iiinova — where your digital self keeps evolving

Reposted by Arjun Krishnan

Casey Dunn @caseywdunn.bsky.social · 25/08/2026

My new book, Phylogenetic Biology, is now published. Available online for free at dunnlab.org/phylogenetic... . Physical copies can be purchased at shop.lightningsource.com/b/085?params... or your favorite bookstore.

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Srinivas Ramachandran @4everbiochemist.bsky.social · 03/06/2026

New from the lab: cfDNA provides an unexpected window into telomere chromatin state and genome-wide chromatin changes that accompany telomere shortening, from a blood draw. We hope this opens a path toward monitoring telomere biology disorders with a blood test. www.biorxiv.org/content/10.6...

biorxiv.org

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Institute for Artificial Intelligence in Medicine @iaim-nu.bsky.social · 12/05/2026

@nucatsinstitute.bsky.social and @kristiholmes.bsky.social were pleased to host @compbiologist.bsky.social for a seminar discussing his lab's work developing #ComputationalMethods that organize poorly labeled data. The goal is to make millions of publicly available datasets discoverable.

Photo of Arjun Krishnan with Kristi Holmes in front of slides with the title "Everything Everwhere All At Once: Making Sense of Massive Public Data Collections."

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Ran Blekhman @blekhman.bsky.social · 29/04/2026

🧬 AI in Genomics Symposium next week! We're bringing together leading scientists from across academia and industry pushing the boundaries of AI in genomics research Join us May 8 at the University of Chicago - KCBD Auditorium, 9:30–5:00, reception to follow. No registration needed!

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Arjun Krishnan @compbiologist.bsky.social · 13/04/2026

What is the PhD actually for, especially now that AI can do increasingly more of what we train scientists to do? compbiologist.substack.com/p/what-is-th... A response to @pracheeac.bsky.social's thought-provoking essay "Free the PhD".

compbiologist.substack.com

What is the PhD actually for?

A response to Prachee’s “Free the PhD”, and a critique of my own work

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Arjun Krishnan @compbiologist.bsky.social · 07/04/2026

Sean Davis & I are hiring a postdoc to work on turning massive public biological data collections into reusable engines for discovery. #ML / #AI + large-scale omics + open software Details + apply: cu.taleo.net/careersectio... seandavi.github.io | thekrishnanlab.org @cubiomedinfo.bsky.social

cu.taleo.net

Post-Doctoral Fellow

Click the link provided to see the complete job description.

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Arjun Krishnan @compbiologist.bsky.social · 06/04/2026

A lab using my recent articles on AI use during PhD training doi.org/10.5281/zeno... & doi.org/10.5281/zeno... to kickstart a discussion and draft guidelines for their group is exactly the kind of use I hoped these articles would inspire! Highly recommend reading Ran's post.

doi.org

Build expertise first: why PhD training must sequence AI use after foundational skill development

Generative AI tools have arrived in PhD training environments faster than principled frameworks for their use. The debate has polarized between enthusiasts who argue trainees must adopt AI immediately...

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Ran Blekhman @blekhman.bsky.social · 05/04/2026

I wrote about why every lab should have AI use guidelines, and how to do it. open.substack.com/pub/blekhman...

open.substack.com

You need to make AI guidelines for your lab

Here's why you should, and how to start

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Braasch Lab @ Michigan State University @fishevodevogeno.bsky.social · 03/04/2026

@compbiologist.bsky.social and @fishevodevogeno.bsky.social present newly minted Dr. Hao Yuan @yhbioinfo.bsky.social! @michiganstateu.bsky.social Amazing work @ the intersection of computational, biomedical, and evolutionary biology! Hao starts a postdoc in the @edwardmarcotte.bsky.social Lab soon.

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Arjun Krishnan @compbiologist.bsky.social · 20/03/2026

We have a new platform for employees to give and receive kudos, and it's weekly digests are brutal!

Recognition Digest: February 27, 2026 - March 6, 2026

Summary:
0
Recognitions Received by Your Team

Recognitions
Looks like no one in this group has been recognized recently. Start the momentum and send a recognition!

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Arjun Krishnan @compbiologist.bsky.social · 09/03/2026

Enjoyed working w/ Stephen & Agnes on this! Gave me a chance to think systematically about where AI 🤖 can backstop human 🧑🏼‍🔬 fallibilities in peer review (fatigue, ordering effects, bias) vs. where human judgment remains essential (novelty, feasibility, creative leaps) while grappling w/ the risks.

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Stephen Turner @stephenturner.us · 09/03/2026

Peer review reliability is shockingly low. Meta-analyses show reviewer agreement barely above chance, and grant outcomes often depend more on who reviews than what's proposed. Our new preprint with Agnes Urban and Arjun Krishnan @compbiologist.bsky.social : papers.ssrn.com/sol3/papers.... 🧵 1/

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

8/8 PhD programs, mentors, & professional societies urgently need community standards built on developmental frameworks, not ad hoc policies shaped by convenience. Thanks to NSF for the support. We welcome feedback from the community to refine these ideas! #PhDLife #Bioinformatics #OpenScience

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

7/8 The practical Guide 📋 (zenodo.org/records/18452319) provides task-specific protocols for computational data analysis, manuscript writing, literature review, and more. Designed to be adapted by institutions, programs, and labs for their specific training contexts.

zenodo.org

Expertise before augmentation: a practical guide to using generative AI during research training

This comprehensive implementation guide accompanies the framework described in "Build expertise first: why PhD training must sequence AI use after foundational skill development" (Krishnan, 2026, DOI:...

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

6/8 The Perspective article 📄 (zenodo.org/records/18649847) presents the conceptual framework grounded in empirical evidence from learning science. It introduces principles like "expertise before augmentation" and explains why cognitive automation during training undermines development.

zenodo.org

Build expertise first: why PhD training must sequence AI use after foundational skill development

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

5/8 The solution is sequencing: Build foundational expertise FIRST through deliberate, feedback-driven practice. Then use AI to augment that expertise. The threshold isn't a fixed number of attempts; it's demonstrated independent mastery: complete tasks, explain reasoning, catch errors.

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

4/8 We introduce the "verification paradox": trainees can't meaningfully verify AI outputs because verification requires the very expertise they're still developing. Using AI before that expertise exists bypasses the developmental process while producing polished outputs that mask the gap.

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

3/8 The key insight: GenAI is categorically different from previous automation (calculators, statistical software, search engine). Those automated mechanical execution. GenAI automates cognition itself: reasoning, synthesis, judgment. This difference changes everything about when it should be used.

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

2/8 The debate has polarized between "adopt now or fall behind" vs "AI will destroy learning." Both miss the critical question: not WHETHER ❓ to use AI, but WHEN ⏳.

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Arjun Krishnan @compbiologist.bsky.social · 18/02/2026

PhD programs worldwide face an urgent question: How should trainees use ChatGPT, Claude & similar tools? Now online: two resources on thoughtfully integrating generative AI into research training. 📄 Conceptual framework: zenodo.org/records/18649847 📋 Practical guide: zenodo.org/records/18452319 🧵

zenodo.org

Build expertise first: why PhD training must sequence AI use after foundational skill development

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Stephen Turner @stephenturner.us · 16/02/2026

From Arjun Krishnan @compbiologist.bsky.social Expertise before augmentation: a practical guide to using generative AI during research training: zenodo.org/records/1845... Build expertise first: why PhD training must sequence AI use after foundational skill development: zenodo.org/records/1864...

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Soragni:Lab @alice.soragnilab.com · 10/02/2026

Happy to share this new, very intentional chapter. I have left UCLA after 14 years to join the University of Colorado Anschutz as Professor of Biomedical Informatics and Neurosurgery and the inaugural Marsico Chair in Excellence in Functional Precision Medicine/n news.cuanschutz.edu/dbmi/cu-ansc...

news.cuanschutz.edu

CU Anschutz Recruits National Leader to Launch Functional Personalized Medicine Initiative

CU Anschutz welcomes Alice Soragni to launch a Functional Personalized Medicine Initiative using rapid tumor organoid testing to guide treatment decisions.

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Braasch Lab @ Michigan State University @fishevodevogeno.bsky.social · 26/01/2026

Our Fish EvoDevoGeno Lab @michiganstateu.bsky.social has its 10th anniversary today! 🐠🐟🧪🧬🔬 Thanks to all lab members - present & past, pictured or not - for making the last decade a success! & thanks to our partners in crime of the @brainyfishguts.bsky.social Lab, too! #EndlessFishMostBeautiful

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Arjun Krishnan @compbiologist.bsky.social · 08/01/2026

4/4 This advanced short course formalizes the instruction of these ideas. The goal is to: 1) Discuss common misunderstandings & typical errors in the practice of statistical data analysis. 2) Provide a mental toolkit for critically thinking about statistical methods & results. Feedback welcome 🙌🏼

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Arjun Krishnan @compbiologist.bsky.social · 08/01/2026

3/4 As a result, most students piece together a mental model of acceptable, standard, or "best" practices in their field from shards of information gathered from mentors, peers, and published papers.

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Arjun Krishnan @compbiologist.bsky.social · 08/01/2026

2/4 Statistical inquiry, data analysis, and visualization are immensely powerful, but many of the ideas underlying them are nuanced and unintuitive. Unfortunately, these ideas—and the skills needed to apply them to real problems and datasets—are rarely taught in statistics or data-analysis courses.

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Arjun Krishnan @compbiologist.bsky.social · 08/01/2026

I'm looking forward to re-teaching: Rethinking Data Analysis — A researcher’s guide to avoiding missteps and misuse This is an advanced short course on developing a mental toolkit for rigorous practice & critical consumption of statistical data analyses. 🧵 1/4

HMGP 7622
Rethinking Data Analysis — A researcher’s guide to avoiding missteps and misuse
Feb 3 – May 5, 2026 | Tue 2–3:30p

OVERVIEW
This is a short (1-credit) course designed to:
1) Discuss common misunderstandings & typical errors in the practice of statistical data analysis.
2) Provide a mental toolkit for critically thinking about statistical methods and results.

TOPICS
Estimating error, uncertainty • Underpowered statistics • Multiple testing • P-hacking • Pseudoreplication • Regression to the mean • Double dipping • Spurious associations • Visualization challenges • Reproducibility, replicability

PREREQUISITES
1) Introductory knowledge of statistics & probability
2) Introductory experience with data wrangling, analysis, & visualization using R/Python.

INSTRUCTOR
Arjun Krishnan
Associate Professor, Department of Biomedical Informatics
University of Colorado Anschutz Medical Campus
arjun.krishnan@cuanschutz.edu | @compbiologist | thekrishnanlab.org

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

10/10 Big thanks to NIH/NIGMS, NSF, & @simonsfoundation.org for funding this work! We welcome feedback from the community! 🙌 #Bioinformatics #TranslationalResearch #OpenScience

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

9/10 By embracing data-driven, evolution-agnostic approaches, we believe that the field can accelerate discoveries in both common and rare diseases, improving model organism selection and ultimately paving the way for more reliable therapeutic interventions.

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

8/10 Key future directions we highlight: - Capturing specific facets of complex diseases - Building networks for more species & contexts - Automated ontology/knowledge graph construction - Better benchmarking for cross-species single-cell methods - Leveraging non-traditional research organisms

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

7/10 Kudos to resources like @geneontology.bsky.social , @monarchinitiative.bsky.social, @alliancegenome.bsky.social, & @bgee.org for grounding so much data & knowledge in this space in structured formats. These & many others are included in our catalog ☝🏽

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

6/10 We provide detailed resources to help computational & wet-lab researchers find, improve-upon, and apply appropriate methods: 📊 Supp Table 1: Comprehensive catalog of methods (name, category, input/output, data types) 📚 Supp Table 2 & Note: Valuable datasets for cross-species work

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

5/10 With the explosion of large-scale multi-species genomics data and advanced #AI & #ML methods, it's an exciting time to rethink cross-species translational biomedicine. Our article offers a roadmap to navigate this frontier and maximize the value of research organisms.

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

4/10 Traditional approaches rely heavily on homology. But shared ancestry ≠ shared function & vice-versa. Here, we introduce the concept of Agnology, which embraces this complexity: "agno-" = unknown/not known, reflecting data-driven functional equivalence regardless of evolutionary origin.

Figure depicting how traditional methods rely on homology to determine functional equivalence and, by contrast, modern data-driven approaches use what we call 'agnology', identifying fully or partially functionally equivalent genes regardless of evolutionary origin, allowing for ambiguity in homology or analogy.

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

3/10 Our article covers methods that tackle 4 key questions in cross-species research: 1. Predicting function/disease-gene relationships across species 2. Identifying agnologous molecular components 3. Inferring perturbed transcriptomes across species 4. Mapping agnologous cell types and states

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

2/10 #ResearchOrganisms like 🐭 & 🐟 are crucial for studying genes, functions, cell types, & disease. But translating findings to 👨‍⚕️ is tricky. We explore data-driven methods to bridge the gap & introduce the concept of "agnologs" — functional equivalents identified independent of evolutionary origin.

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Arjun Krishnan @compbiologist.bsky.social · 06/01/2026

Our Perspective article on Computational Strategies for Cross-Species Knowledge Transfer is now published in @natmethods.nature.com! This was a collab b/w @krishnanlab.bsky.social & @fishevodevogeno.bsky.social, led by the amazing Hao Yuan @yhbioinfo.bsky.social. 🧵 www.nature.com/articles/s41...

Figure depicting the four classes of important questions that frequently arise when using research organisms to study biomedical questions and translating findings to humans. a, How to predict disease–gene or function–gene relationships across species? Diagram depicts genes in each species associating with specific functions, diseases and phenotypes. b, How to identify functionally equivalent molecular components across species? Diagram depicts finding the most equivalent gene, pathway or expression module or phenotype between species. c, How to infer perturbed molecular profiles across species? Diagram depicts gene expression in each species as a result of taking a particular perturbation like a drug. d, How to map equivalent cell types and cell states across species? Diagram depicts alignment of cell types across species. This Perspective comprehensively lays out the landscape of recent and state-of-the-art data-driven strategies, including those that leverage artificial intelligence and machine learning (ML), for answering these questions.

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Arjun Krishnan @compbiologist.bsky.social · 04/01/2026

Same from @cp-cellreports.bsky.social: Dec 24th, 25th, & 27th. Closed/removed on Dec 30th!

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Srinivas Ramachandran @4everbiochemist.bsky.social · 16/12/2025

Known for decades: DNA sequence drives nucleosome "rotational positioning" (which face of DNA contacts histones) But: How does this persist when remodelers & transcription constantly mobilize nucleosomes? Our new preprint 1/ : www.biorxiv.org/content/10.6...

biorxiv.org

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Kelly Sullivan @kellydsullivan.bsky.social · 14/02/2025

Very proud to be a member of this team. A huge group effort to improve the lives of those with Down syndrome. Thank you to everyone that has helped us along the way.

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Arjun Krishnan @compbiologist.bsky.social · 22/01/2025

Congratulations! Kudos to @richabdill.com & @samanthagraham.bsky.social for leading this huge project! Thanks for bring us onboard! Mansooreh Ahmadian & Parker Hicks lead the part of the work on inferring study annotations from unstructured metadata and text from the linked publications.

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Arjun Krishnan @compbiologist.bsky.social · 03/01/2025

We just use the #papers-articles channel our group’s Slack workspace.

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Arjun Krishnan @compbiologist.bsky.social · 20/12/2024

A favorite! Interestingly, Goodhart stated (in 1975): “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Marilyn Strathern generalized it in 1997 to its famous version👇🏽 pmc.ncbi.nlm.nih.gov/articles/PMC...

pmc.ncbi.nlm.nih.gov

“When a Measure Becomes a Target, It Ceases to be a Good Measure”

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Titus Brown @titus.idyll.org · 16/12/2024

Regularly tempted to write in my NIH grants innovation section: "Funding software that already exists and works well would be highly innovative for the NIH." (I bet half the panel would break down ROFL, but I'm also highly skeptical that I'd get a good score, or that the PO would be amused.)

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Denis Wirtz @deniswirtz.bsky.social · 14/12/2024

Last update of our databases for the year. Download them here: 493 early-career funding opportunities: research.jhu.edu/rdt/funding-... 313 postdoc fellowships: research.jhu.edu/rdt/funding-... 189 PhD fellowships: research.jhu.edu/rdt/funding-...

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Evan Brenner @evanbrenner.bsky.social · 10/12/2024

The views from the office are another real perk of working here at @cubiomedinfo.bsky.social.

A morning landscape shot from the rooftop of a building in Aurora, CO. The scene faces west towards the Front Range mountains, with the city skyline of Denver in the center of the shot. The view, rows of scattered buildings, evergreens, and brown branches of leafless deciduous trees, rising up into the mountains, is blanketed in a crisp white layer of snow. The sky is a sharp blue to white gradient with clouds enveloping the highest mountain peaks including Mt. Blue Sky in the distance. Some goofy Canadian geese are in mid-flight at around the level of the horizon, and while you can't hear them in a photo, I certainly could at the time.

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