Quaid Morris
@quaidmorris.bsky.social
Computational biology, machine learning, AI, RNA, cancer genomics. My views are my own. https://www.morrislab.ai
He/him/his
He/him/his
It looks like you are trying to kill all AI pop-ups, would you like to ... <sniff>
May 14, 2025 at 4:39 PM
It looks like you are trying to kill all AI pop-ups, would you like to ... <sniff>
I don’t know what “flag” means, but I would very much like to be added to What’s Science @danirabaiotti.bsky.social please!
Here’s a cute cat picture for your trouble
Here’s a cute cat picture for your trouble
September 5, 2023 at 9:53 PM
I don’t know what “flag” means, but I would very much like to be added to What’s Science @danirabaiotti.bsky.social please!
Here’s a cute cat picture for your trouble
Here’s a cute cat picture for your trouble
ucRBPs are highly abundant in whole-cell mass spectrometry surveys. Weak non-specific RNA interactions coupled with high abundance could explain their prevalence in RNA interactome capture studies.
November 16, 2024 at 9:58 AM
ucRBPs are highly abundant in whole-cell mass spectrometry surveys. Weak non-specific RNA interactions coupled with high abundance could explain their prevalence in RNA interactome capture studies.
In contrast, CCHC‐zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD.
November 16, 2024 at 9:49 AM
In contrast, CCHC‐zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD.
Identification and analysis of sequence-specific unconventional RNA binding domains were mostly consistent with RNA‐binding as a derived function.
November 16, 2024 at 9:41 AM
Identification and analysis of sequence-specific unconventional RNA binding domains were mostly consistent with RNA‐binding as a derived function.
Only 23 ucRBPs (4.7 %) displayed sequence specificity, including 5 proteins not previously shown to possess RNA binding activity.
November 18, 2024 at 3:08 PM
Only 23 ucRBPs (4.7 %) displayed sequence specificity, including 5 proteins not previously shown to possess RNA binding activity.
Interested in gaining research experience before #gradschool? Consider our #postbac program - the MSK Bridge! Join @MSKEducation for a live info session on Thursday, Jan 19 @ 3 pm (EST).
Register: https://tinyurl.com/MSKBridgeInfoSession2023
#PrePhD #PreMDPhD #AcademicTwitter
Register: https://tinyurl.com/MSKBridgeInfoSession2023
#PrePhD #PreMDPhD #AcademicTwitter
November 18, 2024 at 2:47 PM
Interested in gaining research experience before #gradschool? Consider our #postbac program - the MSK Bridge! Join @MSKEducation for a live info session on Thursday, Jan 19 @ 3 pm (EST).
Register: https://tinyurl.com/MSKBridgeInfoSession2023
#PrePhD #PreMDPhD #AcademicTwitter
Register: https://tinyurl.com/MSKBridgeInfoSession2023
#PrePhD #PreMDPhD #AcademicTwitter
Out today: COVID forecasting using conditional latent ODE w/ @ianshi3
- easy forecasting of NPI change impacts
- data-driven
- often sig. better, never sig. worse at death forecasts than all others, incl expert-driven ones
P: https://doi.org/10.1093/jamia/ocac160
G:...
- easy forecasting of NPI change impacts
- data-driven
- often sig. better, never sig. worse at death forecasts than all others, incl expert-driven ones
P: https://doi.org/10.1093/jamia/ocac160
G:...
November 18, 2024 at 2:47 PM
Out today: COVID forecasting using conditional latent ODE w/ @ianshi3
- easy forecasting of NPI change impacts
- data-driven
- often sig. better, never sig. worse at death forecasts than all others, incl expert-driven ones
P: https://doi.org/10.1093/jamia/ocac160
G:...
- easy forecasting of NPI change impacts
- data-driven
- often sig. better, never sig. worse at death forecasts than all others, incl expert-driven ones
P: https://doi.org/10.1093/jamia/ocac160
G:...
Check out our new RBP motif algorithm:
PRIESSTESS 🧙♀️
- fits RNA sequence and structure models
- is trivially interpretable
- generalizes as well or better as black-box...
PRIESSTESS 🧙♀️
- fits RNA sequence and structure models
- is trivially interpretable
- generalizes as well or better as black-box...
November 18, 2024 at 2:47 PM
Check out our new RBP motif algorithm:
PRIESSTESS 🧙♀️
- fits RNA sequence and structure models
- is trivially interpretable
- generalizes as well or better as black-box...
PRIESSTESS 🧙♀️
- fits RNA sequence and structure models
- is trivially interpretable
- generalizes as well or better as black-box...
November 18, 2024 at 2:47 PM
Multiple genomic features, known to affect mutational processes, are weakly correlated with changepoint locations: gene and mutation density, cell-of-origin chromatin state, copy number aberrations, and kataegis.
But no single feature explains the recurrence.
But no single feature explains the recurrence.
November 16, 2024 at 11:42 AM
Multiple genomic features, known to affect mutational processes, are weakly correlated with changepoint locations: gene and mutation density, cell-of-origin chromatin state, copy number aberrations, and kataegis.
But no single feature explains the recurrence.
But no single feature explains the recurrence.
Some recurrent changepoints may track changes in chromatin state during cancer development. Five of eight recurrent changepoints in CLL show shifts in activity between SBS9 and SBS5, signatures which decrease and increase, respectively, during subclonal expansion.
November 16, 2024 at 11:34 AM
Some recurrent changepoints may track changes in chromatin state during cancer development. Five of eight recurrent changepoints in CLL show shifts in activity between SBS9 and SBS5, signatures which decrease and increase, respectively, during subclonal expansion.
At recurrent changepoints, mutational signatures often change in similar or comparable ways. Fifty-five melanomas with a changepoint on chr 1p show similar, large changes in the activity of two UV-associated signatures: SBS7a and SBS7b.
November 16, 2024 at 11:27 AM
At recurrent changepoints, mutational signatures often change in similar or comparable ways. Fifty-five melanomas with a changepoint on chr 1p show similar, large changes in the activity of two UV-associated signatures: SBS7a and SBS7b.
Recurrent changepoints are common. Eight cancer types have recurrent changepoints present in at least seven samples, and five of these recurrent changepoints are shared by multiple tumor types.
November 16, 2024 at 11:19 AM
Recurrent changepoints are common. Eight cancer types have recurrent changepoints present in at least seven samples, and five of these recurrent changepoints are shared by multiple tumor types.
AI score was, however, a significant predictor of outcome (HR=1.4) and cancer recurrence (HR=1.7), even accounting for all other major prognostic factors. And it was even a better predictor than the original AI status annotation used to develop the score!
November 16, 2024 at 12:15 PM
AI score was, however, a significant predictor of outcome (HR=1.4) and cancer recurrence (HR=1.7), even accounting for all other major prognostic factors. And it was even a better predictor than the original AI status annotation used to develop the score!
I'm enjoying playing around with the new GeneMANIA data release by @garybader1's group (Ruth Isserlin and Christian Lopes). Nearly 900 human networks!
Check it out: https://www.genemania.org
Check it out: https://www.genemania.org
November 18, 2024 at 2:47 PM
I'm enjoying playing around with the new GeneMANIA data release by @garybader1's group (Ruth Isserlin and Christian Lopes). Nearly 900 human networks!
Check it out: https://www.genemania.org
Check it out: https://www.genemania.org
We looked to see if evolutionary dynamics in blood impacted how long an individual remained disease free. Individuals fitting neutral or negative models of evolution tend to remain disease free for significantly longer than those with signatures of positive selection
November 16, 2024 at 2:47 PM
We looked to see if evolutionary dynamics in blood impacted how long an individual remained disease free. Individuals fitting neutral or negative models of evolution tend to remain disease free for significantly longer than those with signatures of positive selection
We next looked at how mutations were distributed across genes in different evolutionary classes. Many genes are commonly mutated in neutral, combination and negative classes of evolution with a small number exclusively mutated in negative and positive evolutionary models.
November 16, 2024 at 2:38 PM
We next looked at how mutations were distributed across genes in different evolutionary classes. Many genes are commonly mutated in neutral, combination and negative classes of evolution with a small number exclusively mutated in negative and positive evolutionary models.
We capture higher mutation rates in precancerous blood. Controls have a higher ratio of passenger to driver mutations which appear to slow clonal expansions in the presence of a driver mutation. There are more targets for negative selection in individuals who remain healthy!
November 16, 2024 at 2:30 PM
We capture higher mutation rates in precancerous blood. Controls have a higher ratio of passenger to driver mutations which appear to slow clonal expansions in the presence of a driver mutation. There are more targets for negative selection in individuals who remain healthy!
Interestingly, we find that the evolutionary dynamics are closely related to the age of each individual, with the proportion of individuals with signatures of negative selection declining with age
November 16, 2024 at 2:20 PM
Interestingly, we find that the evolutionary dynamics are closely related to the age of each individual, with the proportion of individuals with signatures of negative selection declining with age
We applied our classifier to deeply sequenced blood from 92 individuals who progressed to #AML and 385 controls. Most individuals show signatures of negative selection ( negative only or in combination with positive selection) which is often overlooked in evolutionary models
November 16, 2024 at 2:11 PM
We applied our classifier to deeply sequenced blood from 92 individuals who progressed to #AML and 385 controls. Most individuals show signatures of negative selection ( negative only or in combination with positive selection) which is often overlooked in evolutionary models
We were able to discriminate between different evolutionary models with a high accuracy of 86%, only confusing two regimes that are known to be difficult to distinguish. Time to apply our classifier to analyze evolution in real blood samples!
November 16, 2024 at 2:04 PM
We were able to discriminate between different evolutionary models with a high accuracy of 86%, only confusing two regimes that are known to be difficult to distinguish. Time to apply our classifier to analyze evolution in real blood samples!
Now with deep learning techniques called amortized inference we can train ensembles of deep neural networks to do inference for a realistic model of HSC evolution. We trained our ensemble on over 5 million simulated blood populations
November 16, 2024 at 1:44 PM
Now with deep learning techniques called amortized inference we can train ensembles of deep neural networks to do inference for a realistic model of HSC evolution. We trained our ensemble on over 5 million simulated blood populations
We use deep learning and population genetics models to investigate how the interplay of positive and negative selection influences #AML progression. Effectively, does negative selection acting on passenger mutations play a role in shaping health outcomes?
November 16, 2024 at 1:24 PM
We use deep learning and population genetics models to investigate how the interplay of positive and negative selection influences #AML progression. Effectively, does negative selection acting on passenger mutations play a role in shaping health outcomes?