PPT-Machine learning for gene expression-based prediction of individual drug response for

Author : williams | Published Date : 2024-01-20

Nicolas Borisov 1 Victor Tkachev 23 Maxim Sorokin 23 and Anton Buzdin 234 1 Moscow Institute of Physics and Technology 141701 Moscow Oblast Russia 2

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Machine learning for gene expression-bas..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Machine learning for gene expression-based prediction of individual drug response for: Transcript

Nicolas Borisov 1 Victor Tkachev 23 Maxim Sorokin 23 and Anton Buzdin 234 1 Moscow Institute of Physics and Technology 141701 Moscow Oblast Russia 2 OmicsWayCorp. a highly accurate and interpretable ensemble predictor. Song . L, . Langfelder. P, Horvath S. . BMC . Bioinformatics . 2013. Steve Horvath (. shorvath@mednet.ucla.edu. ) . University of California, Los . . Kasturi. , Raj . Acharya. , . Shruthi. . Prabhakara. Department of Computer Science and Engineering, Penn State University. Clustering is performed using the k-. medoid. procedure on the RBF-fitted genes using . CpG. Island . landscape (part 2). Héctor. Corrada Bravo. CMSC858P Spring 2012. (many slides courtesy of Rafael Irizarry). How do we measure DNA methylation?. Microarray Data. One question…. Where do we measure? . http://. www.cbioportal.org/index.do. Web resource for exploring, visualizing, and analyzing . multidimentional. cancer genomics data. cBioPortal. : Purpose and Advantages. reduces. . molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. Data. Lijing Wang. 1. , . Yangzhong. . Tang. 2. , . Stevan. . Djakovic. 2. , . Julie . Rice. 2. , . Tony . Wu. 2. , . Daniel J. . Anderson. 2. , . Yuan . Yao. 3. DahShu. Data Science Symposium: Computational Precision Health . 01/24/2012. Agenda. 0. Introduction of machine . learning. --Some clinical examples. Introduction . of classification. 1. Cross validation. 2. . Over-fitting. Feature (gene) selection. Performance assessment. Presented by: . Xuwen. Zhao. Overview. Why we need this prediction. Algorithms used. SVM (support vector machine). RFE (recursive feature elimination). 3 different conditions to test for accuracies . How do we regulate the expression of our genes? . Involved in gene expression. DNA regulatory sequences. Regulatory genes. Small regulatory proteins (. RNAs. ). Regulatory sequences. Stretches of DNA that interact with regulatory proteins to control transcription.. Systematic . In . silico. . analysis using public database. Sung Hwan Lee. 1,4. , Baek Gil . Kim. 2,3. , . Ho Kyoung Hwang. 1,4. , Woo . Jung . Lee. 1,4. , Chang . Moo Kang. 1,4**. . 1. Department . ” data, and data resources. Anthony Gitter. Cancer Bioinformatics (BMI 826/CS 838). January . 22, . 2015. What computational analysis contributes to cancer research. Predicting driver alterations. Defining properties of cancer (sub)types. UNC Collaborative Core Center for Clinical Research Speaker Series. August 14, 2020. Jamie E. Collins, PhD. Orthopaedic. and Arthritis Center for Outcomes Research, Brigham and Women’s Hospital. Department of . 20 18 , Vol. 9 http://www. jcancer .org 22 49 J J o o u u r r n n a a l l o o f f C C a a n n c c e e r r 201 8 ; 9 ( 1 3 ) : 2249 - 2 265 . doi: 10.7150/ jca . 24744 Review Gene Expression Detecti Dr. Xiangyu Li. KTH-Royal In. stitute. of Technology. http://sysmedicine.com. 1. Where the transcripts come from?. 2. Pre-mRNA. Intron. Patterns . of alternative . splicing. 3. HPA 37 different human tissues. Dipartimento di Scienze del Farmaco. Università del Piemonte Orientale. Ferrara, 19 ottobre 2012. Hotel San Girolamo dei Gesuati. . “. One. . Size. . Doesn't. . Fit. . All. ”. Pharmacogenomics.

Download Document

Here is the link to download the presentation.
"Machine learning for gene expression-based prediction of individual drug response for"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.