Finals Week Looms

Hope you all had a fantastic Thanksgiving holiday! Black Friday is over... this can only mean one thing to all college students. Finals week is almost here! Let's all start preparing NOW! Don't let finals creep up on you and end up cramming at the last minute. It never works in your favor. Even 30 minutes a day beginning today, will help. Procrastination is the enemy- let's rock these last few weeks! Happy studying.

Project Reference- NLP and indexing

Proc AMIA Symp. 2001: 17–21.

PMCID: PMC2243666

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

A. R. Aronson

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This article has been cited by other articles in PMC.

Abstract

The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

Full text

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1000 Genomes Project-Exonic Sequencing Datasets

Copy Number Variation detection from 1000 Genomes project exon capture sequencing data

Jiantao Wu, Krzysztof R Grzeda, Chip Stewart, Fabian Grubert, Alexander E Urban, Michael P Snyder and Gabor T Marth

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BMC Bioinformatics 2012, 13:305 doi:10.1186/1471-2105-13-305
Published: 17 November 2012

Abstract (provisional)

Background

DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.

Results

As members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%.

Conclusions

This study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.

The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.

Research: Hurry up and wait!

This has been one of those slow weeks-research often moves at a snails pace. While waiting for responses from clinical labs this week, I had time to reflect on the life cycle of research. It isn't always glamorous.
The fun begins once a research topic has been defined and a hypothesis formulated. Getting organized and defining fuzzy concepts that allow them to be measured, qualitatively and quantitatively sets the stage for the journey ahead. For me, creating a statistical data set ramps up the excitement of a new project. Things really heat up once an experiment is launched. And then....the waiting begins. Results can't be forced, so time takes control of the process. I am impatient. I hate waiting. I sent second requests to 77 labs with a deadline of Nov 30th-updated the database- prepared a rough outline of my paper...the boring stuff. I suppose this is the calm before the storm. I hope to have more next week...

http://explorable.com/defining-a-research-problem.html

This is it!!!! My Dream Grad level Research project

Improving accuracy for cancer classification with a new algorithm for genes selection

Hongyan Zhang, Haiyan Wang, Zhijun Dai, Ming-shun Chen and Zheming Yuan

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BMC Bioinformatics 2012, 13:298 doi:10.1186/1471-2105-13-298
Published: 13 November 2012

Abstract (provisional)

Background

Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.

Results

We applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature.

Conclusions

Evaluation of a gene's contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.

Alabama State University- Center for NanoBiotechnology Research

Center for NanoBiotechnology Research

Main Content

The Center for Nano Biotechnology Research (CNBR) at Alabama State University aims to enhance research infrastructure to perform world-class research in the emerging new area of nanotechnology.

The center facilitates four research themes:
1) Structural studies of RSV at nanoscale and viral inhibition by nanoparticles,
2) Carbon nanotube Attached with ssDNA as nanosensor for detection of Salmonella Typhimurium,
3) The Development of Nanobiomaterials for Drug Delivery, and
4) Delivery of Nanoparticle Encapsuled Anti­Chlamydial Peptides in an Animal Model.The CNBR aims to:
These projects are carried out in collaboration with the University of Louisville, University of South Florida, University of Alabama at Birmingham and Tulane National Primate. The center includes an international scientist exchange program in collaboration with China, India, Argentina, Singapore and Japan. The CNBR aims to:

• Communicate with the community
• Perform world-class research in nanobiotechnology
• Provide educational opportunities to minority students
• Develop new curricula and programs in nanobiotechnology
• Enhance industry-institutional research and commercialize research products

http://www.newscientist.com/article/dn22502-zoologger-the-leggiest-animal-in-the-world.html

http://www.newscientist.com/article/dn22502-zoologger-the-leggiest-animal-in-the-world.html

Transfer Information Link

http://www.collegetransfer.net/AlabamaStateUniversity/TransferProfile/tabid/145/Default.aspx

LHC Rap

http://youtu.be/j50ZssEojtM

Research Possibilities

http://www.alasu.edu/academics/colleges--departments/science-mathematics-technology/biological-sciences-department/phd-microbiology/faculty/dr-shree-singh/index.aspx

Hi All!

My name is Deeda Webster, I am interested in the field of Bioinformatics. Currently, I am enjoying an unpaid internship at Arizona Strategic Enterprise Technology. The research project I've been assigned involves a survey of all CLIA accredited clinical laboratories in the State of Arizona, there are about 587. I am currently seeking to identify the capability of these laboratories to electronically send and receive structured laboratory results. Standardized nomenclature use by laboratories, such as the Logical Observation Identifiers Names and Codes (LOINC) is another major focus of my research . At the conclusion of the project I will analyze the data and create statistical summary and comparison reports to identify laboratory's barriers to interoperability and standardization . It has been my good fortune to meet with Arizona DHS epidemiologists to discuss the future of syndromic surveillance. Fascinating!