Building the Model – WEKA

I have obtained the Molecular Properties of both Actives and Inactives using PowerMV.

Having this as the Input , I am trying to build a MODEL using MACHINE LEARNING METHOD known as WEKA.

• Weka is to apply a learning method to a dataset and analyze its output to learn more about the data.
• Weka is to use learned models to generate predictions on new instances.
• Third is to apply several different learners and compare their performance in order to choose one for prediction. The learning methods are called classifiers.

Using Random Forest as the Classifier I have developed many Models. Random Forest algorithm is based on decision trees, where each tree is independently constructed and each node is split using the best among a subset of predictors randomly chosen at the node. It is the most accurate classifier and produces most precise results for all the datasets.

One of the issue that needs to be taken into consideration while using machine learning technique on a highly imbalanced dataset is the cost of misclassification.The use of cost-sensitive classifiers can abrogate this issue and minimize misclassification errors.

Using the statistical measures I am still trying to choose the  best accurate model for my study.

Hope i ll finish it soon …

Bioassay dataset

I have collected the bioassay dataset of Confirmation Assay for Inhibitors of Leishmania Mexicana Pyruvate Kinase (LmPK) , AID 2559.

It consists of 150 compound totally.

In that 58 are Actives

67 are Inactives and

25 are Inconclusive.

Collection of Bioassay datasets for Pyruvate Kinase in Leishmaniasis

Id	TOTAL NO OF COMPOUNDS	ACTIVE	INACTIVE	INCONCLUSIVE	UNSPECIFIED
AID_945	1279	22	1234	23
AID_959	1279	21	1233	20	5
AID_1721	293196	1086	290104	2003
AID_624173	410870	494	404618	5758
AID_720569	889	597	74	218
AID_720584	889	553	127	209
AID_2533	244	86	116	42
AID_2530	253	46	197	15

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