Machine-Learning & AI-based Approaches for GPCR Bioactive Ligand - PowerPoint PPT Presentation

Oct 01, 2023 •31 likes •411 views

Cambridge Healthtech Institute's 14th Annual GPCR-Based Drug Discovery Discovery on Target Conference September 19, 2019 | Boston, MA https://www.discoveryontarget.com/GPCR-drug-discovery Machine-Learning & AI-based Approaches for GPCR

Cambridge Healthtech Institute's 14th Annual GPCR-Based Drug Discovery — Discovery on Target Conference September 19, 2019 | Boston, MA https://www.discoveryontarget.com/GPCR-drug-discovery Machine-Learning & AI-based Approaches for GPCR Bioactive Ligand Discovery Sebastian Raschka, Ph.D. Assistant Professor sraschka@wisc.edu Department of Statistics http://stat.wisc.edu/~sraschka/

Recommend

Machine learning vs. knowledge based approaches to ADR identification November 2017 Topics Short

Machine learning vs. knowledge based approaches to ADR identification November 2017 Topics Short about us Iden3fying ADRs Machine Learning for seman3c rela3ons iden3fica3on Results Challenges SHORT ABOUT US Focus on

242 views • 22 slides

TAXONOMY AND CHALLENGES IN MACHINE LEARNING-BASED APPROACHES TO DETECT ATTACKS IN THE INTERNET

The 15th International Conference on Availability, Reliability and Security (ARES 2020) August 25 to August 28, 2020 in Dublin, Ireland ID-86 workshop paper (IoT-SECFOR) TAXONOMY AND CHALLENGES IN MACHINE LEARNING-BASED APPROACHES TO DETECT

583 views • 13 slides

Learning Approaches to Estimate Depth from RGB Lecture 5 What will we learn - Latest Approaches

Learning Approaches to Estimate Depth from RGB Lecture 5 What will we learn - Latest Approaches to Depth Estimation based on Machine Learning (DNNs) Why do we need new approaches? Paper1 -> CNNs for Depth estimation Paper2

634 views • 17 slides

10a Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10a Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

935 views • 54 slides

10d Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10d Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

1.05k views • 29 slides

10b Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10b Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

866 views • 50 slides

10c Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10c Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

605 views • 35 slides

Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis

Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis Genetics-based Machine Learning and Behavior Based Robotics: A New Synthesis, Marco Dorigo, Uwe Schnepf, IEEE Transactions on System, MAn, and Cybernetics,

348 views • 34 slides

Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma

Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma material interaction and primary radiation Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma

903 views • 29 slides

Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th Outline Machine Learning

Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th Outline Machine Learning and hep-th Introduction to Inflation Learning to Inflate Algorithm Results Possible Next Steps Machine Learning and hep-th Machine

709 views • 34 slides

Machine learning approaches to predicting protein-ligand binding Dr Pedro J Ballester MRC

Machine learning approaches to predicting protein-ligand binding Dr Pedro J Ballester MRC Methodology Research Fellow EMBL-EBI, Cambridge, United Kingdom EBI is an Outstation of the European Molecular Biology Laboratory. Talk outline 1.

880 views • 30 slides

Thinking about Genomics through a Machine Learning Lens: Basics of how several DNA- and RNA-based

Thinking about Genomics through a Machine Learning Lens: Basics of how several DNA- and RNA-based problems translate into commonly-studied areas of machine learning Faculty Lead Discussion (short version) 26 June 2018 GAUSSI Summer Retreat

323 views • 21 slides

Using machine learning approaches to identify imaging markers that predict antidepressant

Using machine learning approaches to identify imaging markers that predict antidepressant response Amit Etkin, MD, PhD Associate Professor Department of Psychiatry and Behavioral Sciences Stanford Neurosciences Institute Stanford University

474 views • 22 slides

INTRODUCTION TO MACHINE LEARNING Joseph C. Osborn CS 51A Spring 2020 Machine Learning is

INTRODUCTION TO MACHINE LEARNING Joseph C. Osborn CS 51A Spring 2020 Machine Learning is Machine learning is about predicting the future based on the past. -- Hal Daume III Machine Learning is Machine learning is about predicting

917 views • 59 slides

Balancing Selection and Beyond: Machine learning approaches for determining selection scenarios

Balancing Selection and Beyond: Machine learning approaches for determining selection scenarios in a complex parameter space Thursday 14 th February 2019 Kaileigh Ahlquist Ramachandran Lab, Brown University Balancing selection maintains

536 views • 19 slides

1 Model-Based Classification Model-Based Classification Model-based approach Build a

Machine Learning CSE 473: Artificial Intelligence Nave Bayes Up until now: how use a model to make optimal decisions Machine learning: how to acquire a model from data / experience Learning parameters (e.g. probabilities)

628 views • 8 slides

Practical Advice for Building Machine Learning Applications Machine Learning Based on lectures

Practical Advice for Building Machine Learning Applications Machine Learning Based on lectures and papers by Andrew Ng, Pedro Domingos, Tom Mitchell and others 1 ML and the world Making ML work in the world Mostly experiential advice Also

863 views • 69 slides

Prediction of Dementia Risk with Community Health Data using Machine Learning Approaches Kup-Sze

Prediction of Dementia Risk with Community Health Data using Machine Learning Approaches Kup-Sze Choi 1 , Cho-Lik Ho 1 , Ta Zhou 1 , Xiao Shen 1 , Guanjin Wang 2 1 Centre for Smart Health, School of Nursing, Hong Kong Polytechnic University 2

115 views • 10 slides

Machine Learning over Complete Intersection Calabi-Yau Manifolds Workshop on Machine Learning

Machine Learning over Complete Intersection Calabi-Yau Manifolds Workshop on Machine Learning Landscape ICTP, Trieste, Italy Challenger Mishra, ICMAT Madrid based on 1806.03121, and upcoming December 12, 2018 1. Physics Motivations 2.

655 views • 48 slides

An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning http://www.cs.iastate.edu/~cs573x/bbsilab.html Machine Learning Software Preparing Data Building Classifiers Interpreting Results Machine Learning Software

496 views • 26 slides

Classifying unstructured text Deterministic and machine learning approaches Stephanie Fischer

Using a simple tool to solve a complex problem does not result in a simple solution. Larry Wall Classifying unstructured text Deterministic and machine learning approaches Stephanie Fischer Hamburg Mnchen Berlin Kln Leipzig

506 views • 39 slides

CPSC 340: Machine Learning and Data Mining Non-Parametric Models Summer 2020 Course Map

CPSC 340: Machine Learning and Data Mining Non-Parametric Models Summer 2020 Course Map Machine Learning Approaches Supervised Semi-supervised Unsupervised Reinforcement Learning Learning Learning Learning Classification Regression

1.06k views • 57 slides

Machine learning, statistical, and network science approaches for comparing brain graphs within

Machine learning, statistical, and network science approaches for comparing brain graphs within and between modalities Jonas Richiardi FINDlab / LabNIC http://www.stanford.edu/~richiard/ Dept. of Neurology & Dept. of Neuroscience

472 views • 46 slides

PAC-learning, VC Dimension and Margin-based Bounds Machine Learning 10701/15781 Carlos

More details: General: http://www.learning-with-kernels.org/ Example of more complex bounds: http://www.research.ibm.com/people/t/tzhang/papers/jmlr02_cover.ps.gz PAC-learning, VC Dimension and Margin-based Bounds Machine Learning

919 views • 59 slides