PPT-Deep Learning in Bioinformatics

Asmitha Rathis Why Bioinformatics Protein structure Genetic Variants Anomaly classification Protein classification SegmentationSplicing Why is Deep Learning beneficial scalable with large datasets and are effective in identifying complex patterns from featurerich datasets . Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. CS 678 – Deep Learning. 1. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. Introducing Programming in Genetics Classes. Ogun. . Adebali. , Ed . Himelblau. , . Ioannis. . Tsiligaridis. A Bridge to Bioinformatics Tools. Goals:. -Reinforce material learned in the genetics . c. Outline. Introduction/Questions. Explain . user space (home directories) vs share space (. lab directories/shares).. Working . in a team, file/folder . permissions.. The . module system, when to request software, when to install software . Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Provided by WV-INBRE. Mary E. Davis, Ph.D.. Bioinformatics Core Director. Outline. Overview of WV-INBRE. Technology supported. Agilent microarray. Illumina. HiSeq1000 (Next Generation Sequencing). Bioinformatics analysis resources supported. Jan - June 2015. Consulting researchers on bioinformatics tools, data analysis, data management, . and interpretation of experimental . data.. Analyzing of high. -throughput . sequencing data and conducting . Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. Richard H. Scheuermann, Ph.D.. Director of Informatics. JCVI. Outline. What is Bioinformatics? . Some definitions. Data types and analysis objectives. Big Data. T. he Big Data . value proposition. The Power of Bioinformatics. Dr. . Matthew . Cserhati (UNMC). Nebraska . Wesleyan. Phage Symposium. April 15, 2016. Personal introduction. MSc: . biology. , . Eotvos Lorand University, Hungary. BSc: . University of Szeged, . software engineering, Hungary. Dr. Ronald Moura. ronaldmoura1989@gmail.com. https://www.linkedin.com/in/ronald-moura-660017178. /. Gordon Moore . “The . number of transistors in a dense integrated circuit doubles about every two . computer science, mathematics, information technology and statistics. to analyze the biological data like . genomics, Protein structure prediction, . Pharmaco-phore. modeling , toxicity prediction and drug designing.. Petrus Tang, Ph.D. (. 鄧致剛. ). Graduate Institute of Basic Medical Sciences. and. Bioinformatics Center, Chang Gung University. .. petang@mail.cgu.edu.tw. EXT: . 5136. 助教：. 蔡智宇. (. 分機.