Kudisthalert, Wasu
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Item type:Publication, A coefficient comparison of weighted similarity extreme learning machine for drug screening(2016-03-23); Machine learning techniques are becoming popular in drug discovery process. It can be used to predict the biological activities of compounds. This paper focuses on virtual screening task. We proposed the Weighted Similarity Extreme Learning Machine algorithm (WELM). It is based on Single Layer Feedforward Neural Network. The algorithm is powerful, iteratively free, and easy to program. In this work, we compared the performance of 17 different types of coefficients with WELM on a well-known dataset in the area of virtual screening named Maximum Unbiased Validation dataset. Moreover, the WELM with different types of coefficients were also compared with the conventional technique-similarity searching. WELM together with Jaccard/Tanimoto were able to achieve the best results on average in most of the activity classes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Clustering-based weighted extreme learning machine for classification in drug discovery process(2016-01-01); Extreme Learning Machine (ELM) is a universal approximation method that is extremely fast and easy to implement, but the weights of the model are normally randomly selected so they can lead to poor prediction performance. In this work, we applied Weighted Similarity Extreme Learning Machine in combination with Jaccard/Tanimoto (WELM-JT) and cluster analysis (namely, k-means clustering and Support Vector Clustering) on similarity and distance measures (i.e., Jaccard/ Tanimoto and Euclidean) in order to predict which compounds with not-so-different chemical structures have an activity for treating a certain symptom or disease. The proposed method was experimented on one of the most challenging datasets named Maximum Unbiased Validation (MUV) dataset with 4 different types of fingerprints (i.e. ECFP 4, ECFP 6, FCFP 4 and FCFP 6). The experimental results show that WELM-JT in combination with k-means-ED gave the best performance. It retrieved the highest number of active molecules and used the lowest number of nodes. Meanwhile, WELM-JT with k-means-JT and ECFP 6 encoding proved to be a robust contender for most of the activity classes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Virtual screening by a new clustering-based weighted similarity extreme learning machine approach(2018-04-01); Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.