🦠🔬🤖🧑💻 #mAIcrobe interface preview, a platform for #DeepLearning enabled bacterial image-based phenotyping. Check it out
💻: github.com/HenriquesLab...
📄: www.biorxiv.org/content/10.1...
💻: github.com/HenriquesLab...
📄: www.biorxiv.org/content/10.1...
October 27, 2025 at 12:08 PM
🦠🔬🤖🧑💻 #mAIcrobe interface preview, a platform for #DeepLearning enabled bacterial image-based phenotyping. Check it out
💻: github.com/HenriquesLab...
📄: www.biorxiv.org/content/10.1...
💻: github.com/HenriquesLab...
📄: www.biorxiv.org/content/10.1...
I'm excited to share that our work on #deeplearning based shape modeling is finally out in #ieee.
🧵 for info about Data, Benchmarks, and Models!
@akshay-chaudhari.bsky.social @stanfordmedicine.bsky.social
www.medrxiv.org/content/10.1...
ieeexplore.ieee.org/document/107...
🧵 for info about Data, Benchmarks, and Models!
@akshay-chaudhari.bsky.social @stanfordmedicine.bsky.social
www.medrxiv.org/content/10.1...
ieeexplore.ieee.org/document/107...
November 25, 2024 at 8:26 PM
I'm excited to share that our work on #deeplearning based shape modeling is finally out in #ieee.
🧵 for info about Data, Benchmarks, and Models!
@akshay-chaudhari.bsky.social @stanfordmedicine.bsky.social
www.medrxiv.org/content/10.1...
ieeexplore.ieee.org/document/107...
🧵 for info about Data, Benchmarks, and Models!
@akshay-chaudhari.bsky.social @stanfordmedicine.bsky.social
www.medrxiv.org/content/10.1...
ieeexplore.ieee.org/document/107...
#Deeplearning-based #CRISPR DNA integration and editing by Thomas Naert & Sören Lienkamp & Co with lots of @mesospim.bsky.social #lightsheet #Microscopy data of tadpoles (and some taken with the #Schmidt #multiimmersion objective prototype!) 🔬🐸 www.nature.com/articles/s41...
August 13, 2025 at 2:09 PM
#Deeplearning-based #CRISPR DNA integration and editing by Thomas Naert & Sören Lienkamp & Co with lots of @mesospim.bsky.social #lightsheet #Microscopy data of tadpoles (and some taken with the #Schmidt #multiimmersion objective prototype!) 🔬🐸 www.nature.com/articles/s41...
The results are in: top codes in Stanford #RNA 3D Folding @kaggle.com competition are competitive with CASP16-leading humans Vfold, beat AlphaFold 3. Top team’s trick was template-based modeling, not #DeepLearning. Congrats: john, odat, Eigen, + all 1706 participants! www.kaggle.com/competitions...
September 24, 2025 at 4:21 PM
The results are in: top codes in Stanford #RNA 3D Folding @kaggle.com competition are competitive with CASP16-leading humans Vfold, beat AlphaFold 3. Top team’s trick was template-based modeling, not #DeepLearning. Congrats: john, odat, Eigen, + all 1706 participants! www.kaggle.com/competitions...
Happy to share that our Helixer deeplearning-based annotation of 230 superasterid genomes is now online on
@datadryad.bsky.social
💻🌱
doi.org/10.5061/drya...
Check out how we used 69 of them to study the molecular evolution of a plant immune receptor network here! 👇
@datadryad.bsky.social
💻🌱
doi.org/10.5061/drya...
Check out how we used 69 of them to study the molecular evolution of a plant immune receptor network here! 👇
May 9, 2025 at 11:19 AM
Happy to share that our Helixer deeplearning-based annotation of 230 superasterid genomes is now online on
@datadryad.bsky.social
💻🌱
doi.org/10.5061/drya...
Check out how we used 69 of them to study the molecular evolution of a plant immune receptor network here! 👇
@datadryad.bsky.social
💻🌱
doi.org/10.5061/drya...
Check out how we used 69 of them to study the molecular evolution of a plant immune receptor network here! 👇
New study @biorxivpreprint
We revea, how TnsC filaments form and grow in #CRISPR-associated transposons using #FreeEnergy sims & introduce #DeepLearning-based Graph Attention Networks for decoding biomolecular systems!
www.biorxiv.org/content/10.1...
#GNN #MolecularDynamics #StructuralBiology
We revea, how TnsC filaments form and grow in #CRISPR-associated transposons using #FreeEnergy sims & introduce #DeepLearning-based Graph Attention Networks for decoding biomolecular systems!
www.biorxiv.org/content/10.1...
#GNN #MolecularDynamics #StructuralBiology
June 19, 2025 at 8:09 PM
New study @biorxivpreprint
We revea, how TnsC filaments form and grow in #CRISPR-associated transposons using #FreeEnergy sims & introduce #DeepLearning-based Graph Attention Networks for decoding biomolecular systems!
www.biorxiv.org/content/10.1...
#GNN #MolecularDynamics #StructuralBiology
We revea, how TnsC filaments form and grow in #CRISPR-associated transposons using #FreeEnergy sims & introduce #DeepLearning-based Graph Attention Networks for decoding biomolecular systems!
www.biorxiv.org/content/10.1...
#GNN #MolecularDynamics #StructuralBiology
Storm bound in Antarctica and catching up with reading, including an *excellent* paper by one of our PhD students on #DeepLearning applications, soon to be submitted.
Not for first time I'm struck that 1) there is a genuine #AI revolution in climate 2)It's mostly based on ERA-5 reanalysis.
Not for first time I'm struck that 1) there is a genuine #AI revolution in climate 2)It's mostly based on ERA-5 reanalysis.
December 25, 2025 at 11:07 PM
Storm bound in Antarctica and catching up with reading, including an *excellent* paper by one of our PhD students on #DeepLearning applications, soon to be submitted.
Not for first time I'm struck that 1) there is a genuine #AI revolution in climate 2)It's mostly based on ERA-5 reanalysis.
Not for first time I'm struck that 1) there is a genuine #AI revolution in climate 2)It's mostly based on ERA-5 reanalysis.
Via #OPG_AO: Suppressing catastrophic forgetting in deep-learning-based imaging through scattering media https://bit.ly/43hKyoS #DeepLearning #CatastrophicForgetting 💡⚛️
May 25, 2026 at 6:10 PM
Via #OPG_AO: Suppressing catastrophic forgetting in deep-learning-based imaging through scattering media https://bit.ly/43hKyoS #DeepLearning #CatastrophicForgetting 💡⚛️
🤖 NVIDIA pushes tile-based GPU programming for efficient computing
NVIDIA is promoting tile based GPU programming to improve computing efficiency in AI models. This strategy is evident in recent tutorials...
#AIInference #DeepLearning #OperationalEfficiency #AI #AIPulse
NVIDIA is promoting tile based GPU programming to improve computing efficiency in AI models. This strategy is evident in recent tutorials...
#AIInference #DeepLearning #OperationalEfficiency #AI #AIPulse
Read the full article →
www.synestesia.uk
July 12, 2026 at 6:40 AM
🤖 NVIDIA pushes tile-based GPU programming for efficient computing
NVIDIA is promoting tile based GPU programming to improve computing efficiency in AI models. This strategy is evident in recent tutorials...
#AIInference #DeepLearning #OperationalEfficiency #AI #AIPulse
NVIDIA is promoting tile based GPU programming to improve computing efficiency in AI models. This strategy is evident in recent tutorials...
#AIInference #DeepLearning #OperationalEfficiency #AI #AIPulse
After years of development, collaboration, and hard work, it's finally here!
Now published in Bioinformatics: Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics academic.oup.com/bioinformati...
#TeamMassSpec #DeepLearning #AI #Proteomics
Now published in Bioinformatics: Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics academic.oup.com/bioinformati...
#TeamMassSpec #DeepLearning #AI #Proteomics
Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics
AbstractMotivation. Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and c
academic.oup.com
July 8, 2026 at 8:44 PM
After years of development, collaboration, and hard work, it's finally here!
Now published in Bioinformatics: Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics academic.oup.com/bioinformati...
#TeamMassSpec #DeepLearning #AI #Proteomics
Now published in Bioinformatics: Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics academic.oup.com/bioinformati...
#TeamMassSpec #DeepLearning #AI #Proteomics
@johannesrahlf.bsky.social presented #ForestSens at the #ANDSummit2025 at Oracle in Redwood Shores, CA!
🌲 LiDAR-based tree segmentation
🌳 Drone-driven tree species classification
🔥 Forest disturbance and damage detection
#AI #LiDAR #forestmanagement #precisionforestry #deeplearning
📷 Jean Ihm
🌲 LiDAR-based tree segmentation
🌳 Drone-driven tree species classification
🔥 Forest disturbance and damage detection
#AI #LiDAR #forestmanagement #precisionforestry #deeplearning
📷 Jean Ihm
April 10, 2025 at 9:09 AM
@johannesrahlf.bsky.social presented #ForestSens at the #ANDSummit2025 at Oracle in Redwood Shores, CA!
🌲 LiDAR-based tree segmentation
🌳 Drone-driven tree species classification
🔥 Forest disturbance and damage detection
#AI #LiDAR #forestmanagement #precisionforestry #deeplearning
📷 Jean Ihm
🌲 LiDAR-based tree segmentation
🌳 Drone-driven tree species classification
🔥 Forest disturbance and damage detection
#AI #LiDAR #forestmanagement #precisionforestry #deeplearning
📷 Jean Ihm
There's a lot of interest in #deeplearning for tree crown segmentation from imagery, but most methods are tested in manual annotations, checked visually.
How well do these perform on rigourously ground-truthed data? Our new preprint, by Matt Allen finds...not so well.🧵
arxiv.org/abs/2503.14273
How well do these perform on rigourously ground-truthed data? Our new preprint, by Matt Allen finds...not so well.🧵
arxiv.org/abs/2503.14273
Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on Closed Canopy: Validation Using TLS
Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based forest inventories ar...
arxiv.org
March 19, 2025 at 2:07 PM
There's a lot of interest in #deeplearning for tree crown segmentation from imagery, but most methods are tested in manual annotations, checked visually.
How well do these perform on rigourously ground-truthed data? Our new preprint, by Matt Allen finds...not so well.🧵
arxiv.org/abs/2503.14273
How well do these perform on rigourously ground-truthed data? Our new preprint, by Matt Allen finds...not so well.🧵
arxiv.org/abs/2503.14273
🤖 Rule-based planning boosts autonomous vehicle navigation accuracy
Incorporating rule based planning into neuro symbolic driving frameworks improves autonomous vehicle navigation performance by reducing average distance...
#DeepLearning #ImitationLearning #Robotics #AI #AIPulse
Incorporating rule based planning into neuro symbolic driving frameworks improves autonomous vehicle navigation performance by reducing average distance...
#DeepLearning #ImitationLearning #Robotics #AI #AIPulse
Read the full article →
www.synestesia.uk
June 24, 2026 at 1:36 PM
🤖 Rule-based planning boosts autonomous vehicle navigation accuracy
Incorporating rule based planning into neuro symbolic driving frameworks improves autonomous vehicle navigation performance by reducing average distance...
#DeepLearning #ImitationLearning #Robotics #AI #AIPulse
Incorporating rule based planning into neuro symbolic driving frameworks improves autonomous vehicle navigation performance by reducing average distance...
#DeepLearning #ImitationLearning #Robotics #AI #AIPulse
Homomorphic encryption was integrated into deep learning models for CT-based renal mass classification https://doi.org/10.1148/ryai.240798 #DeepLearning #kidneys #HomomorphicEncryption
November 25, 2025 at 7:15 AM
Homomorphic encryption was integrated into deep learning models for CT-based renal mass classification https://doi.org/10.1148/ryai.240798 #DeepLearning #kidneys #HomomorphicEncryption
Dentate nucleus segmentation tool using #DeepLearning applied to brain MRI-based quantitative susceptibility mapping images https://doi.org/10.1148/ryai.240478 #BIRAC25 #radiology
November 20, 2025 at 3:15 AM
Dentate nucleus segmentation tool using #DeepLearning applied to brain MRI-based quantitative susceptibility mapping images https://doi.org/10.1148/ryai.240478 #BIRAC25 #radiology
NEW!
StomaGAN: Improving image-based analysis of #stomata through Generative Adversarial Networks
by Jonathon Gibbs and Alexandra Gibbs
#PlantScience #DeepLearning #Phenotyping
StomaGAN: Improving image-based analysis of #stomata through Generative Adversarial Networks
by Jonathon Gibbs and Alexandra Gibbs
#PlantScience #DeepLearning #Phenotyping
February 24, 2025 at 3:59 PM
NEW!
StomaGAN: Improving image-based analysis of #stomata through Generative Adversarial Networks
by Jonathon Gibbs and Alexandra Gibbs
#PlantScience #DeepLearning #Phenotyping
StomaGAN: Improving image-based analysis of #stomata through Generative Adversarial Networks
by Jonathon Gibbs and Alexandra Gibbs
#PlantScience #DeepLearning #Phenotyping
VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts. #MicrobialPathogenicity #DeepLearning #BriefingsinBioinformatics #Bioinformatics 🧬 🖥️
academic.oup.com/bib/article/...
academic.oup.com/bib/article/...
June 16, 2025 at 1:49 PM
VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts. #MicrobialPathogenicity #DeepLearning #BriefingsinBioinformatics #Bioinformatics 🧬 🖥️
academic.oup.com/bib/article/...
academic.oup.com/bib/article/...
Enhancing plant morphological trait identification in #herbarium collections through deep learning–based segmentation
New #AppsPlantSci research by Ariouat, Sklab, Prifti, Zucker & Chenin
bsapubs.onlinelibrary.wiley.com/doi/full/10.... @ird-fr.bsky.social #botany #DeepLearning
New #AppsPlantSci research by Ariouat, Sklab, Prifti, Zucker & Chenin
bsapubs.onlinelibrary.wiley.com/doi/full/10.... @ird-fr.bsky.social #botany #DeepLearning
February 18, 2025 at 3:48 PM
Enhancing plant morphological trait identification in #herbarium collections through deep learning–based segmentation
New #AppsPlantSci research by Ariouat, Sklab, Prifti, Zucker & Chenin
bsapubs.onlinelibrary.wiley.com/doi/full/10.... @ird-fr.bsky.social #botany #DeepLearning
New #AppsPlantSci research by Ariouat, Sklab, Prifti, Zucker & Chenin
bsapubs.onlinelibrary.wiley.com/doi/full/10.... @ird-fr.bsky.social #botany #DeepLearning
#DeepLearning based #genomic prediction could hold the key to improved #plant #breeding programs, but it's not without challenges. Gao et al. review key obstacles and future directions.
Explore their findings here!⬇️🔓
doi.org/10.1111/jipb...
#PlantScience #OpenAccess
Explore their findings here!⬇️🔓
doi.org/10.1111/jipb...
#PlantScience #OpenAccess
May 19, 2025 at 1:03 PM
#DeepLearning based #genomic prediction could hold the key to improved #plant #breeding programs, but it's not without challenges. Gao et al. review key obstacles and future directions.
Explore their findings here!⬇️🔓
doi.org/10.1111/jipb...
#PlantScience #OpenAccess
Explore their findings here!⬇️🔓
doi.org/10.1111/jipb...
#PlantScience #OpenAccess
ONLINE NOW! @naturecancer.bsky.social
'Spatially resolved transcriptomics and graph-based #deeplearning improve accuracy of routine #CNStumor #diagnostics'
✏️By Dieter Henrik Heiland & Felix Sahm and colleagues
🔗 www.nature.com/articles/s43...
#spatialtranscriptomics
'Spatially resolved transcriptomics and graph-based #deeplearning improve accuracy of routine #CNStumor #diagnostics'
✏️By Dieter Henrik Heiland & Felix Sahm and colleagues
🔗 www.nature.com/articles/s43...
#spatialtranscriptomics
Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics - Nature Cancer
Ritter et al. present a spatial transcriptomics and deep learning-based approach named NePSTA (neuropathology spatial transcriptomic analysis) and leverage it to improve neuropathological diagnostics ...
www.nature.com
February 12, 2025 at 4:13 PM
ONLINE NOW! @naturecancer.bsky.social
'Spatially resolved transcriptomics and graph-based #deeplearning improve accuracy of routine #CNStumor #diagnostics'
✏️By Dieter Henrik Heiland & Felix Sahm and colleagues
🔗 www.nature.com/articles/s43...
#spatialtranscriptomics
'Spatially resolved transcriptomics and graph-based #deeplearning improve accuracy of routine #CNStumor #diagnostics'
✏️By Dieter Henrik Heiland & Felix Sahm and colleagues
🔗 www.nature.com/articles/s43...
#spatialtranscriptomics
🤖 AI-Powered Agent-Based Modeling Adapts to Real-Time Changes
Researchers are increasingly leveraging large language models to improve the adaptability of agent based modeling simulations. A recent arXiv preprint...
#PredictiveModels #DeepLearning #RealTimePrediction #AI #AIPulse
Researchers are increasingly leveraging large language models to improve the adaptability of agent based modeling simulations. A recent arXiv preprint...
#PredictiveModels #DeepLearning #RealTimePrediction #AI #AIPulse
Read the full article →
www.synestesia.uk
July 10, 2026 at 5:40 PM
🤖 AI-Powered Agent-Based Modeling Adapts to Real-Time Changes
Researchers are increasingly leveraging large language models to improve the adaptability of agent based modeling simulations. A recent arXiv preprint...
#PredictiveModels #DeepLearning #RealTimePrediction #AI #AIPulse
Researchers are increasingly leveraging large language models to improve the adaptability of agent based modeling simulations. A recent arXiv preprint...
#PredictiveModels #DeepLearning #RealTimePrediction #AI #AIPulse
Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models https://doi.org/10.1148/ryai.240502 @tum.de #segmentation #DeepLearning #MachineLearning
February 2, 2026 at 3:15 PM
Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models https://doi.org/10.1148/ryai.240502 @tum.de #segmentation #DeepLearning #MachineLearning
Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions. #DNAsequence #DeepLearning #ReadsToImages #MobileElementInsertions #LowMosaicism #Genomics #Bioinformatics @natcomms.nature.com 🧬 🖥️ 🧪
www.nature.com/articles/s41...
www.nature.com/articles/s41...
October 18, 2025 at 1:15 PM
Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions. #DNAsequence #DeepLearning #ReadsToImages #MobileElementInsertions #LowMosaicism #Genomics #Bioinformatics @natcomms.nature.com 🧬 🖥️ 🧪
www.nature.com/articles/s41...
www.nature.com/articles/s41...
In this paper is proposed a novel search-based compilation method that incorporates energy metrics into the search process to generate energy-efficient GPU kernels, achieving up to 21.69% reduced energy consumption, while maintaining good latency performance. #DeepLearning
arxiv.org/pdf/2411.18873
arxiv.org/pdf/2411.18873
December 2, 2024 at 7:25 AM
In this paper is proposed a novel search-based compilation method that incorporates energy metrics into the search process to generate energy-efficient GPU kernels, achieving up to 21.69% reduced energy consumption, while maintaining good latency performance. #DeepLearning
arxiv.org/pdf/2411.18873
arxiv.org/pdf/2411.18873
Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models https://doi.org/10.1148/ryai.240502 @tum.de #DeepLearning #AI #MachineLearning
February 10, 2026 at 3:15 AM
Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models https://doi.org/10.1148/ryai.240502 @tum.de #DeepLearning #AI #MachineLearning