32 results on '"Walsh, Jesse"'
Search Results
2. Tissue-specific gene expression and protein abundance patterns are associated with fractionation bias in maize
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Walsh, Jesse R, Woodhouse, Margaret R, Andorf, Carson M, and Sen, Taner Z
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Biotechnology ,Human Genome ,Generic health relevance ,Chromosome Mapping ,Evolution ,Molecular ,Gene Duplication ,Gene Expression ,Gene Expression Regulation ,Plant ,Gene Ontology ,Genes ,Plant ,Genome ,Plant ,Phylogeny ,Plant Proteins ,Pollen ,Polyploidy ,Zea mays ,Subgenome ,Gene expression ,Protein abundance ,Maize ,Functional divergence ,Microbiology ,Plant Biology ,Crop and Pasture Production ,Plant Biology & Botany ,Crop and pasture production ,Plant biology - Abstract
BACKGROUND:Maize experienced a whole-genome duplication event approximately 5 to 12 million years ago. Because this event occurred after speciation from sorghum, the pre-duplication subgenomes can be partially reconstructed by mapping syntenic regions to the sorghum chromosomes. During evolution, maize has had uneven gene loss between each ancient subgenome. Fractionation and divergence between these genomes continue today, constantly changing genetic make-up and phenotypes and influencing agronomic traits. RESULTS:Here we regenerate the subgenome reconstructions for the most recent maize reference genome assembly. Based on both expression and abundance data for homeologous gene pairs across multiple tissues, we observed functional divergence of genes across subgenomes. Although the genes in the larger maize subgenome are often expressing more highly than their homeologs in the smaller subgenome, we observed cases where homeolog expression dominance switches in different tissues. We demonstrate for the first time that protein abundances are higher in the larger subgenome, but they also show tissue-specific dominance, a pattern similar to RNA expression dominance. We also find that pollen expression is uniquely decoupled from protein abundance. CONCLUSION:Our study shows that the larger subgenome has a greater range of functional assignments and that there is a relative lack of overlap between the subgenomes in terms of gene functions than would be suggested by similar patterns of gene expression and protein abundance. Our study also revealed that some reactions are catalyzed uniquely by the larger and smaller subgenomes. The tissue-specific, nonequivalent expression-level dominance pattern observed here implies a change in regulatory control which favors differentiated selective pressure on the retained duplicates leading to eventual change in gene functions.
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- 2020
3. Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study
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Byeon, Seul Kee, Madugundu, Anil K, Garapati, Kishore, Ramarajan, Madan Gopal, Saraswat, Mayank, Kumar-M, Praveen, Hughes, Travis, Shah, Rameen, Patnaik, Mrinal M, Chia, Nicholas, Ashrafzadeh-Kian, Susan, Yao, Joseph D, Pritt, Bobbi S, Cattaneo, Roberto, Salama, Mohamed E, Zenka, Roman M, Kipp, Benjamin R, Grebe, Stefan K G, Singh, Ravinder J, Sadighi Akha, Amir A, Algeciras-Schimnich, Alicia, Dasari, Surendra, Olson, Janet E, Walsh, Jesse R, Venkatakrishnan, A J, Jenkinson, Garrett, O'Horo, John C, Badley, Andrew D, and Pandey, Akhilesh
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- 2022
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4. MaizeGDB 2018: the maize multi-genome genetics and genomics database
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Portwood, John L, Woodhouse, Margaret R, Cannon, Ethalinda K, Gardiner, Jack M, Harper, Lisa C, Schaeffer, Mary L, Walsh, Jesse R, Sen, Taner Z, Cho, Kyoung Tak, Schott, David A, Braun, Bremen L, Dietze, Miranda, Dunfee, Brittney, Elsik, Christine G, Manchanda, Nancy, Coe, Ed, Sachs, Marty, Stinard, Philip, Tolbert, Josh, Zimmerman, Shane, and Andorf, Carson M
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Biotechnology ,Human Genome ,Underpinning research ,1.5 Resources and infrastructure (underpinning) ,Generic health relevance ,Computational Biology ,Databases ,Genetic ,Gene Expression Regulation ,Plant ,Genetic Variation ,Genome ,Plant ,Genomics ,Information Storage and Retrieval ,Internet ,Polymorphism ,Single Nucleotide ,Proteomics ,User-Computer Interface ,Zea mays ,Environmental Sciences ,Information and Computing Sciences ,Developmental Biology ,Biological sciences ,Chemical sciences ,Environmental sciences - Abstract
Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.
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- 2019
5. The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize
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Walsh, Jesse R, Schaeffer, Mary L, Zhang, Peifen, Rhee, Seung Y, Dickerson, Julie A, and Sen, Taner Z
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Networking and Information Technology R&D (NITRD) ,HIV/AIDS ,Generic health relevance ,Computational Biology ,Molecular Sequence Annotation ,Plant Proteins ,Zea mays ,Metabolic pathway databases ,BioCyc ,CornCyc ,Database comparison ,MaizeCyc ,JavaCycO ,Biochemistry and Cell Biology ,Computer Software ,Other Medical and Health Sciences ,Bioinformatics ,Bioinformatics and computational biology ,Medical biochemistry and metabolomics - Abstract
BackgroundAs metabolic pathway resources become more commonly available, researchers have unprecedented access to information about their organism of interest. Despite efforts to ensure consistency between various resources, information content and quality can vary widely. Two maize metabolic pathway resources for the B73 inbred line, CornCyc 4.0 and MaizeCyc 2.2, are based on the same gene model set and were developed using Pathway Tools software. These resources differ in their initial enzymatic function assignments and in the extent of manual curation. We present an in-depth comparison between CornCyc and MaizeCyc to demonstrate the effect of initial computational enzymatic function assignments on the quality and content of metabolic pathway resources.ResultsThese two resources are different in their content. MaizeCyc contains GO annotations for over 21,000 genes that CornCyc is missing. CornCyc contains on average 1.6 transcripts per gene, while MaizeCyc contains almost no alternate splicing. MaizeCyc also does not match CornCyc's breadth in representing the metabolic domain; MaizeCyc has fewer compounds, reactions, and pathways than CornCyc. CornCyc's computational predictions are more accurate than those in MaizeCyc when compared to experimentally determined function assignments, demonstrating the relative strength of the enzymatic function assignment pipeline used to generate CornCyc.ConclusionsOur results show that the quality of initial enzymatic function assignments primarily determines the quality of the final metabolic pathway resource. Therefore, biologists should pay close attention to the methods and information sources used to develop a metabolic pathway resource to gauge the utility of using such functional assignments to construct hypotheses for experimental studies.
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- 2016
6. Multiomics single timepoint measurements to predict severe COVID-19 – Authors' reply
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Garapati, Kishore, primary, Byeon, Seul Kee, additional, Walsh, Jesse R, additional, Jenkinson, Garrett, additional, Cattaneo, Roberto, additional, O'Horo, John C, additional, Badley, Andrew D, additional, and Pandey, Akhilesh, additional
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- 2023
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7. P570: Getting it right on the first test: Machine learning plus genome-wide methylation profiling resolves equivocal cases of Beckwith-Wiedemann syndrome
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Theis, Jeanne, primary, Hardcastle, Jayson, additional, Vollenweider, Jason, additional, Jerde, Calvin, additional, Rumilla, Kandelaria, additional, Koellner, Christine, additional, Klee, Eric, additional, Walsh, Jesse, additional, Jenkinson, Garrett, additional, Balan, Jagadheshwar, additional, and Hasadsri, Linda, additional
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- 2023
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8. OP038: Novel whole methylome automated data analysis tool for investigation of unsolved diagnostic odyssey cases
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Koleilat, Alaa, primary, Aly, Yousof, additional, Hardcastle, Jayson, additional, Walsh, Jesse, additional, Jenkinson, Garret, additional, Koellner, Christine, additional, Rumilla, Kandelaria, additional, and Hasadsri, Linda, additional
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- 2022
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9. COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation
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Sankaranarayanan, Saranya, primary, Balan, Jagadheshwar, additional, Walsh, Jesse R, additional, Wu, Yanhong, additional, Minnich, Sara, additional, Piazza, Amy, additional, Osborne, Collin, additional, Oliver, Gavin R, additional, Lesko, Jessica, additional, Bates, Kathy L, additional, Khezeli, Kia, additional, Block, Darci R, additional, DiGuardo, Margaret, additional, Kreuter, Justin, additional, O’Horo, John C, additional, Kalantari, John, additional, Klee, Eric W, additional, Salama, Mohamed E, additional, Kipp, Benjamin, additional, Morice, William G, additional, and Jenkinson, Garrett, additional
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- 2021
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10. Corrigendum: Characterising an Alternative Murine Model of Diabetic Cardiomyopathy
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Tate, Mitchel, primary, Prakoso, Darnel, additional, Willis, Andrew M., additional, Peng, Cheng, additional, Deo, Minh, additional, Qin, Cheng Xue, additional, Walsh, Jesse L., additional, Nash, David M., additional, Cohen, Charles D., additional, Rofe, Alex K., additional, Sharma, Arpeeta, additional, Kiriazis, Helen, additional, Donner, Daniel G., additional, De Haan, Judy B., additional, Watson, Anna M. D., additional, De Blasio, Miles J., additional, and Ritchie, Rebecca H., additional
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- 2021
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11. COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation (Preprint)
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Sankaranarayanan, Saranya, primary, Balan, Jagadheshwar, additional, Walsh, Jesse R, additional, Wu, Yanhong, additional, Minnich, Sara, additional, Piazza, Amy, additional, Osborne, Collin, additional, Oliver, Gavin R, additional, Lesko, Jessica, additional, Bates, Kathy L, additional, Khezeli, Kia, additional, Block, Darci R, additional, DiGuardo, Margaret, additional, Kreuter, Justin, additional, O’Horo, John C, additional, Kalantari, John, additional, Klee, Eric W, additional, Salama, Mohamed E, additional, Kipp, Benjamin, additional, Morice, William G, additional, and Jenkinson, Garrett, additional
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- 2021
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12. MOESM4 of Tissue-specific gene expression and protein abundance patterns are associated with fractionation bias in maize
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Walsh, Jesse, Woodhouse, Margaret, Andorf, Carson, and Sen, Taner
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Additional file 4. Pathway diagram of reactions unique to each subgenome.
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- 2020
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13. Comprehensive molecular characterization of clear cell renal cell carcinoma
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Creighton, Chad J., Morgan, Margaret, Gunaratne, Preethi H., Wheeler, David A., Gibbs, Richard A., Robertson, Gordon A., Chu, Andy, Beroukhim, Rameen, Cibulskis, Kristian, Signoretti, Sabina, Hsin-Ta Wu, Fabio Vandin, Raphael, Benjamin J., Verhaak, Roel G. W., Tamboli, Pheroze, Torres-Garcia, Wandaliz, Akbani, Rehan, Weinstein, John N., Reuter, Victor, Hsieh, James J., Brannon, Rose A., Ari Hakimi, A., Jacobsen, Anders, Ciriello, Giovanni, Reva, Boris, Ricketts, Christopher J., Linehan, Marston W., Stuart, Joshua M., Rathmell, Kimryn W., Shen, Hui, Laird, Peter W., Muzny, Donna, Davis, Caleb, Xi, Liu, Chang, Kyle, Kakkar, Nipun, Treviño, Lisa R., Benton, Susan, Reid, Jeffrey G., Morton, Donna, Doddapaneni, Harsha, Han, Yi, Lewis, Lora, Dinh, Huyen, Kovar, Christie, Zhu, Yiming, Santibanez, Jireh, Wang, Min, Hale, Walker, Kalra, Divya, Getz, Gad, Lawrence, Michael S., Sougnez, Carrie, Carter, Scott L., Sivachenko, Andrey, Lichtenstein, Lee, Stewart, Chip, Voet, Doug, Fisher, Sheila, Gabriel, Stacey B., Lander, Eric, Schumacher, Steve E., Tabak, Barbara, Saksena, Gordon, Onofrio, Robert C., Cherniack, Andrew D., Gentry, Jeff, Ardlie, Kristin, Meyerson, Matthew, Chun, Hye-Jung E., Mungall, Andrew J., Sipahimalani, Payal, Stoll, Dominik, Ally, Adrian, Balasundaram, Miruna, Butterfield, Yaron S. N., Carlsen, Rebecca, Carter, Candace, Chuah, Eric, Coope, Robin J. N., Dhalla, Noreen, Gorski, Sharon, Guin, Ranabir, Hirst, Carrie, Hirst, Martin, Holt, Robert A., Lebovitz, Chandra, Lee, Darlene, Li, Haiyan I., Mayo, Michael, Moore, Richard A., Pleasance, Erin, Plettner, Patrick, Schein, Jacqueline E., Shafiei, Arash, Slobodan, Jared R., Tam, Angela, Thiessen, Nina, Varhol, Richard J., Wye, Natasja, Zhao, Yongjun, Birol, Inanc, Jones, Steven J. M., Marra, Marco A., Auman, Todd J., Tan, Donghui, Jones, Corbin D., Hoadley, Katherine A., Mieczkowski, Piotr A., Mose, Lisle E., Jefferys, Stuart R., Topal, Michael D., Liquori, Christina, Turman, Yidi J., Shi, Yan, Waring, Scot, Buda, Elizabeth, Walsh, Jesse, Wu, Junyuan, Bodenheimer, Tom, Hoyle, Alan P., Simons, Janae V., Soloway, Mathew G., Balu, Saianand, Parker, Joel S., Hayes, Neil D., Perou, Charles M., Kucherlapati, Raju, Park, Peter, Triche, Timothy, Jr, Weisenberger, Daniel J., Lai, Phillip H., Bootwalla, Moiz S., Maglinte, Dennis T., Mahurkar, Swapna, Berman, Benjamin P., Van Den Berg, David J., Cope, Leslie, Baylin, Stephen B., Noble, Michael S., DiCara, Daniel, Zhang, Hailei, Cho, Juok, Heiman, David I., Gehlenborg, Nils, Mallard, William, Lin, Pei, Frazer, Scott, Stojanov, Petar, Liu, Yingchun, Zhou, Lihua, Kim, Jaegil, Chin, Lynda, Vandin, Fabio, Wu, Hsin-Ta, Benz, Christopher, Yau, Christina, Reynolds, Sheila M., Shmulevich, Ilya, Verhaak, Roel G.W., Vegesna, Rahul, Kim, Hoon, Zhang, Wei, Cogdell, David, Jonasch, Eric, Ding, Zhiyong, Lu, Yiling, Zhang, Nianxiang, Unruh, Anna K., Casasent, Tod D., Wakefield, Chris, Tsavachidou, Dimitra, Mills, Gordon B., Schultz, Nikolaus, Antipin, Yevgeniy, Gao, Jianjiong, Cerami, Ethan, Gross, Benjamin, Aksoy, Arman B., Sinha, Rileen, Weinhold, Nils, Sumer, Onur S., Taylor, Barry S., Shen, Ronglai, Ostrovnaya, Irina, Berger, Michael F., Ladanyi, Marc, Sander, Chris, Fei, Suzanne S., Stout, Andrew, Spellman, Paul T., Rubin, Daniel L., Liu, Tiffany T., Ng, Sam, Paull, Evan O., Carlin, Daniel, Goldstein, Theodore, Waltman, Peter, Ellrott, Kyle, Zhu, Jing, Haussler, David, Xiao, Weimin, Shelton, Candace, Gardner, Johanna, Penny, Robert, Sherman, Mark, Mallery, David, Morris, Scott, Paulauskis, Joseph, Burnett, Ken, Shelton, Troy, Kaelin, William G., Choueiri, Toni, Atkins, Michael B., Curley, Erin, Tickoo, Satish, Thorne, Leigh, Boice, Lori, Huang, Mei, Fisher, Jennifer C., Vocke, Cathy D., Peterson, James, Worrell, Robert, Merino, Maria J., Schmidt, Laura S., Czerniak, Bogdan A., Aldape, Kenneth D., Wood, Christopher G., Boyd, Jeff, Weaver, JoEllen, Iacocca, Mary V., Petrelli, Nicholas, Witkin, Gary, Brown, Jennifer, Czerwinski, Christine, Huelsenbeck-Dill, Lori, Rabeno, Brenda, Myers, Jerome, Morrison, Carl, Bergsten, Julie, Eckman, John, Harr, Jodi, Smith, Christine, Tucker, Kelinda, Zach, Leigh Anne, Bshara, Wiam, Gaudioso, Carmelo, Dhir, Rajiv, Maranchie, Jodi, Nelson, Joel, Parwani, Anil, Potapova, Olga, Fedosenko, Konstantin, Cheville, John C., Thompson, Houston R., Mosquera, Juan M., Rubin, Mark A., Blute, Michael L., Pihl, Todd, Jensen, Mark, Sfeir, Robert, Kahn, Ari, Chu, Anna, Kothiyal, Prachi, Snyder, Eric, Pontius, Joan, Ayala, Brenda, Backus, Mark, Walton, Jessica, Baboud, Julien, Berton, Dominique, Nicholls, Matthew, Srinivasan, Deepak, Raman, Rohini, Girshik, Stanley, Kigonya, Peter, Alonso, Shelley, Sanbhadti, Rashmi, Barletta, Sean, Pot, David, Sheth, Margi, Demchok, John A., Davidsen, Tanja, Wang, Zhining, Yang, Liming, Tarnuzzer, Roy W., Zhang, Jiashan, Eley, Greg, Ferguson, Martin L., Mills Shaw, Kenna R., Guyer, Mark S., Ozenberger, Bradley A., and Sofia, Heidi J.
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- 2013
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14. Comprehensive genomic characterization of squamous cell lung cancers
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Hammerman, Peter S., Lawrence, Michael S., Voet, Douglas, Jing, Rui, Cibulskis, Kristian, Sivachenko, Andrey, Stojanov, Petar, McKenna, Aaron, Lander, Eric S., Gabriel, Stacey, Getz, Gad, Sougnez, Carrie, Imielinski, Marcin, Helman, Elena, Hernandez, Bryan, Pho, Nam H., Meyerson, Matthew, Chu, Andy, Chun, Hye-Jung E., Mungall, Andrew J., Pleasance, Erin, Gordon Robertson, A., Sipahimalani, Payal, Stoll, Dominik, Balasundaram, Miruna, Birol, Inanc, Butterfield, Yaron S. N., Chuah, Eric, Coope, Robin J. N., Corbett, Richard, Dhalla, Noreen, Guin, Ranabir, He, An, Hirst, Carrie, Hirst, Martin, Holt, Robert A., Lee, Darlene, Li, Haiyan I., Mayo, Michael, Moore, Richard A., Mungall, Karen, Ming Nip, Ka, Olshen, Adam, Schein, Jacqueline E., Slobodan, Jared R., Tam, Angela, Thiessen, Nina, Varhol, Richard, Zeng, Thomas, Zhao, Yongjun, Jones, Steven J. M., Marra, Marco A., Saksena, Gordon, Cherniack, Andrew D., Schumacher, Stephen E., Tabak, Barbara, Carter, Scott L., Nguyen, Huy, Onofrio, Robert C., Crenshaw, Andrew, Ardlie, Kristin, Beroukhim, Rameen, Winckler, Wendy, Protopopov, Alexei, Zhang, Jianhua, Hadjipanayis, Angela, Lee, Semin, Xi, Ruibin, Yang, Lixing, Ren, Xiaojia, Zhang, Hailei, Shukla, Sachet, Chen, Peng-Chieh, Haseley, Psalm, Lee, Eunjung, Chin, Lynda, Park, Peter J., Kucherlapati, Raju, Socci, Nicholas D., Liang, Yupu, Schultz, Nikolaus, Borsu, Laetitia, Lash, Alex E., Viale, Agnes, Sander, Chris, Ladanyi, Marc, Todd Auman, J., Hoadley, Katherine A., Wilkerson, Matthew D., Shi, Yan, Liquori, Christina, Meng, Shaowu, Li, Ling, Turman, Yidi J., Topal, Michael D., Tan, Donghui, Waring, Scot, Buda, Elizabeth, Walsh, Jesse, Jones, Corbin D., Mieczkowski, Piotr A., Singh, Darshan, Wu, Junyuan, Gulabani, Anisha, Dolina, Peter, Bodenheimer, Tom, Hoyle, Alan P., Simons, Janae V., Soloway, Matthew G., Mose, Lisle E., Jefferys, Stuart R., Balu, Saianand, OʼConnor, Brian D., Prins, Jan F., Liu, Jinze, Chiang, Derek Y., Neil Hayes, D., Perou, Charles M., Cope, Leslie, Danilova, Ludmila, Weisenberger, Daniel J., Maglinte, Dennis T., Pan, Fei, Van Den Berg, David J., Triche, Timothy, Jr, Herman, James G., Baylin, Stephen B., Laird, Peter W., Noble, Michael, Voet, Doug, Gehlenborg, Nils, DiCara, Daniel, Zhang, Jinhua, Wu, Chang-Jiun, Yingchun Liu, Spring, Zou, Lihua, Lin, Pei, Cho, Juok, Nazaire, Marc-Danie, Robinson, Jim, Thorvaldsdottir, Helga, Mesirov, Jill, Sinha, Rileen, Ciriello, Giovanni, Cerami, Ethan, Gross, Benjamin, Jacobsen, Anders, Gao, Jianjiong, Arman Aksoy, B., Weinhold, Nils, Ramirez, Ricardo, Taylor, Barry S., Antipin, Yevgeniy, Reva, Boris, Shen, Ronglai, Mo, Qianxing, Seshan, Venkatraman, Paik, Paul K., Akbani, Rehan, Zhang, Nianxiang, Broom, Bradley M., Casasent, Tod, Unruh, Anna, Wakefield, Chris, Craig Cason, R., Baggerly, Keith A., Weinstein, John N., Haussler, David, Benz, Christopher C., Stuart, Joshua M., Zhu, Jingchun, Szeto, Christopher, Scott, Gary K., Yau, Christina, Ng, Sam, Goldstein, Ted, Waltman, Peter, Sokolov, Artem, Ellrott, Kyle, Collisson, Eric A., Zerbino, Daniel, Wilks, Christopher, Ma, Singer, Craft, Brian, Du, Ying, Cabanski, Christopher, Walter, Vonn, Marron, J. S., Liu, Yufeng, Wang, Kai, Creighton, Chad J., Zhang, Yiqun, Travis, William D., Rekhtman, Natasha, Yi, Joanne, Aubry, Marie C., Cheney, Richard, Dacic, Sanja, Flieder, Douglas, Funkhouser, William, Illei, Peter, Myers, Jerome, Tsao, Ming-Sound, Penny, Robert, Mallery, David, Shelton, Troy, Hatfield, Martha, Morris, Scott, Yena, Peggy, Shelton, Candace, Sherman, Mark, Paulauskis, Joseph, Govindan, Ramaswamy, Azodo, Ijeoma, Beer, David, Bose, Ron, Byers, Lauren A., Carbone, David, Chang, Li-Wei, Chiang, Derek, Chun, Elizabeth, Collisson, Eric, Ding, Li, Heymach, John, Ida, Cristiane, Johnson, Bruce, Jurisica, Igor, Kaufman, Jacob, Kosari, Farhad, Kwiatkowski, David, Maher, Christopher A., Mungall, Andy, Pao, William, Peifer, Martin, Robertson, Gordon, Rusch, Valerie, Siegfried, Jill, Stuart, Joshua, Thomas, Roman K., Tomaszek, Sandra, Vaske, Charles, Weisenberger, Daniel, Wigle, Dennis A., Yang, Ping, John Zhang, Jianjua, Jensen, Mark A., Sfeir, Robert, Kahn, Ari B., Chu, Anna L., Kothiyal, Prachi, Wang, Zhining, Snyder, Eric E., Pontius, Joan, Pihl, Todd D., Ayala, Brenda, Backus, Mark, Walton, Jessica, Baboud, Julien, Berton, Dominique L., Nicholls, Matthew C., Srinivasan, Deepak, Raman, Rohini, Girshik, Stanley, Kigonya, Peter A., Alonso, Shelley, Sanbhadti, Rashmi N., Barletta, Sean P., Greene, John M., Pot, David A., Bandarchi-Chamkhaleh, Bizhan, Boyd, Jeff, Weaver, JoEllen, Azodo, Ijeoma A., Tomaszek, Sandra C., Christine Aubry, Marie, Ida, Christiane M., Brock, Malcolm V., Rogers, Kristen, Rutledge, Marian, Brown, Travis, Lee, Beverly, Shin, James, Trusty, Dante, Dhir, Rajiv, Siegfried, Jill M., Potapova, Olga, Fedosenko, Konstantin V., Nemirovich-Danchenko, Elena, Zakowski, Maureen, Iacocca, Mary V., Brown, Jennifer, Rabeno, Brenda, Czerwinski, Christine, Petrelli, Nicholas, Fan, Zhen, Todaro, Nicole, Eckman, John, Rathmell, Kimryn W., Thorne, Leigh B., Huang, Mei, Boice, Lori, Hill, Ashley, Curley, Erin, Morrison, Carl, Gaudioso, Carmelo, Bartlett, John M. S., Kodeeswaran, Sugy, Zanke, Brent, Sekhon, Harman, David, Kerstin, Juhl, Hartmut, Van Le, Xuan, Kohl, Bernard, Thorp, Richard, Viet Tien, Nguyen, Van Bang, Nguyen, Sussman, Howard, Duc Phu, Bui, Hajek, Richard, Phi Hung, Nguyen, Khan, Khurram Z., Muley, Thomas, Mills Shaw, Kenna R., Sheth, Margi, Yang, Liming, Buetow, Ken, Davidsen, Tanja, Demchok, John A., Eley, Greg, Ferguson, Martin, Dillon, Laura A. L., Schaefer, Carl, Guyer, Mark S., Ozenberger, Bradley A., Palchik, Jacqueline D., Peterson, Jane, Sofia, Heidi J., Thomson, Elizabeth, and Johnson, Bruce E.
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- 2012
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15. Comprehensive molecular characterization of human colon and rectal cancer
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Muzny, Donna M., Bainbridge, Matthew N., Chang, Kyle, Dinh, Huyen H., Drummond, Jennifer A., Fowler, Gerald, Kovar, Christie L., Lewis, Lora R., Morgan, Margaret B., Newsham, Irene F., Reid, Jeffrey G., Santibanez, Jireh, Shinbrot, Eve, Trevino, Lisa R., Wu, Yuan-Qing, Wang, Min, Gunaratne, Preethi, Donehower, Lawrence A., Creighton, Chad J., Wheeler, David A., Gibbs, Richard A., Lawrence, Michael S., Voet, Douglas, Jing, Rui, Cibulskis, Kristian, Sivachenko, Andrey, Stojanov, Petar, McKenna, Aaron, Lander, Eric S., Gabriel, Stacey, Getz, Gad, Ding, Li, Fulton, Robert S., Koboldt, Daniel C., Wylie, Todd, Walker, Jason, Dooling, David J., Fulton, Lucinda, Delehaunty, Kim D., Fronick, Catrina C., Demeter, Ryan, Mardis, Elaine R., Wilson, Richard K., Chu, Andy, Chun, Hye-Jung E., Mungall, Andrew J., Pleasance, Erin, Robertson, Gordon A., Stoll, Dominik, Balasundaram, Miruna, Birol, Inanc, Butterfield, Yaron S. N., Chuah, Eric, Coope, Robin J. N., Dhalla, Noreen, Guin, Ranabir, Hirst, Carrie, Hirst, Martin, Holt, Robert A., Lee, Darlene, Li, Haiyan I., Mayo, Michael, Moore, Richard A., Schein, Jacqueline E., Slobodan, Jared R., Tam, Angela, Thiessen, Nina, Varhol, Richard, Zeng, Thomas, Zhao, Yongjun, Jones, Steven J. M., Marra, Marco A., Bass, Adam J., Ramos, Alex H., Saksena, Gordon, Cherniack, Andrew D., Schumacher, Stephen E., Tabak, Barbara, Carter, Scott L., Pho, Nam H., Nguyen, Huy, Onofrio, Robert C., Crenshaw, Andrew, Ardlie, Kristin, Beroukhim, Rameen, Winckler, Wendy, Meyerson, Matthew, Protopopov, Alexei, Zhang, Juinhua, Hadjipanayis, Angela, Lee, Eunjung, Xi, Ruibin, Yang, Lixing, Ren, Xiaojia, Zhang, Hailei, Sathiamoorthy, Narayanan, Shukla, Sachet, Chen, Peng-Chieh, Haseley, Psalm, Xiao, Yonghong, Lee, Semin, Seidman, Jonathan, Chin, Lynda, Park, Peter J., Kucherlapati, Raju, Auman, Todd J., Hoadley, Katherine A., Du, Ying, Wilkerson, Matthew D., Shi, Yan, Liquori, Christina, Meng, Shaowu, Li, Ling, Turman, Yidi J., Topal, Michael D., Tan, Donghui, Waring, Scot, Buda, Elizabeth, Walsh, Jesse, Jones, Corbin D., Mieczkowski, Piotr A., Singh, Darshan, Wu, Junyuan, Gulabani, Anisha, Dolina, Peter, Bodenheimer, Tom, Hoyle, Alan P., Simons, Janae V., Soloway, Matthew, Mose, Lisle E., Jefferys, Stuart R., Balu, Saianand, O’Connor, Brian D., Prins, Jan F., Chiang, Derek Y., Hayes, Neil D., Perou, Charles M., Hinoue, Toshinori, Weisenberger, Daniel J., Maglinte, Dennis T., Pan, Fei, Berman, Benjamin P., Van Den Berg, David J., Shen, Hui, Triche, Timothy, Jr, Baylin, Stephen B., Laird, Peter W., Noble, Michael, Voet, Doug, Gehlenborg, Nils, DiCara, Daniel, Wu, Chang-Jiun, Yingchun Liu, Spring, Zhou, Lihua, Lin, Pei, Park, Richard W., Nazaire, Marc-Danie, Robinson, Jim, Thorvaldsdottir, Helga, Mesirov, Jill, Thorsson, Vesteinn, Reynolds, Sheila M., Bernard, Brady, Kreisberg, Richard, Lin, Jake, Iype, Lisa, Bressler, Ryan, Erkkilä, Timo, Gundapuneni, Madhumati, Liu, Yuexin, Norberg, Adam, Robinson, Tom, Yang, Da, Zhang, Wei, Shmulevich, Ilya, de Ronde, Jorma J., Schultz, Nikolaus, Cerami, Ethan, Ciriello, Giovanni, Goldberg, Arthur P., Gross, Benjamin, Jacobsen, Anders, Gao, Jianjiong, Kaczkowski, Bogumil, Sinha, Rileen, Aksoy, Arman B., Antipin, Yevgeniy, Reva, Boris, Shen, Ronglai, Taylor, Barry S., Chan, Timothy A., Ladanyi, Marc, Sander, Chris, Akbani, Rehan, Zhang, Nianxiang, Broom, Bradley M., Casasent, Tod, Unruh, Anna, Wakefield, Chris, Hamilton, Stanley R., Cason, Craig R., Baggerly, Keith A., Weinstein, John N., Haussler, David, Benz, Christopher C., Stuart, Joshua M., Benz, Stephen C., Sanborn, Zachary J., Vaske, Charles J., Zhu, Jingchun, Szeto, Christopher, Scott, Gary K., Yau, Christina, Ng, Sam, Goldstein, Ted, Ellrott, Kyle, Collisson, Eric, Cozen, Aaron E., Zerbino, Daniel, Wilks, Christopher, Craft, Brian, Spellman, Paul, Penny, Robert, Shelton, Troy, Hatfield, Martha, Morris, Scott, Yena, Peggy, Shelton, Candace, Sherman, Mark, Paulauskis, Joseph, Gastier-Foster, Julie M., Bowen, Jay, Ramirez, Nilsa C., Black, Aaron, Pyatt, Robert, Wise, Lisa, White, Peter, Bertagnolli, Monica, Brown, Jen, Chu, Gerald C., Czerwinski, Christine, Denstman, Fred, Dhir, Rajiv, Dörner, Arnulf, Fuchs, Charles S., Guillem, Jose G., Iacocca, Mary, Juhl, Hartmut, Kaufman, Andrew, Kohl, Bernard, III, Van Le, Xuan, Mariano, Maria C., Medina, Elizabeth N., Meyers, Michael, Nash, Garrett M., Paty, Phillip B., Petrelli, Nicholas, Rabeno, Brenda, Richards, William G., Solit, David, Swanson, Pat, Temple, Larissa, Tepper, Joel E., Thorp, Richard, Vakiani, Efsevia, Weiser, Martin R., Willis, Joseph E., Witkin, Gary, Zeng, Zhaoshi, Zinner, Michael J., Zornig, Carsten, Jensen, Mark A., Sfeir, Robert, Kahn, Ari B., Chu, Anna L., Kothiyal, Prachi, Wang, Zhining, Snyder, Eric E., Pontius, Joan, Pihl, Todd D., Ayala, Brenda, Backus, Mark, Walton, Jessica, Whitmore, Jon, Baboud, Julien, Berton, Dominique L., Nicholls, Matthew C., Srinivasan, Deepak, Raman, Rohini, Girshik, Stanley, Kigonya, Peter A., Alonso, Shelley, Sanbhadti, Rashmi N., Barletta, Sean P., Greene, John M., Pot, David A., Mills Shaw, Kenna R., Dillon, Laura A. L., Buetow, Ken, Davidsen, Tanja, Demchok, John A., Eley, Greg, Ferguson, Martin, Fielding, Peter, Schaefer, Carl, Sheth, Margi, Yang, Liming, Guyer, Mark S., Ozenberger, Bradley A., Palchik, Jacqueline D., Peterson, Jane, Sofia, Heidi J., and Thomson, Elizabeth
- Published
- 2012
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16. Defining the Progression of Diabetic Cardiomyopathy in a Mouse Model of Type 1 Diabetes
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De Blasio, Miles J., primary, Huynh, Nguyen, additional, Deo, Minh, additional, Dubrana, Leslie E., additional, Walsh, Jesse, additional, Willis, Andrew, additional, Prakoso, Darnel, additional, Kiriazis, Helen, additional, Donner, Daniel G., additional, Chatham, John C., additional, and Ritchie, Rebecca H., additional
- Published
- 2020
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17. The Novel Small-molecule Annexin-A1 Mimetic, Compound 17b, Elicits Vasoprotective Actions in Streptozotocin-induced Diabetic Mice
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Marshall, Sarah A, primary, Qin, Cheng Xue, additional, Jelinic, Maria, additional, O’Sullivan, Kelly, additional, Deo, Minh, additional, Walsh, Jesse, additional, Li, Mandy, additional, Parry, Laura J, additional, Ritchie, Rebecca H., additional, and Leo, Chen Huei, additional
- Published
- 2020
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18. Annexin‐A1 deficiency exacerbates pathological remodelling of the mesenteric vasculature in insulin‐resistant, but not insulin‐deficient, mice
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Jelinic, Maria, primary, Kahlberg, Nicola, additional, Leo, Chen Huei, additional, Ng, Hooi Hooi, additional, Rosli, Sarah, additional, Deo, Minh, additional, Li, Mandy, additional, Finlayson, Siobhan, additional, Walsh, Jesse, additional, Parry, Laura J., additional, Ritchie, Rebecca H., additional, and Qin, Cheng Xue, additional
- Published
- 2020
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19. Characterising an Alternative Murine Model of Diabetic Cardiomyopathy
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Tate, Mitchel, primary, Prakoso, Darnel, additional, Willis, Andrew M., additional, Peng, Cheng, additional, Deo, Minh, additional, Qin, Cheng Xue, additional, Walsh, Jesse L., additional, Nash, David M., additional, Cohen, Charles D., additional, Rofe, Alex K., additional, Sharma, Arpeeta, additional, Kiriazis, Helen, additional, Donner, Daniel G., additional, De Haan, Judy B., additional, Watson, Anna M. D., additional, De Blasio, Miles J., additional, and Ritchie, Rebecca H., additional
- Published
- 2019
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- View/download PDF
20. Cardioprotective Actions of the Annexin-A1 N-Terminal Peptide, Ac2-26, Against Myocardial Infarction
- Author
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Qin, Cheng Xue, primary, Rosli, Sarah, additional, Deo, Minh, additional, Cao, Nga, additional, Walsh, Jesse, additional, Tate, Mitchel, additional, Alexander, Amy E., additional, Donner, Daniel, additional, Horlock, Duncan, additional, Li, Renming, additional, Kiriazis, Helen, additional, Lee, Man K. S., additional, Bourke, Jane E., additional, Yang, Yuan, additional, Murphy, Andrew J., additional, Du, Xiao-Jun, additional, Gao, Xiao Ming, additional, and Ritchie, Rebecca H., additional
- Published
- 2019
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21. MaizeGDB 2018: the maize multi-genome genetics and genomics database
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Portwood, John L, primary, Woodhouse, Margaret R, additional, Cannon, Ethalinda K, additional, Gardiner, Jack M, additional, Harper, Lisa C, additional, Schaeffer, Mary L, additional, Walsh, Jesse R, additional, Sen, Taner Z, additional, Cho, Kyoung Tak, additional, Schott, David A, additional, Braun, Bremen L, additional, Dietze, Miranda, additional, Dunfee, Brittney, additional, Elsik, Christine G, additional, Manchanda, Nancy, additional, Coe, Ed, additional, Sachs, Marty, additional, Stinard, Philip, additional, Tolbert, Josh, additional, Zimmerman, Shane, additional, and Andorf, Carson M, additional
- Published
- 2018
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22. Association between fish oil consumption and the incidence of mental health issues among active duty military personnel
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Walsh, Jesse-LaRou, Shen, Yu-Chu, Hartmann, Latika, and Graduate School of Business and Public Policy (GSBPP)
- Subjects
suicide ideation ,depression ,Mental health ,PTSD ,omega-3 ,fish oil ,military - Abstract
There is increasing attention from the military to understand the potential benefit of enhancing service members’ meals with omega-3 nutrients to improve their overall mental health. This research warrants attention due to the increase in the number of military members returning from wars with mental health issues such as PTSD and depression, and an increasing number of military members who are medically discharged for these mental health issues. Using the 2011 DOD Health Related Behaviors Survey of Active Duty Military Personnel, we analyze the association between fish oil consumption and mental health outcomes. This analysis focuses on three outcomes that capture a service members’ state of mental health (depression, post-traumatic stress [PTS], suicide ideation), and whether service members sought mental health therapy within the past 12 months. We estimated logistic regression models where the key independent variables were various levels of fish oil use (none [reference group], light, moderate, and daily use). For each outcome, we estimated five models that include control variables in the following categories: demographics, combat exposure, lifestyle—activities, lifestyle—nutrition, and lifestyle-stress. In addition, we estimated a model on the Navy-only population to examine whether Navy personnel might exhibit different patterns than DOD as a whole. We also explore whether there are gender differences in the association between fish oil usage and mental health outcomes. The survey did not show higher fish oil consumption to be associated with lower incidences of depression, post-traumatic stress, or suicide ideation among all the services. Navy-only analysis has similar findings, except that one of the models indicated that light fish oil use lowered the likelihood of Navy personnel experiencing high PTS in the past 30 days. Our recommendations are to analyze the survey data across all years it has been given to see if there are trends, encourage the military to place more emphasis on lifestyle choices pertaining to health and nutrition, and urge the military to help service members with stress and anxiety. http://archive.org/details/associationbetwe1094548490 Lieutenant Commander, United States Navy Approved for public release; distribution is unlimited.
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- 2016
23. Additional file 1 of The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize
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Walsh, Jesse, Schaeffer, Mary, Peifen Zhang, Rhee, Seung, Dickerson, Julie, and Sen, Taner
- Abstract
Method for extracting and comparing data from CornCyc and MaizeCyc. This document describes how CornCyc and MaizeCyc data were selected, extracted, and filtered and/or modified before comparison. The five major data types, Genes, Proteins, Compounds, Pathways, and Reactions were each handled in unique ways. (PDF 342 kb)
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- 2016
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24. Cardioprotective Actions of the Annexin-A1 N-Terminal Peptide, Ac2-26, Against Myocardial Infarction.
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Qin, Cheng Xue, Rosli, Sarah, Deo, Minh, Cao, Nga, Walsh, Jesse, Tate, Mitchel, Alexander, Amy E., Donner, Daniel, Horlock, Duncan, Li, Renming, Kiriazis, Helen, Lee, Man K. S., Bourke, Jane E., Yang, Yuan, Murphy, Andrew J., Du, Xiao-Jun, Gao, Xiao Ming, and Ritchie, Rebecca H.
- Subjects
HEART fibrosis ,PEPTIDE receptors ,REPERFUSION ,MYOCARDIAL infarction ,CORONARY disease - Abstract
The anti-inflammatory, pro-resolving annexin-A1 protein acts as an endogenous brake against exaggerated cardiac necrosis, inflammation, and fibrosis following myocardial infarction (MI) in vivo. Little is known, however, regarding the cardioprotective actions of the N-terminal-derived peptide of annexin A1, Ac
2-26 , particularly beyond its anti-necrotic actions in the first few hours after an ischemic insult. In this study, we tested the hypothesis that exogenous Ac2-26 limits cardiac injury in vitro and in vivo. Firstly, we demonstrated that Ac2-26 limits cardiomyocyte death both in vitro and in mice subjected to ischemia-reperfusion (I-R) injury in vivo (Ac2-26, 1 mg/kg, i.v. just prior to post-ischemic reperfusion). Further, Ac2-26 (1 mg/kg i.v.) reduced cardiac inflammation (after 48 h reperfusion), as well as both cardiac fibrosis and apoptosis (after 7-days reperfusion). Lastly, we investigated whether Ac2-26 preserved cardiac function after MI. Ac2-26 (1 mg/kg/day s.c., osmotic pump) delayed early cardiac dysfunction 1 week post MI, but elicited no further improvement 4 weeks after MI. Taken together, our data demonstrate the first evidence that Ac2-26 not only preserves cardiomyocyte survival in vitro , but also offers cardioprotection beyond the first few hours after an ischemic insult in vivo. Annexin-A1 mimetics thus represent a potential new therapy to improve cardiac outcomes after MI. [ABSTRACT FROM AUTHOR]- Published
- 2019
- Full Text
- View/download PDF
25. A computational platform to maintain and migrate manual functional annotations for BioCyc databases
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Walsh, Jesse R, primary, Sen, Taner Z, additional, and Dickerson, Julie A, additional
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- 2014
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26. Semi-automated Constraint-based Metabolic Model Generation
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Walsh, Jesse R., primary and Dickerson, Julie A., additional
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- 2013
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27. Degeneracy analysis of T cell receptors in mice. (160.17)
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Buntzman, Adam, primary, Vincent, Benjamin, additional, Krovi, S.Harsha, additional, Steele, Shaun, additional, Walsh, Jesse, additional, Kepler, Thomas, additional, and Frelinger, Jeffrey, additional
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- 2011
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28. Damage-induced localized hypermutability
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Burch, Lauranell H., primary, Yang, Yong, additional, Sterling, Joan F., additional, Roberts, Steven A., additional, Chao, Frank G., additional, Xu, Hong, additional, Zhang, Leilei, additional, Walsh, Jesse, additional, Resnick, Michael A., additional, Mieczkowski, Piotr A., additional, and Gordenin, Dmitry A., additional
- Published
- 2011
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- View/download PDF
29. Large Scale T cell receptor repertoire sequencing of murine CD8+ T cells. (144.5)
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Buntzman, Adam, primary, Vincent, Benjamin, additional, Steele, Shaun, additional, Walsh, Jesse, additional, Kepler, Thomas, additional, and Frelinger, Jeffrey, additional
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- 2010
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30. Computational methods for integrated analysis of omics and pathway data
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Walsh, Jesse R., primary
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31. COVID-19 mortality prediction from deep learning in a large multistate EHR and LIS dataset: algorithm development and validation.
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Sankaranarayanan, Saranya, Balan, Jagadheshwar, Walsh, Jesse R, Wu, Yanhong, Minnich, Sara, Piazza, Amy, Osborne, Collin, Oliver, Gavin R, Lesko, Jessica, Bates, Kathy L, Khezeli, Kia, Block, Darci R, DiGuardo, Margaret, Kreuter, Justin, O'Horo, John C, Kalantari, John, Klee, Eric W, Salama, Mohamed E, Kipp, Benjamin, and Morice, William G
- Abstract
Background: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the lab tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a dataset of this nature enable patient stratification and provide methods to guide clinical treatment.Objective: Here we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population.Methods: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system (LIS) and electronic health record (EHR) COVID-19 datasets in the published literature, which included 11,807 patients with residence in 41 states, treated at medical sites across five states in three time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach using AutoGluon, and various recurrent neural network (RNN) architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the RNNs utilized the tensorflow keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test.Results: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938±0.004 AUROC. The model retained strong performance when reducing the follow-up time to 12 hours (0.916±0.005 AUROC), and leave-one-out feature importance analysis indicated the most independently valuable features were: age, Charlson score, minimum oxygen saturation, fibrinogen and serum iron level. In the prospective testing cohort this model provides an AUROC of 0.901 and statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive: 95% CI [0.043,0.106]).Conclusions: Our deep learning approach using GRU-D provides an alert system to flag mortality on COVID-19 positive patients, using clinical covariates and lab values within a 72-hour window after the first positive nucleic acid test.Clinicaltrial: [ABSTRACT FROM AUTHOR]- Published
- 2021
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32. Cardioprotective Actions of the Annexin-A1 N-Terminal Peptide, Ac 2-26 , Against Myocardial Infarction.
- Author
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Qin CX, Rosli S, Deo M, Cao N, Walsh J, Tate M, Alexander AE, Donner D, Horlock D, Li R, Kiriazis H, Lee MKS, Bourke JE, Yang Y, Murphy AJ, Du XJ, Gao XM, and Ritchie RH
- Abstract
The anti-inflammatory, pro-resolving annexin-A1 protein acts as an endogenous brake against exaggerated cardiac necrosis, inflammation, and fibrosis following myocardial infarction (MI) in vivo . Little is known, however, regarding the cardioprotective actions of the N-terminal-derived peptide of annexin A1, Ac
2-26 , particularly beyond its anti-necrotic actions in the first few hours after an ischemic insult. In this study, we tested the hypothesis that exogenous Ac2-26 limits cardiac injury in vitro and in vivo. Firstly, we demonstrated that Ac2-26 limits cardiomyocyte death both in vitro and in mice subjected to ischemia-reperfusion (I-R) injury in vivo (Ac2-26, 1 mg/kg, i.v. just prior to post-ischemic reperfusion). Further, Ac2-26 (1 mg/kg i.v.) reduced cardiac inflammation (after 48 h reperfusion), as well as both cardiac fibrosis and apoptosis (after 7-days reperfusion). Lastly, we investigated whether Ac2-26 preserved cardiac function after MI. Ac2-26 (1 mg/kg/day s.c., osmotic pump) delayed early cardiac dysfunction 1 week post MI, but elicited no further improvement 4 weeks after MI. Taken together, our data demonstrate the first evidence that Ac2-26 not only preserves cardiomyocyte survival in vitro , but also offers cardioprotection beyond the first few hours after an ischemic insult in vivo . Annexin-A1 mimetics thus represent a potential new therapy to improve cardiac outcomes after MI.- Published
- 2019
- Full Text
- View/download PDF
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