Protein cysteine thiols can be divided into four groups based on their reactivities: those that form permanent structural disulfide bonds, those that coordinate with metals, those that remain in the reduced state, and those that are susceptible to reversible oxidation. Physicochemical parameters of oxidationsusceptible protein thiols were organized into a database named the Balanced Oxidation Susceptible Cysteine Thiol Database (BALOSCTdb). BALOSCTdb contains 161 cysteine thiols that undergo reversible oxidation and 161 cysteine thiols that are not susceptible to oxidation. Each cysteine was represented by a set of 12 parameters, one of which was a label (1/0) to indicate whether its thiol moiety is susceptible to oxidation. A computer program (the C4.5 decision tree classifier re-implemented as the J48 classifier) segregated cysteines into oxidation-susceptible and oxidation-non-susceptible classes. The classifier selected three parameters critical for prediction of thiol oxidation susceptibility: (1) distance to the nearest cysteine sulfur atom, (2) solvent accessibility, and (3) pKa. The classifier was optimized to correctly predict 136 of the 161 cysteine thiols susceptible to oxidation. Leave-one-out cross-validation analysis showed that the percent of correctly classified cysteines was 80.1% and that 16.1% of the oxidation-susceptible cysteine thiols were incorrectly classified. The algorithm developed from these parameters, named the Cysteine Oxidation Prediction Algorithm (COPA), is presented here. COPA prediction of oxidation-susceptible sites can be utilized to locate protein cysteines susceptible to redox-mediated regulation and identify possible enzyme catalytic sites with reactive cysteine thiols.Keywords: cysteine; thiol; oxidation; prediction; C4.5; J48; redox; classifier; decision tree Protein cysteine thiols can be divided into four broad categories: those that form permanent structural disulfide bonds, those that coordinate metals, those that are permanently in the reduced state, and those that are reversibly oxidized. Permanent structural disulfide bonds are formed during the folding process by oxidizing enzymes (for example, DsbA in bacteria and protein disulfide isomerase in eukaryotes) (Kadokura et al. 2003;Maattanen et al. 2006). Permanent structural disulfide bonds are typically observed in oxidizing environments such as extracellular spaces and the endoplasmic reticulum. Protein cysteine thiols can be coordinated to metal ions, typically iron, copper, or zinc. Metal coordinated thiols are found in oxidizing environments and in the cytosolic compartment of the cell. The remaining protein cysteine thiols in the cytosol are either permanently reduced or are susceptible to reversible oxidation (Thomas et al. 1995). The reversibly oxidized protein thiols (ROPTs) in the cytosol are often required for enzyme catalysis or for regulation of protein activity (Finkel 2003;Linke and Jakob 2003).Reprint requests to: Jamil Momand, Department of Chemistry and Biochemistry, California State Univ...

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The MapReduce approach has been popular in computing large scale data since Google implemented its platform on Google Distributed File Systems (GFS) followed by Amazon Web Service (AWS) providing the Apache Hadoop platform in inexpensive computing nodes. Map/Reduce motivates to redesign and convert the existing sequential algorithms to MapReduce as restricted parallel programming so that the paper proposes Market Basket Analysis algorithm with MapReduce as well as apriority property. Two algorithms are proposed by adapting an existing Apriori-algorithm and building a simple algorithm that sorts data sets and converts it to (key, value) pairs to fit with MapReduce. It is executed on Amazon EC2 Map/Reduce platform. The experimental results show that the Apriori-algorithm does not perform as well as the simple algorithm. Using the simple algorithm, the code with Map/Reduce increases the performance by adding more nodes, but at a certain point there is a bottleneck that does not allow further performance gain. It is believed that the operations of distributing, aggregating, and reducing data in Map/Reduce, cause the bottleneck.

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This paper aims to apply several machine learning (ML) models to the massive dataset present in the area of e‐commerce from Amazon to analyze and predict ratings and to recommend products. For this purpose, we have used both traditional and Big Data algorithms. As the Amazon product review dataset is large, we present Big Data architecture suitable massive dataset for storing and computation, which is not possible with the traditional architecture. Furthermore, the dataset contains 15 attributes and has about 7 million records. With the dataset, we develop several models in Oracle Big Data and Azure Cloud Computing services to predict the review rating and recommendation for the items at Amazon. We present a comparative conclusion in terms of the accuracy as well as the efficiency with Spark ML—the Big Data architecture, and Azure ML—the traditional architecture. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Technologies > Prediction

Accurate estimation of neuronal count and distribution is central to the understanding of the organization and layout of cortical maps in the brain, and changes in the cell population induced by brain disorders. High-throughput 3D microscopy techniques such as Knife-Edge Scanning Microscopy (KESM) are enabling whole-brain survey of neuronal distributions. Data from such techniques pose serious challenges to quantitative analysis due to the massive, growing, and sparsely labeled nature of the data. In this paper, we present a scalable, incremental learning algorithm for cell body detection that can address these issues. Our algorithm is computationally efficient (linear mapping, non-iterative) and does not require retraining (unlike gradient-based approaches) or retention of old raw data (unlike instance-based learning). We tested our algorithm on our rat brain Nissl data set, showing superior performance compared to an artificial neural network-based benchmark, and also demonstrated robust performance in a scenario where the data set is rapidly growing in size. Our algorithm is also highly parallelizable due to its incremental nature, and we demonstrated this empirically using a MapReduce-based implementation of the algorithm. We expect our scalable, incremental learning approach to be widely applicable to medical imaging domains where there is a constant flux of new data.

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