Monther Alhamdoosh

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MOTIVATION Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and(More)
Ensemble learning aims to improve the generalization power and the reliability of learner models through sampling and optimization techniques. It has been shown that an ensemble constructed by a selective collection of base learners outperforms favorably. However, effective implementation of such an ensemble from a given learner pool is still an open(More)
Negative correlation learning (NCL) aims to produce ensembles with sound generalization capability through controlling the disagreement among base learners' outputs. Such a learning scheme is usually implemented by using feed-forward neural networks with error back-propagation algorithms (BPNNs). However, it suffers from slow convergence, local minima(More)
The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation(More)
Our paper emphasizes the relevance of Extreme Learning Machine (ELM) in Bioinformatics applications by addressing the problem of predicting the disulfide connectivity from protein sequences. We test different activation functions of the hidden neurons and we show that for the task at hand the Radial Basis Functions are the best performing. We also show that(More)
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