biokit | pypi | Package Usage | Ecosyste.ms: Repos

dreamtools/dreamtools

Code sharing related to DREAM challenges

* requirements.txt
* setup.py

Size: 7.1 MB - Last synced: 19 days ago - Pushed: about 7 years ago

CancerRxGene/gdsctools

Tools related to the Genomics of Drug Sensitivity in Cancer (GDSC) projects (http://www.cancerrxgene.org/ )

==0.4.1 requirements.txt

Size: 14.3 MB - Last synced: 3 days ago - Pushed: over 2 years ago

Disonay/NNmnist

0.5.0 poetry.lock
^0.5.0 pyproject.toml

Size: 157 KB - Last synced: about 1 year ago - Pushed: about 2 years ago

cellnopt/cellnopt

Training of logic models of signalling networks using prior knowledge networks and perturbation data.

* setup.py

Size: 11.3 MB - Last synced: 25 days ago - Pushed: about 4 years ago

petercombs/dicty

==0.4.2 requirements.txt

Size: 1.78 MB - Last synced: about 1 year ago - Pushed: over 1 year ago

Cloud-PG/smart-cache

A project to develop and test solutions for smart caching

==0.4.6 Probe/requirements.txt

Size: 32.6 MB - Last synced: 12 months ago - Pushed: 12 months ago

nxwi/illacceptanything Fork of illacceptanything/illacceptanything

The project where literally anything* goes.

* data/requirements.txt

Size: 1.33 GB - Last synced: 7 months ago - Pushed: over 1 year ago

nandigamchandu/netlify

==0.4.2 requirements.txt

Last synced: over 1 year ago

saomyachaudhury/WESAD_Sensor_Data_Analysis

==0.5.0 requirements.txt

Size: 31.3 MB - Last synced: about 1 year ago - Pushed: over 1 year ago

illacceptanything/illacceptanything

The project where literally anything* goes.

* data/requirements.txt

Size: 1.47 GB - Last synced: 10 days ago - Pushed: 10 days ago

THZ34/biokit

>=0.0.1.dev6 requirements.txt

Size: 30.8 MB - Last synced: 14 days ago - Pushed: 14 days ago

The development of advection–dispersion particle transport models (PTM) for transport of nanoparticles in porous media has focused on improving model fit by inclusion of empirical parameters. However, this has done little to disentangle the complex behavior of nanoparticles in porous media and to provide mechanistic insights into nanoparticle transport. The most prominent limitation of current PTMs is that they do not consider the influence of physicochemical conditions of the experiments on the transport of nanomaterials. Here, we overcome this limitation by bypassing traditional advection–dispersion PTMs and relating the physicochemical conditions of the experiments to the experimental outcome using ensemble machine-learning methods. We identify a small set of factors that seem to determine the transport of nanoparticles in column experiments by recursive feature elimination with cross validation. .

==0.0.5 requirements.txt

Size: 363 KB - Last synced: about 2 months ago - Pushed: over 8 years ago

ntchambers/illacceptanything

The project where literally anything* goes

* requirements.txt

Last synced: over 1 year ago

doaa-altarawy/dreamtools Fork of dreamtools/dreamtools

Code sharing related to DREAM challenges

* setup.py

Size: 7.06 MB - Last synced: 9 months ago - Pushed: over 8 years ago

yyx5048/Cubic-Perovskite-WorkFunction

==0.4.4 requirements.txt

Size: 4.19 MB - Last synced: 5 months ago - Pushed: over 3 years ago

eli-s-goldberg/rfecvNano

The development of advection–dispersion particle transport models (PTM) for transport of nanoparticles in porous media has focused on improving model fit by inclusion of empirical parameters. However, this has done little to disentangle the complex behavior of nanoparticles in porous media and to provide mechanistic insights into nanoparticle transport. The most prominent limitation of current PTMs is that they do not consider the influence of physicochemical conditions of the experiments on the transport of nanomaterials. Here, we overcome this limitation by bypassing traditional advection–dispersion PTMs and relating the physicochemical conditions of the experiments to the experimental outcome using ensemble machine-learning methods. We identify a small set of factors that seem to determine the transport of nanoparticles in column experiments by recursive feature elimination with cross validation. .