methods and approaches. Bioengineering thus deals with living organism and biological products and broadly includes food engineering and biotechnology. This module emphasises hands-on data analysis activities, and will provide you with the knowledge and skills required to work with this data in industry or academia.

Genomic Technologies in Clinical Diagnostics 15 credits. Powerful new technologies are transforming healthcare. Over the last decade technologies have emerged that allow scientists to interrogate the genome at the level of the chromosome or a single nucleotide in just a few days, resulting in greater availability of genomic data which is increasingly being used to determine health management.

This online module focuses on these fundamental genomic technologies. It will allow you to familiarise yourself with the molecular and cytogenetic techniques currently employed in diagnostic laboratories and, using that knowledge, develop testing stratagems for particular clinical conditions. You will also gain an in-depth understanding of genetic technologies currently used in research, and the challenges involved in implementing novel technologies in the diagnostic setting.

Research Methods and Management 15 credits. This MRes-specific module tightly integrates with the research project by introducing you to the conceptual, technical, regulatory and ethical aspects of conducting research. It also covers a number of transferable skills related to self-directed learning, literature analysis, communication, and time management.

The principal assessment will be on the research proposal for your laboratory project. Research project credits. The supervised research project provides immersive work-based training in translational science. You will choose a subject, formulate a specific research question or aim, devise a research strategy to address this question, perform the research, and analyse the resulting data.

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The project background, experimental procedures, results and discussion are written up as a 25,word dissertation and presented orally to an audience with the aid of a poster. To facilitate your transition to research, you study four core taught modules during the first part of the year, with the remaining time focused entirely on project work. A key principle guiding development of the MRes course has been to create a connected curriculum that emphasises the close alignment of teaching and research.

To help achieve this, you will choose a project subject and supervisor early on, and the taught modules will provide multiple opportunities to relate session content to the themes of your individual project. You will prepare an extensive project-based research proposal as part of the Research Methods and Management module.

Nutrigenomics in Clinical Practice - Genes, Food, and Specialty Diagnostics

Cross-module connections will also be created by revisiting selected themes in each of the taught components, and through the use of a mandatory portfolio in which you record in-course assessment work, feedback, and reflections. The supervised research project constitutes a central learning method by providing immersive work-based training in translational science. The taught modules will be delivered using lectures, tutorials, presentations, discussions and online activities. Teaching will generally be organised so that you obtain background information first, then explore the subject more deeply through presentations, discussions, exercises or practicals.

You may obtain background knowledge through online or face-to-face lectures or through independent and self-directed study. Our postgraduate courses in Translational Medicine equip graduates with expertise in bench-to-bedside pathways, genomic diagnostics and data analysis.


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  5. The MSc and PGDip courses include additional training in clinical trials management, personalised medicine, population health and epidemiology. By providing practical research experience and training in drug development, genomic diagnostics and data analysis, our Translational Medicine MRes equips you with skills that are in great demand in the life sciences sector. According to the Association of the British Pharmaceutical Industry, there is currently a skills shortage in translational medicine which requires complex understanding to bridge the gap between bench and bedside.

    The MSc and PGDip courses emphasise taught modules and are designed to prepare you for professional roles related to the support, administration, funding, management or governance of translational research.


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    Exercise Genomics (Molecular and Translational Medicine)
    Exercise Genomics (Molecular and Translational Medicine)
    Exercise Genomics (Molecular and Translational Medicine)
    Exercise Genomics (Molecular and Translational Medicine)
    Exercise Genomics (Molecular and Translational Medicine)
    Exercise Genomics (Molecular and Translational Medicine)
    Exercise Genomics (Molecular and Translational Medicine)

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