Rupashree Dass

Machine Learning Scientist in Bioinformatics at Computomics.

[AGBT Meeting @agbt]

Abstract Select: @DassRupashree from @computomics discusses machine learning-based phenotype prediction to help in detecting significantly associated genetic markers at @AGBT Agriculture 2023 #AGBTAg #AGBTAg23

[AGBT Meeting @agbt]

Full House! Great group of hundreds of scientists and experts from around the globe here on Day 1 at @AGBT Agriculture 2023. #AGBTAg #AGBTAg23

[Frontiers - Plant Science @FrontPlantSci]

New Research: A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species: Genomic selection is an integral tool for breeders to accurately select plants directly… https://t.co/PFZKgCUGKB #PlantScience #PlantSci

[taiyeb zahir @TaiyebZZ]

Christmas cookies in @lab_maier are definitely a success. @g_hammeranne demonstrating top notch microbiology and baking skills.

[Mateusz Urbanczyk @urbanczykmat]

Having troubles identifying your metabolites in the biological material by NMR? Use the temperature coefficients! Check our new work at RSC Advances:
https://t.co/UuqE93a1Sb
#NMRchat #NMR @RSCAdvances @ICHF_PAN @UniWarszawski

[taiyeb zahir @TaiyebZZ]

And they don't give tracking barcodes so there is no way to tell which sample belongs to whom. They assume that the registration queue is exactly in same order as the testing queue.

[taiyeb zahir @TaiyebZZ]

Took a #Schnelltest at #Heidelberg Hauptbahnhof. Total fraud and money making business. Gave two samples received result of one.

[ICMRBS-ECR @ICMRBS_ECR]

📢 The next @ICMRBS-ECR webinar will be this Wednesday (May 5⃣th) with talks by @Borja_ml, @DassRupashree, and @angusjrobertson

We will start at 9am EDT / 2pm BST / 3pm CEST / 4pm IDT / 6:30pm IST / 9pm CST / 11pm AEDT

See you there! 🧲 #nmrchat

[Rupashree Dass @DassRupashree]

Our newly developed #NMR method proves that impeded hydrogen exchange rates in alpha-synuclein is a manifestation of the electrochemical potential #protein @iNANO_AarhusUni

[bioRxiv Biophysics @biorxiv_biophys]

The contribution of electrostatics to hydrogen exchange in the unfolded protein state https://t.co/gfg8xuiRcF #biorxiv_biophys

[Magnus Kjaergaard @ProteinMagnus]

The server is available via Frans’ homepage using his usual 90’ies web design:
https://t.co/PLtgUDrwO6

[Magnus Kjaergaard @ProteinMagnus]

If you are want to determine secondary structure propensities of proteins by #NMR you should check out this pre-print and accompanying webserver from Frans Mulder (@iNANO_AarhusUni):
https://t.co/DW7o35Q5Xj

[Magnetic Resonance News @nmr900]

PhD Student in the Laboratory of NMR Spectroscopy – Centre of New Technologies, the University of Warsaw
“New dimensions in NMR for better chemical analysis – from small molecules to proteins" Dr. Krzysztof Kazimierczuk https://t.co/AkJSfwVqZy #NMRjobs #NMRchat #NMR

[Rupashree Dass @DassRupashree]

In the hunt for the dark matter of the protein world, we are now able to comprehensively predict disorder in #proteins from sequence using #AI and #NMR data implemented in #ODiNPred.
https://t.co/fDdkWrvOE8
https://t.co/tRry5tdwbH
#aarhusuniversity #data #inano

[Magnus Kjaergaard @ProteinMagnus]

Can protein disorder predictors be improved by training them on better data? This is the question my @iNANO_AarhusUni colleagues ask in their new paper in @SciReports, which describe their new predictor ODiNPred.

https://t.co/Dqe0Qy32R6

[Magnus Kjaergaard @ProteinMagnus]

ODINPred is beaten narrowly by SPOT disorder as the best predictor that considers evolutionary info, but for the much faster prediction without evolutionary info it outperforms the current best predictor:

[Magnus Kjaergaard @ProteinMagnus]

Dass et al. train a neural network on a data set of chemical shift assignments of 1325 proteins, and the resulting predictor is publicly available as a web server at:

https://t.co/CIpAt1lwz9