Closed AhmedYusuff closed 1 year ago
Hi @AhmedYusuff !
Thanks! Let's include this model in our model suggestions to be incorporated. Looking forward to your next suggestion!
I've Done that @GemmaTuron. Many thanks for the Assistance.
eToxPred
A Machine Learning Model that has been trained on different datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds, which gives it the ability to learn the relationships between chemical structures and their toxic effects, and Predict various types of Toxicity in molecules such as carcinogenicity potency, cardiotoxicity, endocrine disruption, mutagenicity, and acute oral toxicity.
The Inability to recognize relevant features and factors that leads to Toxicity in compounds due to the complex nature of chemicals is one of the challenges in developing a model that can accurately predict Toxicity in Molecules.
eToxPred makes use of DBN (Deep belief Network) a neural network built on multiple RBM (Restricted Boltzmann Machine ) nodes which consist of two (2) layers, the visible layer, and the hidden layer.
The visible layer represents the input data, while the hidden layer encodes higher-level representations of the input, all-together they serve as the building blocks of the deep belief networks.
This architecture allows for fast, layer-by-layer training allowing the model to learn the complex representation of Molecular structures and extract the most relevant and informative features from the compounds
etox-pred
Toxicity, Tox21, Fingerprints.
https://bmcpharmacoltoxicol.biomedcentral.com/articles/10.1186/s40360-018-0282-6
https://github.com/pulimeng/etoxpred
GPL-3.0
Hi @AhmedYusuff !
Actually etoxpred is already in our hub, if you check the website it should pop out! (eos92sw
). Let's find a third one that is not there :)
Hi @AhmedYusuff !
Actually etoxpred is already in our hub, if you check the website it should pop out! (
eos92sw
). Let's find a third one that is not there :)
Alright @GemmaTuron .
DETIRE (a hybrid Deep lEarning model for idenTifying vIral sequences fRom mEtagenomes)
Virus Detection from metagenomes can be quite complicated and difficult, as genetic materials from other organisms are often present in the environmental samples collected.
This model is a combination of various deep-learning techniques, a graph convolutional network (GCN) based sequence embedder, and a two-path deep-learning model which are the Convolutional neural networks (CNN) and BiLSTM which is a type of recurrent neural network (RNN). The Model is leveraging on these various combinations to create a Hybrid deep-learning model that could detect viruses directly from metagenomics sequences.
det-ire
Target identification.
https://www.biorxiv.org/content/10.1101/2021.11.19.469211v1.full
https://github.com/crazyinter/DETIRE
Proprietary
Hi @AhmedYusuff !
Thanks, this model is focused on genomic data, therefore at this stage of the Ersilia Model Hub development we cannot tackle it, though we will certainly be doing it in the near future! I'd say this model license is Non-Commercial right? they do give free access for non commercial applications
Next steps:
Hi @AhmedYusuff !
Thanks, this model is focused on genomic data, therefore at this stage of the Ersilia Model Hub development we cannot tackle it, though we will certainly be doing it in the near future! I'd say this model license is Non-Commercial right? they do give free access for non commercial applications
Next steps:
- Can you add your first suggestion, the dff-nds model, in the Ersilia list if you haven't yet?
- Help out interns who are starting late in the contribution period set up
- Can you have a look at this issue and update if there is still the same bug in the model, and we'll take it from there?
- Next week start preparing the application letter!
Many thanks for the feedback @GemmaTuron. Yes, the Model License is Non-Commercial, And dff-nds has been added to the suggestion list. I'll also Proceed with the other Task.
To keep track of the contributions, @AhmedYusuff worked on issue #387 Can you also check issues
To keep track of the contributions, @AhmedYusuff worked on issue #387 Can you also check issues #389 #371
Okay. I'll be on it.
@GemmaTuron, Week 4 task has been completed by submitting my final application.
I'll Continue working towards resolving the outstanding issue #389. Should I also add incorporating model dffn-dds into the Hub?
Hi @AhmedYusuff !
Thanks for your work, at this moment we cannot provide further support to contributors until the internship period, the model is already on the suggestions list so it will be tackled as soon as possible!
Thanks
Many thanks @GemmaTuron for all the assistance.
It has been a very rich experience learning from you. I will go ahead and close this issue as completed.
Week 1 - Get to know the community
Week 2 - Install and run an ML model
Week 3 - Propose new models
Week 4 - Prepare your final application