The First Ghana Biomedical Convention

Venue: Institute of Local Government Studies- Accra

"Opportunities and Challenges of New Technologies in Bio-Medicine"

13th - 15 August 2008

Plenary Speakers

Dr. Peter Atadja, Ph.D
Group Leader (DD II) and Global Development Program Team Research leader, NIBR and Novartis Pharma
Novartis Institutes for Biomedical Research
250 Mass Avenue
Cambridge MA 01239
Email: peter.atadja@novartis.com
E-mail: peter.atadja@pharma.novartis.com
Biography
Biography

Research

Biographic abstract: Born in Ghana, Peter Atadja received the Bachelor of Pharmacy degree Honours from the University of Science and Technology, Kumasi, Ghana. At the Hebrew University of Jerusalem, Israel, he made critical contributions to the pioneering work of his mentors Professors Alex Levitzki and Michael Chorev in the designing and developing of first-generation tyrosine kinase inhibitors (Tyrphostins) for anti-cancer therapy. This work was recognized with his M.Sci. (Magna Cum Laude) in Pharmaceutical and Medicinal Chemistry and the Hebrew University's Michael Sherwood Prize for graduate research. Peter Atadja obtained his Ph.D. in Molecular and Cellular Oncology from the University of Calgary, Canada, where his research yielded important fundamental observations in molecular mechanisms of cellular senescence and their linkage with tumor suppressor pathways. His doctoral dissertation was also nominated for two prestigious Canadian research awards (the Natural Sciences and Engineering Research Award and the Canadian Graduate Research Award). In 1997, he joined Novartis Pharmaceuticals in New Jersey to work on their efforts to develop drugs that target epigenetic mechanisms for anti-cancer therapy. Peter Atadja's productive research at Novartis has led to the development of LBH589, a histone deacetylase inhibitor that was discovered and developed preclinically in his laboratory, which has shown dramatic responses in cutaneous T-cell lymphoma and is now undergoing further clinical development in additional hematologic and solid malignancies. Peter Atadja is currently a Group Leader and Senior Research Investigator at the Novartis Institutes for Biomedical Research in Cambridge, MA, and has published more then 50 peer reviewed articles, invited reviews and book chapters, and more than 100 abstracts and presentations.

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Prof. Henry Colecraft, Ph.D.
Associate Professor of Physiology & Cellular Biophysics & Pharmacology
Columbia University Medical Center
Email: hc2405@columbia.edu
Research

Prof. George B. Richter-Addo, PhD
Professor and OU Presidential Professor & Department Chair
Department of Chemistry and Biochemistry
University of Oklahoma
Email: grichteraddo@ou.edu
Research

Prof. Winfried Amoaku, FRCS, FRCOphth, PhD,
Assoc. Professor/Reader in Ophthalmology and
Vis Sci/Hon Consultant Ophthalmologist
University of Nottingham/Nottingham University Hospitals NHS Trust.
Email: winfried.amoaku@nottingham.ac.uk
Research

Dr. James Adjaye, PhD
Molecular Embryology and Aging group
Max-Planck Institute for Molecular Genetics
(Department of Vertebrate Genomics),
Ihnestrasse 73, D-14195 Berlin, Germany.
Email: adjaye@molgen.mpg.de
Research Team
Research

Dr. George Acquaah-Mensah, Ph.D
Asst. Professor
Dept. of Pharmaceutical Sciences
Massachusetts College of Pharmacy and
Health Sciences, SOP-WORCESTER
19 Foster Street, Worcester, MA 01608
Email: George.Acquaah-Mensah@mcphs.edu
Research

Dr. Elvis K. Tiburu, Ph.D.
BIDMC-Harvard Institute of Medicine
E-mail: etiburu@bidmc.harvard.edu
Research

Dr. Akwasi Anyanful, Ph.D.
Department of Pathology
Emory University School of Medicine
E-mail: aanyanf@emory.edu
Research

Dr. Elsie Effah Kaufmann, PhD
Senior Lecturer & Head
Biomedical Engineering Department
Faculty of Engineering Sciences
University of Ghana
Legon
Email: eek@ug.edu.gh
Talk: Elsie will be speaking on “Tissue Engineering and BioMaterials”.

Dr. John Okyere, PhD
Research Fellow
School of Medical & Surgical Sciences
Institute of Infection, Immunity & Inflammation
University of Nottingham
University Hospital
Queen's Medical Centre
Nottingham NG7 2UH UK
Email: john.okyere@nottingham.ac.uk
Research

Solomon Ofori-Acquah, PhD
Assistant Professor
Aflac Cancer Center and Blood Disorders Services
Division of Hematology/Oncology/BMT
Department of Pediatrics
Emory University School of Medicine
2015 Uppergate Drive
Atlanta, GA 30322
Email: soforia@emory.edu
Research

Martin Safo, Ph.D.
Assistant Professor
Medicinal Chemistry
Virginia Commonwealth University
USA
Email: msafo@vcu.edu
Research

Samuel Kojo Kwofie, MSC
National Bioinformatics Network/South African National Bioinformatics Institute
Cape Town. SA
Email: ghanabiomed@nbn.ac.za
Talk: Translating DNA into money: strategies for the growth of biotechnology in Ghana.

Motivation: Some present-day species have incurred a whole genome doubling event in their evolutionary history, and this is reflected today in patterns of duplicated segments scattered throughout their chromosomes. These duplications may be used as data to ``halve'' the genome, i.e., to reconstruct the ancestral genome at the moment of doubling, but the solution is often highly non-unique. To resolve this problem, we take account of outgroups, external reference genomes, to guide and narrow down the search. Results: We improve on a previous, computationally costly, "brute force" method by adapting the genome halving algorithm of El-Mabrouk and Sankoff so that it rapidly and accurately constructs an ancestor close the outgroups, prior to a local optimization heuristic. We apply this to reconstruct the pre-doubling ancestor of S. cerevisiae and C. glabrata, guided by the genomes of three other yeasts that diverged before the genome doubling event. We analyze the results in terms 1) of the minimum evolution criterion, 2) how close the genome halving result is to the final (local) minimum, and 3) how close the final result is to an ancestor manually constructed by an expert with access to additional information. We also visualize the set of reconstructed ancestors using classic multidimensional scaling to see what aspects of the doubling descendants and outgroups influence the similarities and differences among the reconstructions.

Keywords: algorithms genome halving rearrangements whole genome duplication yeast

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Motivation: Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this paper, we describe in details our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is interesting because it is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2.Our tagger accomplished the highest F-scores among them and second over all. Moreover, we accomplished our results by mostly applying open source packages, making it easy to duplicate our results. Results: We first describe in details how we developed our CRF-based tagger. We designed a very high dimensional feature set that includes most of information that may be possibly relevant. We trained bi-directional CRF models with the same set of features, one applies forward parsing and the other backward, and integrated two models based on the likelihood scores and dictionary filtering. One of the most prominent factors that contribute to the good performance of our tagger is the integration of an additional backward parsing model. However, from the definition of CRF, it appears that a CRF model is symmetric and bi-directional parsing models will produce the same results. We show that due to different feature settings, a CRF model can be asymmetric and our feature setting for our tagger in BioCreative 2 not only produces different results but also give backward parsing models slight but constant advantage over forward parsing model for gene mention tagging. To fully explore the potential of integrating bi-directional parsing models, we applied different feature settings to generate many bi-directional parsing models and integrate them based on the likelihood scores. Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging.