David A Knowles (@davidaknowles.bsky.social) (@david_a_knowles) 's Twitter Profile
David A Knowles (@davidaknowles.bsky.social)

@david_a_knowles

machine learning and functional genomics at @Columbia and @nygenome. he/him/his. @[email protected] @davidaknowles.bsky.social

ID: 3309035346

linkhttp://daklab.github.io/ calendar_today07-08-2015 21:53:01

1,1K Tweet

2,2K Followers

978 Following

David A Knowles (@davidaknowles.bsky.social) (@david_a_knowles) 's Twitter Profile Photo

#MLCB2024 will be Sept 5-6 as a hybrid event in Seattle (nice time to be in the PNW!) with a great lineup of keynotes from Ava Amini, Maria Chikina, Jian Zhou and Nilah Ioannidis. Registration is free and submission deadline is June 15th. More deets at mlcb.github.io.

Hannah Payne (@hannahpaynephd) 's Twitter Profile Photo

📢I am elated to share that I will join the Center for Neural Science & Dept. of Psychology at NYU as an Assistant Professor in Sept 2025! My lab will use the remarkable food-caching behavior and active vision of chickadees to study the neural basis of episodic memory 🕊️🧠👀🏙️✨

📢I am elated to share that I will join the Center for Neural Science & Dept. of Psychology at NYU as an Assistant Professor in Sept 2025! My lab will use the remarkable food-caching behavior and active vision of chickadees to study the neural basis of episodic memory 🕊️🧠👀🏙️✨
Jacob Schreiber (@jmschreiber91) 's Twitter Profile Photo

Looking forward to #MLCB2024 later this week! Really happy to see the wide variety of work being done. Sometimes, in the past, it seems like the field converged on the same set of problems... Sad that I won't be able to be there in person this year. :(

David A Knowles (@davidaknowles.bsky.social) (@david_a_knowles) 's Twitter Profile Photo

#MLCB2024 kicks off in 22h (9am PST) with keynote from Jian Zhou on DL for transcriptional initiation! Full schedule at mlcb.github.io and public livestream at youtube.com/live/reqWvNOKl…. Please RT!

Arjun Krishnan (@compbiologist) 's Twitter Profile Photo

Two Troyanskaya Lab alums — Jian Zhou & Maria Chikina — giving keynotes at #MLCB2024! Jian now talking about sequence basis of transcription initiation in the human genome. Tomorrow, Maria is talking about biophysically interpretable sequence to function models.

David A Knowles (@davidaknowles.bsky.social) (@david_a_knowles) 's Twitter Profile Photo

The fantastic Machine Learning in CompBio #MLCB2024 papers are up at proceedings.mlr.press/v261/ (thanks Neil Lawrence !) Reminder that all the talks are available on our yt channel youtube.com/@mlcbconf. Please RT!

David A Knowles (@davidaknowles.bsky.social) (@david_a_knowles) 's Twitter Profile Photo

Our first foray into (noncoding) rare variant association testing! gruyere learns functional annotation importance & finds associations missed by existing methods. Anjali Das did a fantastic job with model assessment & scaling! Joint w Towfique Raj medrxiv.org/content/10.110…

David A Knowles (@davidaknowles.bsky.social) (@david_a_knowles) 's Twitter Profile Photo

#MLCB2025 will be Sept 10-11 at NY Genome Center in NYC! Paper deadline June 1st & in-person registration will open in May. Join our mailing list groups.google.com/g/mlcb/ for future announcements. More details at mlcb.github.io. Please RT!

Nicholas Mancuso (@nmancuso_) 's Twitter Profile Photo

Ok that's it I'm done here. I'll leave this account up to avoid it being eventually commandeered. but will not be posting/interacting.

Jonathan Sebat (@sebatlab) 's Twitter Profile Photo

Your math is wrong dude. Let me get this straight. All this time you thought a that an indirect cost rate of 60% meant that Universities took 60% of the grant? 🤦‍♂️

Jonathan Sebat (@sebatlab) 's Twitter Profile Photo

explainer for the tech bros. 60% is added on top of direct cost (and equipment are not included). So for every dollar that goes to a scientist, roughly $0.80 is allowable. Then you add 60% facilities/admin costs on top ($0.48). Uni’s final cut is $0.48/$1.48 =0.324 of the grant

Surya Ganguli (@suryaganguli) 's Twitter Profile Photo

*Every single* cure for a disease ultimately flowed from basic exploratory research. Stopping basic research is like stopping the mountain rains and expecting rivers of cures to still flow. Examples: 1) studying saliva of Gila monster -> GLP1's 2) studying funghi -> first