{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,5]],"date-time":"2024-05-05T08:40:13Z","timestamp":1714898413451},"reference-count":26,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,1,1]]},"abstract":"<p>Personalized medicine is customizing treatments to a patient\u2019s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model\u2019s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors\u2019 modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors\u2019 model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.<\/p>","DOI":"10.4018\/jkdb.2012010101","type":"journal-article","created":{"date-parts":[[2013,1,29]],"date-time":"2013-01-29T21:03:34Z","timestamp":1359493414000},"page":"1-22","source":"Crossref","is-referenced-by-count":1,"title":["Improved Feature Selection by Incorporating Gene Similarity into the LASSO"],"prefix":"10.4018","volume":"3","author":[{"given":"Christopher E.","family":"Gillies","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA"}]},{"given":"Xiaoli","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Oakland University, Rochester, MI, USA"}]},{"given":"Nilesh V.","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA"}]},{"given":"Mohammad-Reza","family":"Siadat","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA"}]},{"given":"George D.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department and BioBank Department Beaumont Health System, Royal Oak, MI, USA"}]}],"member":"2432","reference":[{"key":"jkdb.2012010101-0","unstructured":"Akaike, H. (1971, September 2-8). Information theory and an extension of the maximum likelihood principle. In Proceedings of the 2nd International Symposium on Information Theory, Tsahkadsor, Armenia (pp. 267\u2013281)."},{"key":"jkdb.2012010101-1","doi-asserted-by":"publisher","DOI":"10.1038\/75556"},{"key":"jkdb.2012010101-2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkn803"},{"key":"jkdb.2012010101-3","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","author":"S.Boyd","year":"2004","journal-title":"Convex optimization"},{"key":"jkdb.2012010101-4","first-page":"1","article-title":"Introduction to microarray data analysis","author":"W.Dubitzky","year":"2009","journal-title":"A Practical Approach to Microarray Data Analysis"},{"key":"jkdb.2012010101-5","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2407-6-174"},{"key":"jkdb.2012010101-6","unstructured":"Gene (n.d.). Gene. Retrieved November 11, 2012, from http:\/\/www.ncbi.nlm.nih.gov\/gene\/"},{"key":"jkdb.2012010101-7","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.531"},{"issue":"12","key":"jkdb.2012010101-8","first-page":"1497","article-title":"Reduced expression of the membrane skeleton protein beta1-spectrin (sptbn1) is associated with worsened prognosis in pancreatic cancer.","volume":"25","author":"X.Jiang","year":"2010","journal-title":"Histology and Histopathology"},{"key":"jkdb.2012010101-9","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/28.1.27"},{"key":"jkdb.2012010101-10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg153"},{"key":"jkdb.2012010101-11","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-8-60"},{"key":"jkdb.2012010101-12","doi-asserted-by":"publisher","DOI":"10.1101\/gr.079558.108"},{"key":"jkdb.2012010101-13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2009.06.002"},{"key":"jkdb.2012010101-14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000443"},{"key":"jkdb.2012010101-15","unstructured":"Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Sydney, Australia (Vol. 1, pp. 448\u2013453). San Francisco, CA: Morgan Kaufmann."},{"key":"jkdb.2012010101-16","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm344"},{"key":"jkdb.2012010101-17","unstructured":"Seco, N., Veale, T., & Hayes, J. (2004, August 22-27). An intrinsic information content metric for semantic similarity in wordnet. In de Mantaras, R. L. & Saitta, L. (Eds.), In Proceedings of the 16th European Conference Artificial Intelligence, Valencia, Spain (pp. 1089-1090). Washington, DC: IOS Press."},{"key":"jkdb.2012010101-18","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2005.50"},{"key":"jkdb.2012010101-19","doi-asserted-by":"publisher","DOI":"10.1201\/9780203011232"},{"key":"jkdb.2012010101-20","unstructured":"The Gene Ontology (n.d.). Retrieved from May 26, 2012, http:\/\/www.geneontology.org\/"},{"issue":"1","key":"jkdb.2012010101-21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso.","volume":"58","author":"R. J.Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society. Series B. Methodological"},{"key":"jkdb.2012010101-22","doi-asserted-by":"publisher","DOI":"10.1214\/07-AOAS147"},{"key":"jkdb.2012010101-23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"jkdb.2012010101-24","doi-asserted-by":"crossref","first-page":"105","DOI":"10.4137\/CIN.S3805","article-title":"Development and validation of predictive indices for a continuous outcome using gene expression profiles.","volume":"9","author":"Y.Zhao","year":"2010","journal-title":"Cancer Informatics"},{"key":"jkdb.2012010101-25","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000735"}],"container-title":["International Journal of Knowledge Discovery in Bioinformatics"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=74692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,5]],"date-time":"2024-05-05T08:20:47Z","timestamp":1714897247000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jkdb.2012010101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2012,1,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2012,1]]}},"URL":"https:\/\/doi.org\/10.4018\/jkdb.2012010101","relation":{},"ISSN":["1947-9115","1947-9123"],"issn-type":[{"value":"1947-9115","type":"print"},{"value":"1947-9123","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,1,1]]}}}