MLCB25 Machine Learning for Computational Biology, Manolis KellisManolis Kellis

 

Manolis KellisManolis Kellis   • 2ndVerified • 2ndProfessor at MIT and Broad Institute. Genomics, Computational Biology, Disease GeneticsProfessor at MIT and Broad Institute. Genomics, Computational Biology, Disease Genetics2d • 2 days ago • Visible to anyone on or off LinkedIn

MIT Course announcement: Machine Learning for Computational Biology hashtag#MLCB25
Fall'24 Lecture Videos: https://lnkd.in/efSvp7hY
Fall'24 Lecture Notes: https://lnkd.in/eWBAxQHk
(a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks.
(b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning.
(c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification.
(d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology.
Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery.
First Lecture: Thu Sept 4 at 1pm in 32-144
With: Prof. Manolis Kellis, Prof. Eric Alm, TAs: Ananth Shyamal, Shitong Luo
Course website: https://lnkd.in/eemavz6J

ODU CS and DSC courses taught by Hong Qin

 

https://catalog.odu.edu/courses/cs/#graduatecoursestext

https://catalog.odu.edu/courses/dasc/

CS 781  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

CS 782  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

CS 881  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

CS 882  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

DASC 781  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

DASC 782  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

DASC 881  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

DASC 882  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

biomedical ML/AI

 lead students collectively write a survey paper on github
This course will discuss the research forefronts and breakthroughs of artificial intelligence in the field of biomedical related fields, including highly accurate protein structure prediction with AlphaFold, fast and energy-efficient neuromorphic deep learning with first-spike times, machine learning platform to estimate anti-SARS-CoV-2 activities, adversarial interference and its mitigations in privacy-preserving collaborative machine learning; machine learning and algorithm fairness in public and population health, and computer vision in healthcare

nature machine learning
Aviv Regev works
CSHL meeting talks
pipp workshop reports
https://www.cc.gatech.edu/~badityap/ 
https://www.biorxiv.org/content/10.1101/803205v2#readcube-epdf
https://www.nature.com/natmachintell/research-articles
Navigating the pitfalls of applying machine learning in genomics
https://www.nature.com/articles/s41576-021-00434-9 

Collection of ML/AI pitfall papers
https://github.com/crazyhottommy/machine-learning-resource/blob/master/README.md

CS courses with video lectures

 

https://github.com/Developer-Y/cs-video-courses

Spring 2022 courses, textbooks

 

CPSC 4900 CRN 20692 Software Engineering, Instructor Hong Qin

Title: Engineering Software Products: An Introduction to Modern Software Engineering
Year: 2020

Imprint: Pearson
Author: Ian Sommerville
Isbn10: 013521064X
Isbn13: 9780135210642
Item id: PGM2133348

CPSC 2100 CRN 20244 Software Design and Development

Textbook: 

Introduction to Computation and Programming Using Python, third edition: With Application to Computational Modeling and Understanding Data 3rd Edition

by John V. Guttag (Author)

• Publisher ‏ : ‎ The MIT Press; 3rd edition (January 5, 2021)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 496 pages
• ISBN-10 ‏ : ‎ 0262542366
• ISBN-13 ‏ : ‎ 978-0262542364
• 
  

R versus Python

R versus Python

https://www.esparkinfo.com/r-vs-python.html

biomedical data science

PH525x series - Biomedical Data Science

http://genomicsclass.github.io/book/

JAX computational genomics tools

On the academic side:

We will be using a number of genomic analysis software packages/tools. Please try to download and install the tools/programs listed below (IGV, R/RStudio and Python).  Ada Zhan (cc’ed here) can assist you with installation questions. We will also be able to provide support on the first day of the course. We will use a cloud computing environment (web-based) but you will get information on that platform just before the course.

If you do not have a laptop at your disposal please alert me ASAP so that we can prepare a machine for your use.

Please install the following:

Integrative Genomics Viewer: (IGV) (Broad Institute)

Please go to the Broad institute website here and download the IGV version for your Mac or PC.

R:

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio   To install on:

Windows:

Video Tutorial

Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE. Note that if you have separate user and admin accounts, you should run the installers as administrator (right-click on .exe file and select "Run as administrator" instead of double-clicking). Otherwise problems may occur later, for example when installing R packages.

Mac OS X:

Video Tutorial

Install R by downloading and running this .pkg file from CRAN. Also, please install the RStudio IDE.

Linux:

You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please install the  RStudio IDE.

Python:  To set  up Python:

Python is a popular language for scientific computing and data science, as well as being a great for general-purpose programming. Installing all of the scientific packages individually can be a bit difficult, so we recommend an using an all-in-one installer, like Anaconda.

Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.4 is fine).

We will teach Python using the Jupyter notebook, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).

Windows

• Download and install Anaconda.
• Download the default Python 3 installer. Use all of the defaults for installation except make sure to check Make Anaconda the default Python.

Mac OS X

• Download and install Anaconda.
• Download the default Python 3 installer. Use all of the defaults for installation.

Linux

We recommend the all-in-one scientific Python installerAnaconda.

1. Download the installer that matches your operating system and save it in your home folder. Download the default Python 3 installer.
2. Open a terminal window.
3. Type

bash Anaconda-

and then press tab. The name of the file you just downloaded should appear.

4. Press enter. You will follow the text-only prompts. When there is a colon at the bottom of the screen press the down arrow to move down through the text. Type yes and press enter to approve the license. Press enter to approve the default location for the files. Type yes and press enter to prepend Anaconda to your PATH (this makes the Anaconda distribution the default Python).