Search results “Nci60 microarray data mining”
The Importance of Reproducible Research in High-Throughput Biology
VideoLectures.Net View the talk in context: http://videolectures.net/cancerbioinformatics2010_baggerly_irrh/ View the complete Cancer Bioinformatics Workshop, Cambridge 2010: http://videolectures.net/cancerbioinformatics2010_cambridge/ Speaker: Keith A. Baggerly, Graduate School of Biomedical Sciences, University of Texas Health Science Center at Houston License: Creative Commons CC BY-NC-ND 3.0 More information at http://videolectures.net/site/about/ More talks at http://videolectures.net/ 0:00 The Importance of Reproducibility in High-Throughput Biology: Case Studies in Forensic Bioinformatics 1:19 Why is RR So Important in H-TB? 2:43 Using the NCI60 to Predict Sensitivity 5:19 Fit Training Data 5:58 Fit Testing Data 6:46 5-FU Heatmaps - 1 7:21 5-FU Heatmaps - 2 7:30 5-FU Heatmaps - 3 7:46 Their List and Ours 8:28 Offset P-Values: Other Drugs 9:02 Using Their Software 10:00 Heatmaps Match Exactly for Most Drugs! - 1 10:45 Heatmaps Match Exactly for Most Drugs! - 2 11:34 Predicting Docetaxel (Chang 03) 12:36 Predicting Adriamycin (Holleman 04) 13:51 There Were Other Genes... 15:12 RR Theme: Don't Take My Word For It! 15:45 Potti/Nevins Reply (Nat Med 13:1277-8) 16:51 Adriamycin 0.9999 + Correlations (Reply) - 1 17:54 Adriamycin 0.9999 + Correlations (Reply) - 2 18:22 The First 20 Files Now Named 19:14 Validation 1: Hsu et al 20:29 The 4 We Can't Match (Reply) 21:00 Some Timeline Here... - 1 21:12 Some Timeline Here... - 2 21:44 Some Timeline Here... - 3 22:00 Jan 29, 2010 22:21 Why We're Unhappy... - 1 22:44 Why We're Unhappy... - 2 23:25 We Tried Matching The Samples 24:36 FOI(L)A! - 1 24:54 FOI(L)A! - 2 25:32 May 14, 2010 25:53 July 16, 2010 26:21 July 19, 2010 26:48 Subsequent Events, and a Caveat - 1 27:13 Subsequent Events, and a Caveat - 2 27:30 Some Observations 28:09 What Should the Norm Be? 28:38 Some Lessons 29:42 Questions
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Extracting Knowledge From Genomic Experiments By Incorporating the Biomedical Literature
Don't hesitate to comment below if you have any questions or additional phrases Extracting Knowledge From Genomic Experiments By Incorporating the Biomedical Literature James P. Sluka, Ph.D.InPharmix Incorporated [email protected] (317)-422-1464 InPharmix Inc. ,Value and Size of the Biomedical Literature Cost to sequence the Human Genome, ~$3B Yearly expenditures for biomedical research, >$100B Size of GENBANK, ~13GB Size of literature abstracts (MEDLINE), ~12GB 70 Bioinformatics Companies† 4 Scientific Text Mining Companies† †From: http://www.phrma.org/ and http://123genomics.homestead.com/files/companies.html InPharmix Inc. ,What is needed: Tools to integrate Genomics, Bioinformatics and the Literature Sequence Data Rational framework for the understanding of biological process or disease e.g., GENBANK User Guidance, Heuristics or NLP Genomics Data Relationships between entities Gene Lists Scientific Literature Abstracts and Papers InPharmix Inc. ,Dataset We have analyzed a subset of the NCI-60 cancer gene expression database [1]. The initial set consisted of the expression data for the full 9,703 genes for the three leukemia cell lines in the NCI database, CCRF-CEM, MOLT4 and K-562. CCRF-CEM and MOLT-4 are from acute lymphoblastic leukemias (ALL) whereas K-562 represents acute myelogenous leukemia (AML). The K-562/AML data was divided by the average for the two ALL lines in order to reduce the influence of genes characteristic of leukocytic cell lines. The resulting data is similar to the data set of Golub et al. [2] used for CAMDA-2000. The modified expression values were sorted and the 250 most highly expressed genes used as the initial data set. For these 250 genes we removed un-named genes including ESTs, KIAA's and genes annotated as "similar to" another gene, resulting in a final list of 160 named genes. In addition, we included a term for the disease, Acute Myelogenous Leukemia (AML). As our literature database, we used MEDLINE accessed via Entrez. [1] Scherf, U. et al., "A gene expression database for the molecular pharmacology of cancer". Nature Genetics, 24:3 (2000), 236-44. [2] Golub, T.R. et al., "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring", Science, 286 (1999), 531-537. InPharmix Inc. ,Assigning Names The first step in the analysis is to assign names for each gene that are suitable for searching in MEDLINE. In this case, the original names are those that appear in the NCI-60 database. Since these names tend to be brief, cryptic or outdated some work needed to be done to verify or correct the names. To assign the best possible name to each gene we used keyword and/or BLAST searches across several databases (GENBANK, OMIM, GDB and GeneCards). InPharmix Inc. ,PDQ_MED Algorithm I The basic input to PDQ_MED is a list of query terms encompassing the genes, proteins, diseases or other concepts under investigation. An individual query term can consist of more than one version of a particular name. Interleukin-1b, IL-1b, IL1beta… In addition, the user may explicitly join phrases by any of the boolean operators or use any of the field or date operators supported by MEDLINE.ZAG BUTNOT ZIG Searches are carried out by constructing suitable Entrez URLs for all possible pairwise combinations of the query terms. The URLs are then submitted via the web and the search results captured and analyzed by PDQ_MED. InPharmix Inc. ,PDQ_MED Input Page (partial) InPharmix Inc. ,PDQ_MED Algorithm II: Proximity Searching and Local Acronyms A refinement to the basic search strategy is to require a higher degree of "dependence" (closer proximity within the document) between two query terms. In "Proximity" searching, PDQ_MED examines all abstract containing two terms and determines if the terms co-occur in the same sentence. Sentence level proximity searching is not supported by MEDLINE. Acronyms make proximity
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Dr. Daoud Meerzaman: Computational Tools for Cancer Genome Analysis
On July 24, 2013, NCI's Dr. Daoud Meerzaman, Director of R&D/Section Head of Computational Genomics Research (CGR) at the Center for Biomedical Informatics and Information Technology (CBIIT), presented a lecture about Computational Tools for Cancer Genome Analysis, including Cancer Genome Work Bench, Bambino, Pathway Interaction Database, and PathOlogist.
Unsupervised Machine Learning
Dr. Ali Shojaie from the University of Washington presents a lecture titled "Unsupervised Machine Learning." View Slides https://drive.google.com/open?id=0B4IAKVDZz_JUR0V5c0w4MzNjLWM Lecture Abstract In this lecture, Dr. Shojaie will give an overview of unsupervised learning methods commonly used in biological applications, including dimension reduction (such as PCA and MDS), clustering and graphical modeling. Particular emphasis will be given to the generalizability of results from unsupervised learning methods, especially when these methods are used as part of the analysis pipeline in conjunction with supervised learning techniques. About the Speaker Ali Shojaie is an Associate Professor of Biostatistics and Adjunct Associate Professor of Statistics at the University of Washington. Originally trained in Industrial and Systems Engineering, he obtained his PhD in Statistics from the University of Michigan, while completing Masters degrees in Applied Mathematics and Human Genetics. Prior to joining the University of Washington in 2011, he participated in the program on Complex Networks as a Visiting Scholar at the Statistical and Mathematical Sciences Institute (SAMSI). Ali’s current research lies in the intersection of machine learning for high-dimensional data, statistical network analysis and applications in biology and social sciences and he teaches multiple regular and short courses on statistical machine learning and network analysis. Join our weekly meetings from your computer, tablet or smartphone. Visit our website to view our schedule and join our next live webinar! http://www.bigdatau.org/data-science-seminars
2014 Killian Lecture: Stephen J. Lippard, "Understanding and Improving Platinum Anticancer Drugs"
Lecture title: "Understanding and Improving Platinum Anticancer Drugs" Stephen Lippard, the Arthur Amos Noyes professor in the Department of Chemistry, was MIT’s James R. Killian Jr. Faculty Achievement Award winner for 2013–2014. Professor Lippard has spent his career studying the role of inorganic molecules, especially metal ions and their complexes, in critical processes of biological systems. He has made pioneering contributions in understanding the mechanism of the cancer drug cisplatin and in designing new variants to combat drug resistance and side effects. Tuesday, April 1, 2014 Huntington Hall (10-250)

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