ai agent papers from james zou talk

Here are the URLs that appeared in the chat log you shared:

1. https://www.nature.com/articles/s41586-025-09442-9

2. https://www.biorxiv.org/content/10.1101/2025.06.03.657517v1

3. https://arxiv.org/pdf/2507.18901

4. https://arxiv.org/html/2509.06917v1

5. https://agents4science.stanford.edu/

AGE

 promoter methylation is associate social factor in dogs

Yuan 2011 longevity between mouse and human Framingham study. 

George Williams, evolutionary theory of aging. antagnistic pleotropic genes. 

Hongjie

Lu Wang, UT San Antonio

Alex UW Seattle

=>single cell aging: 

worm

https://c.elegans.aging.atlas.research.calicolabs.com/

fly 

=> Hevolution

GPT for few-shot genomics analysis across species

=> women in Japan and immigrant in US have different breast-cancer risks, suggesting environmental factors on age-dependent risks, city of hope presentation. 

=> 

Karen Guerrero Vazquez

Authors: Karen Guerrero Vazquez, Pilib Ó Broin, Katarzyna Goljanek-Whysall

Selection of miRNA candidates for the treatment of sarcopenia using network-based analysis and differential expression scoring. Karen Guerrero Vazquez 1, Pilib Ó Broin 1, Katarzyna Goljanek-Whysall 2. 1 School of Mathematical & Statistical Sciences, National University of Ireland Galway, 2 School of Medicine, National University of Ireland Galway. Sarcopenia is a natural consequence of aging and leads to progressive muscle wasting. Currently, there is no cure for this condition, and target identification and validation remain pressing challenges. Many potential therapeutic targets have failed in clinical trials or shown poor association with the disease. This project aims to address these challenges by creating a model of microRNA:target interactions for more efficient in silico selection of potential therapeutic targets for sarcopenia. We conducted a novel network-based analysis of microRNA involvement in aging using RNAseq and microarray data from five studies, with a total of 246 samples of skeletal muscle from healthy participants with ages ranging from 19 to 85 years old. We analyzed young, middle age and older adults and determined interactions between genes coming from co-expression, colocalization, genetic interaction, physical interaction, predicted and shared protein domain calculated with GeneMania. Next, we predicted microRNAs that are putative regulators of shortlisted genes using target prediction tools, such as TargetScan, mirDB and mirTarbase. Finally, we add tissue expression from Diana-miTed and miRNATissue Atlas2. After identifying the network of gene:gene and microRNA:gene interactions, the relevance of each node was calculated using multiple scoring metrics and measures of centrality in order to identify the most likely key regulatory microRNAs and their targets during aging. Our model of microRNA-target interactions is specifically tailored to the context of muscle aging and offers a more comprehensive and accurate representation of the complex regulatory mechanisms involved in muscle aging than existing tools, as it integrates multiple layers of biological information. By identifying a few tens of microRNAs and genes with potential therapeutic power, our model offers a valuable and efficient approach to target identification and validation for the treatment of sarcopenia. This abstract has emanated from research supported in part by a research grant from Science Foundation Ireland under Grant number [18/CRT/6214]

=> 

Explaining the asynchrony of aging through cell population dynamicsMing Yang1, Benjamin R. Harrison1, Daniel E.L. Promislow1,21 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195 USA 2 Department of Biology, University of Washington, Seattle, WA 98195 USA Different tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cell lifespan explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.This work was supported by NIH R01 AG063371 (to D. Promislow and S. Pletcher) and Norn Group Impetus Grant (to D. Promislow).

https://www.biorxiv.org/content/10.1101/2023.05.31.543091v1

names in AI research

 

Erik Brynjolfsson, promise and peril of human-like AI, Turing trap

Swati Gupta: AI4OPT

Beth Plale: AI accountability

Aaron Smith, ethics of AI in agriculture

Wendell Wallach

AI in the wild, 

Kris Hauser, AI FARMS, open-world AI

AI-EDGE, Kaushik Chowdhury, 

Machine learning for inverse problems, Alex Dimakis

AI for weather and costal forecasting, Philippe Tissot, 

Kathleen Fisher, an analytical framework for AI

Jeff Krolik

AI for social good, Michael Littman

Jim Dolon, NSF PO, Steven Thompson, AIVO/SAIL

Steve Brown, https://youtu.be/1Re9DX7cFRI 

tech symposium notes

 

FlutterFlow , buliding apps cross platform. 

EEG smart move, http://www.eegsmart.com/en/udroneIndex.html

Very few white faculty show up. Mostly black and asian faculty. I saw Mengjun Xie and Yingfeng Wang. Chemistry Department head Dungey. 

Tinus Brown's open science talk slides

 

https://osf.io/bsner/

machine learning for health

 

https://ml4health.github.io/2021/pages/call-for-participation.html

CVPR meeting notes

morphing attack detection  

https://www.christoph-busch.de/projects-mad.html

https://ieeexplore.ieee.org/document/9246583

https://biolab.csr.unibo.it/FVCOnGoing/UI/Form/BenchmarkAreas/BenchmarkAreaDMAD.aspx

intel analytics zoo

https://github.com/intel-analytics/analytics-zoo

normalization method in dnn

https://normalization-dnn.github.io/ 

https://github.com/QinLab/network-controllability

https://www.youtube.com/watch?v=bwIgpxmWnWM

https://sites.google.com/view/fgvc8

https://fadetrcv.github.io/2021/

neural network architecture search:  https://cvpr21-nas.com/

https://www.es.ele.tue.nl/cvpm21/program.pdf

microscopic image:  https://cvmi2021.github.io/

The Cell Tracking and Mitosis Challenge

https://github.com/JonathonLuiten/TrackEval/blob/master/docs/MOTChallenge-Official/Readme.md

Ngyuen multi task corn yield prediction

 Nyguen mentioned that CUDA does not allow recursion

multiple data as input (normalization, input layers)? 

multi-task training (optimization procedure, weight? )

self-boosted forecasting time series 

slide 31: joining features, 

loss function: weight averaged. what weight? 

multi task, multi feature

ISCB HPC AI competition

 

The International Society for Computational Biology is pleased to announce the HPC-AI Advisory Council (HPCAIAC) and National Supercomputing Centre (NSCC) Singapore 2021 APAC HPC-AI Competition.

High-performance computing and artificial intelligence are the most essential tools fueling the advancement of science. In order to handle the ever-growing demands for higher computation performance and the increase in the complexity of research problems, the world of scientific computing continues to re-innovate itself in a fast pace.

The competition encourages international teams in the APAC region to showcase their HPC and AI expertise in a friendly yet spirited competition that builds critical skills, professional relationships, competitive spirits and lifelong comraderies.

Important Deadlines

• The HPC-AI Advisory Council will finalize the list of competitive teams by April 30th, 2021
• The HPC-AI Advisory Council will announce the training plan on May 7th, 2021
• All teams should submit presentation slides together with their code before October 15th 2021
• The HPC-AI Advisory Council will announce the presentation review agenda on October 19th, 2021
• The presentation review is scheduled from October 26 to November 6, 2021 via video conference. Each team will have 30 minutes to present and 30 minutes for Q&A.
• The final results will be announced at the Supercomputing Conference 2021 in November 2021, in St. Louis, MO, USA.
• The award ceremony will take place at the SupercomputingAsia 2022 conference in Singapore

The winning teams will receive the following awards*:

• First Place (one team): $5,000 (USD) and a reserved spot representing APAC at the 2022 International ISC Student Cluster Competition
• Second Place (one team): $3,000 (USD)
• Third Place (one team): $1,500 (USD)
• Merit Prize (up to three teams): $1,000 (USD)
• Each team member will receive a certificate.

To become part of a team – register here - http://www.hpcadvisorycouncil.com/events/2021/APAC-AI-HPC/register.php

Robert rule of order

 

https://www.ccri.edu/acadaffairs/pdfs/Appendix%20IVRobertsRulesOfOrder.pdf

CSHL network biology,

 

genetic perturbation on protein concentration, PPI from string database. 

Weiqun Li, Georgian Washington Univ. hichub

D-Script

http://cb.csail.mit.edu/cb/dscript/

STEM for all Video Showcase

 

https://stemforall2021.videohall.com/pages/about/for-presenters#Content

NSF Workshop on Predictive Intelligence for Pandemic Prevention

NSF Workshop on Predictive Intelligence for Pandemic Prevention

 

https://thepipp.org/

Kistorm discussion,

https://kistorm.com/LKrWmzgfIy1Uz0SL9Aye/Hd4bwYS68oYJxWcvZzyO

https://www.viralemergence.org/work

research seminar.org

 

https://researchseminars.org/seminar/size-and-growth

APS meeting home

 

http://meetings.aps.org/Meeting/MAR21/APS_epitome?fbclid=IwAR0YDv7JBecTCzF4cQciurke8Z7Is7k3loZLkvuWs9ymsG9nX0FBQ-roY_c

Al-hasmi talk, conformational penalty

Conformational penalties: The other half of molecular recognition

At the most fundamental level, living organisms are the product of biomolecules interacting with one another through a process commonly referred to as molecular recognition.  To understand how biomolecule interact with one another, we need a framework that describes those properties of the biomolecules that determine their binding affinities and specificities.  Our current understanding dates back six decades ago when Linus Pauling proposed that specificity is achieved through the structural complementarity of the binding partners.  This concept has been reinforced over the decades thanks to advances in the determination of high-resolution structures of biomolecules by X-ray crystallography and cryoEM.  Static structures only carry information regarding one half of the molecular recognition equation, which I will refer to as income.  This half describes the favorable contacts formed upon complex formation.  The second half, which has received much less attention, I will refer to as income tax.  It represents the energetic cost associated with changing the structure of a biomolecule from one form to another when binding a partner molecule.  Unlike income, the income tax half of the molecular recognition equation can only be determined experimentally through an ensemble description of biomolecules as a probability distribution of many different conformations.  I will argue that income tax, and mechanisms for tax evasion, are ubiquitous in biology and disease, drawing on DNA replication as a primary example. 

https://www.nature.com/articles/s41586-020-2843-2

https://sites.duke.edu/alhashimilab/research/

free energy tax

distribution of the sub-states really affect taxation of free energy. Mentioned to use MD to run and get distribution of molecules, keep the caveat that it might not reflect experimental results. 

TF factors and motifs:: specificity and affinity are generally correlated, but can be altered. 

free engery and probability landscape is connected by logrithm

Tax impairs DNA replication forks and increases DNA breaks in specific oncogenic genome regions

Hassiba Chaib-Mezrag, Delphine Lemaçon, Hélène Fontaine, Marcia Bellon, Xue Tao Bai, Marjorie Drac, Arnaud Coquelle, and Christophe Nicot. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168069/

"Free energy minimisation is equivalent to maximising the mutual information between sensory states and internal states that parameterise the variational density (for a fixed entropy variational density).^{[11][better source needed]} This relates free energy minimization to the principle of minimum redundancy^[25] and related treatments using information theory to describe optimal behaviour." 

This is related to our cross-entropy work on aging analysis 

Kullback-Leibler divergence, or relative entropy

https://en.wikipedia.org/wiki/Relative_entropy

active inference by Karl Friston

Re Friston: a good intro talk: https://www.santafe.edu/events/me-and-my-markov-blanket

what’s the authors name of the self replicating machine?

von Neumann

 The book that was cited is called "The theory of self-reproducing automata"

Here’s Art Burk’s review of von Neumann: http://walterfontana.zone/wp-content/uploads/2020/12/Burks-1969.pdf

https://www.sciencedirect.com/science/article/pii/S2405471218300577

 This paper does exactly that on the Lac repressor

large network, MBI talk

 

 

Cellular and protein homeostasis webinars

Paolo De Los Rios, École polytechnique fédérale de Lausanne (EPFL)

Webinar series on cellular and protein homeostasis

Organised by: P. De Los Rios, N.B. Nillegoda, A. Barducci and P. Goloubinoff

https://tube.switch.ch/channels/4ed71569

Harvard theory lunch talk

 

http://vcp.med.harvard.edu/tl-schedule.html?fbclid=IwAR3PmAySziAjzGjlJcCaKPKwbB18RSRS7WpvSIqhNc_Oq-avP4wWsR1r2vo

 

2020 SACNAS – The National Diversity in STEM Virtual Conference

2020 SACNAS – The National Diversity in STEM Virtual Conference Dates: October 19 – 24 Location: Online! https://www.2020sacnas.org/