Write-up of the Predictive Modelling Competition Results

[edwintse]

I am currently in the process of writing up a summary of the results that we obtained from the first modelling competition (#538) for my thesis and for the eventual paper on this work.

To get this started it would be super helpful if the entrants from the competition (@spadavec, @holeung, @gcincilla, @kellerberrin, @IamDavyG, @jon-c-silva) were able to provide a brief summary description about the models you developed for a broader audience to understand.

There is a Google Sheet here where a new column could be added for this description (in addition to the Summary column already there).

[holeung]

Great! I'm out of town at the moment but will get to it when I return.

[gcincilla]

Very good. How long should be the description? If you can give us the max number of words/characters as a reference, maybe the descriptions will be more homogeneous.

[edwintse]

Probably around 30 words for each description. It would be good to have information about each model tabulated for the paper.

[kellerberrin]

Hi Edwin, Thanks for your email. As requested Please find below a brief description of my entry. *The rankings are based on an ensemble of Deep Neural Network models trained on molecular information obtained from the **pharmacophore **Dragon freeware..* *The Dragon software converted compound molecular SDF records (SMILES) into 1666 relevant pharmacophore fields for all compounds. * *These fields were normalized and submitted to a conventional (dense) Deep Neural Network (Keras/TensorFlow) for training and analysis using the example compounds provided.* *The DNNs were classifiers and were trained on the 3 reported PfATP4 activity classes; [ACTIVE, PARTIAL, INACTIVE] for each of the example compounds.* *The trained DNNs exhibited a number of different optimal classifications (local minina) for the example compounds during training.* *So the different optimal classification DNNs (without further training) were then combined into an ensemble using a small Neural Network meta-classifier to produce a final ranking of the test compounds.* *This ranking was expressed as the probability that a compound would be in the [ACTIVE] activity class. * I hope this brief description satisfies your request. Please contact me if you have any further queries about the above. Best Regards, James.

…

On Thu, 30 May 2019 at 23:09, Edwin Tse ***@***.***> wrote: I am currently in the process of writing up a summary of the results that we obtained from the first modelling competition (#538 <OpenSourceMalaria/OSM_To_Do_List#538>) for my thesis and for the eventual paper on this work. To get this started it would be super helpful if the entrants from the competition ***@***.*** <https://github.com/spadavec>, @holeung <https://github.com/holeung>, @gcincilla <https://github.com/gcincilla>, @kellerberrin <https://github.com/kellerberrin>, @IamDavyG <https://github.com/IamDavyG>, @jon-c-silva) were able to provide a brief summary description about the models you developed for a broader audience to understand. There is a Google Sheet here <https://docs.google.com/spreadsheets/d/1pY6sYXIw66jnzUO3CoP8HceYdDjLRvwg5_pLkBY1Wek/edit#gid=0> where a new column could be added for this description (in addition to the Summary column already there). — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#70?email_source=notifications&email_token=ABXY3BMYQCP6R4OHYRFXSD3PX7U37A5CNFSM4HRF6IL2YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4GWX4M3Q>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABXY3BKWFHVWWPSCFXS37Q3PX7U37ANCNFSM4HRF6ILQ> .

-- Best Regards, James

[edwintse]

Closing. Taken onto the predictive modelling repo here