Samad Jahandideh

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BACKGROUND Current protocols yield crystals for <30% of known proteins, indicating that automatically identifying crystallizable proteins may improve high-throughput structural genomics efforts. We introduce CRYSTALP2, a kernel-based method that predicts the propensity of a given protein sequence to produce diffraction-quality crystals. This method utilizes(More)
In order to establish novel hybrid neural discriminant model, linear discriminant analysis (LDA) was used at the first stage to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies for 498 proteins came from(More)
Regarding the great potential of dual binding site inhibitors of acetylcholinesterase as the future potent drugs of Alzheimer's disease, this study was devoted to extraction of the most effective structural features of these inhibitors from among a large number of quantitative descriptors. To do this, we adopted a unique approach in quantitative(More)
MOTIVATION So far various statistical and machine learning techniques applied for prediction of beta-turns. The majority of these techniques have been only focused on the prediction of beta-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of beta-turn. RESULTS A two-stage hybrid model developed to(More)
Due to the slightly success of protein secondary structure prediction using the various algorithmic and non-algorithmic techniques, similar techniques have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12,(More)
Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat(More)
Regarding the fact that the protein structure is principally encoded in its sequence, investigating the bonding state of cysteine has gained a great deal of attention due to its significance in the formation of protein structure. Due to lack of evident influence of free cysteines on the protein structure, it may be expected that only half-cystines convey(More)
Bioeffects of magnetic field exposure have been motivated accomplishing various studies. However, no consensus or guideline is available for experimental designs relating exposure conditions as yet. In the present work, in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF),(More)
One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research. To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in(More)
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