Top Tweets for #BioTechNatureComms
HIT-EC is a hierarchical interpretable transformer for enzyme commission number prediction with sequence–function relationships. #BiotechNatureComms
https://t.co/NI7zrNGoCp
Evaluating single-cell ATAC-seq atlasing technologies using sequence-to-function modelling #BiotechNatureComms
@hannahdckmnkn
@steinaerts
https://t.co/HXJdKKt6JX
FLASH-MM is a scalable, memory efficient, and statistically robust method for cell-level differential expression analysis. #BiotechNatureComms
@garybader1
@DelaramPB
@DonnellyCentre
https://t.co/JX5eb9rUf7
Raichu preserves enhancer-promoter signals and improves the detection of regulatory chromatin loops across platforms and conditions #normalisation #3DGenome
#BiotechNatureComms
@XiaotaoWang3
@NatureComms
https://t.co/PEu7n6XeCp
MINT, trained on large #PPI datasets, outperforms existing protein language models in predicting binding, mutations, and immune interactions.
@lab_berger
#BiotechNatureComms
https://t.co/LRlak0K4q4
.@umichkim introduce a noise controller combined with antithetic integral feedback for robust single-cell level regulation. #BiotechNatureComms
https://t.co/WGDlsqm2mG
Authors use Restricted Boltzmann machines trained on natural sequences to design new riboswitch aptamer domains and validate their functionality via chemical probing.
@jorgefdcd
@NatureComms
#BiotechNatureComms
https://t.co/UXeqe88djQ
PhenoProfiler provides an end-to-end AI framework that converts high-content cellular images into quantitative phenotypic profiles. @NatureComms
@Sophie_QSong
#BiotechNatureComms
https://t.co/vrwPBrS0T3
.@kevin_tsia present MorphoGenie, an unsupervised model that profiles cell shapes to predict cellular heterogeneity without manual labels. @NatureComms
#BiotechNatureComms
https://t.co/ouvNYmNABq
CellMentor learns representations guided by cell-type labels, improving clustering accuracy and preserving meaningful biological variation. @NatureComms
@dvir_a
#BiotechNatureComms
https://t.co/BcQs4a1pOl
scDrugMap systematically compares foundation models for single-cell drug response prediction. @NatureComms
@Sophie_QSong
#BiotechNatureComms
https://t.co/TusxHte5ri
CASSIA: a multi agent AI system for automated, interpretable, and quality controlled cell type annotations.
@Xie227Xie
#BiotechNatureComms
https://t.co/xZUVlkOF49
MultiVeloVAE infers differential dynamics from multi-lineage, multi-omic, and multi-sample single-cell data. #BiotechNatureComms
@NatureComms
@LabWelch
https://t.co/CFg85zdBKr
DNALONGBENCH: a benchmark suite for long-range DNA prediction #DNAfoundationmodels #BiotechNatureComms
@WenduoC
@ZhenqiaoSong
@zocean636
@lileics
@jmuiuc
https://t.co/xvafxE1jNY
MIRO is a graph neural network method that enhances spatial clustering for single-molecule localisation microscopy datasets. #BiotechNatureComms
@jesuspicas
@carlomanzo78
@qubilab
@VolpeResearch
https://t.co/pfVqi0Nsem
SpatialMETA is a conditional variational autoencoder-based framework designed for the integration of spatial transcriptomics and metabolomics data.
@wanluliu
#BiotechNatureComms
#SpatialOmics
#Metabolomics
https://t.co/qkQ8a1OeTG
A causal framework HALO examines epigenetic plasticity and gene regulation dynamics in single-cell multi-omic data. #Chromatin_accessibility #BiotechNatureComms @NatureComms
https://t.co/RbfDgqgSYB
.@apsduk @uniofwarwick use a host-aware modelling framework to develop genetic controllers to sustain synthetic gene expression. @NatureComms
#synbio #engbio #BiotechNatureComms
https://t.co/vmZLJmA2cX
A #machinelearning strategy to predict the impact of metabolic gene deletions, offering high accuracy for gene essentiality across varied organisms and for many other phenotypes. @NatureComms
@doyarzunrod
#syntheticbiology
#BiotechNatureComms
https://t.co/eEA7kym7Nu
mcRigor detects and filters heterogeneous metacells, optimises metacell partitioning, and improves reliability in single-cell omics studies @NatureComms
@jsb_ucla
#BiotechNatureComms
https://t.co/HOOQM1477H
Last Seen Hashtags on Sotwe
Most Popular Users
Elon Musk 
@elonmusk
240.7M followers
Barack Obama
@barackobama
119.2M followers
Donald J. Trump
@realdonaldtrump
111.7M followers
Cristiano Ronaldo
@cristiano
110.8M followers
Narendra Modi
@narendramodi
107M followers
Rihanna
@rihanna
97.7M followers
NASA
@nasa
92.2M followers
Justin Bieber
@justinbieber
90.9M followers
KATY PERRY
@katyperry
87.8M followers
Taylor Swift
@taylorswift13
81.6M followers
Lady Gaga
@ladygaga
73.2M followers
Virat Kohli
@imvkohli
70.1M followers
Kim Kardashian
@kimkardashian
69.8M followers
YouTube
@youtube
68.7M followers
Bill Gates
@billgates
64M followers
Neymar Jr
@neymarjr
62.8M followers
The Ellen Show
@theellenshow
62.4M followers
CNN
@cnn
61.9M followers
Selena Gomez
@selenagomez
60.9M followers
X
@x
60.8M followers