CS795 - day1- 2025 Jan 17 Friday

Zoom, start recording

Datacamp: registration

HPC survey (5 minutes)

 The Research & Cloud Computing group (RCC) recently launched a survey regarding the need for training for research computing users. We would like to ask you to promote this survey among your students in classes and research groups as well as your colleagues, postdocs and other staff. The survey link is:

 

https://odu.co1.qualtrics.com/jfe/form/SV_9zCyC5peVHeQgl0

 

Please encourage them to submit responses by the end of January so we can use the findings to adjust offerings for this semester. Your help will be greatly appreciated!

CoLab

syllabus, 

SoCrative ice break, anonymous

Github

Let students introduct each other in breakout room. Then student A introduce student B. 

AI101, tensor flow playground. 

== did not finish. leave for next class. 

skipp self-introduction video. 

project team, 

ChatGPT, anthropic, 

all of us account

A primer on deep learning in genetics, classification model

https://colab.research.google.com/github/hongqin/Python-CoLab-bootcamp/blob/master/A_Primer_on_Deep_Learning_in_Genomics_Public.ipynb

Comment Published: 26 June 2024 Harnessing the deep learning power of foundation models in single-cell omics Qin Ma, Yi Jiang, Hao Cheng & Dong Xu Nature Reviews Molecular Cell Biology volume 25, pages593–594 (2024)Cite this article 2697 Accesses 1 Citations 5 Altmetric Metrics

 https://www.nature.com/articles/s41580-024-00756-6

• Comment
• Published: 26 June 2024

Harnessing the deep learning power of foundation models in single-cell omics

• Qin Ma, 
• Yi Jiang, 
• Hao Cheng & 
• Dong Xu 

Nature Reviews Molecular Cell Biology volume 25, pages593–594 (2024)Cite this article

• 2697 Accesses

• 1 Citations

• 5 Altmetric

• Metrics

AI in medical training

 

https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10417

AI Pademics

 

https://ai4pandemics.org/

 

Federated learning enables big data for rare cancer boundary detection

https://pubmed.ncbi.nlm.nih.gov/36470898/

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

ML in health equity

 

irene chang, ML health equity

https://youtu.be/H55zNBM3sf0

https://mlhc19mit.github.io/ 

deep learning in bio sequence analysis and modeling

 

https://github.com/hussius/deeplearning-biology

https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr

https://github.com/nageshsinghc4/DNA-Sequence-Machine-learning

https://github.com/search?q=dna+sequence+deep+learning

https://www.researchgate.net/publication/301703031_DNA_Sequence_Classification_by_Convolutional_Neural_Network