An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction

A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories

Understanding the protein folding mechanism remains a grand challenge in structural biology. In the past several years, computational theories in molecular dynamics have been employed to shed light on the folding process. Coupled with high computing power and large scale storage, researchers now can computationally simulate the protein folding process in atomistic details at femtosecond temporal resolution. Such simulation often produces a large number of folding trajectories, each consisting of a series of 3D conformations of the protein under study. As a result, effectively managing and analyzing such trajectories is becoming increasingly important. In this article, we present a spatio-temporal mining approach to analyze protein folding trajectories. It exploits the simplicity of contact maps, while also integrating 3D structural information in the analysis. It characterizes the dynamic folding process by first identifying spatio-temporal association patterns in contact maps, then studying how such patterns evolve along a folding trajectory. We demonstrate that such patterns can be leveraged to summarize folding trajectories, and to facilitate the detection and ordering of important folding events along a folding path. We also show that such patterns can be used to identify a consensus partial folding pathway across multiple folding trajectories. Furthermore, we argue that such patterns can capture both local and global structural topology in a 3D protein conformation, thereby facilitating effective structural comparison amongst conformations. We apply this approach to analyze the folding trajectories of two small synthetic proteins-BBA5 and GSGS (or Beta3S). We show that this approach is promising towards addressing the above issues, namely, folding trajectory summarization, folding events detection and ordering, and consensus partial folding pathway identification across trajectories

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Latex agglutination tests for selected Escherichia coli enzymes

Rapid latex agglutination assays for identifying tryptophanase, glutamate decarboxylase, and glucuronidase from Escherichia coli cell lysates were developed by using rabbit polyclonal antibodies elicited to commercial E. coli enzyme preparations. The latex agglutination tests had sensitivities of 77% for tryptophanase, 83% for glutamate decarboxylase, and 70% for glucuronidase. Specificities were 61% for tryptophanase, 57% for glutamate decarboxylase, and 82% for glucuronidase. The sensitivities and specificities were too low to warrant continued development of the assays;Development of the latex agglutination tests required extensive studies in attempts to reduce or eliminate nonspecific agglutination reactions. The effects of a wide variety of assay conditions on assay performance were studied. Latex particles of different affinities and with different surface charges, different blocking agents, assay buffers of different compositions, pH values, and ionic strengths, chaotropic agents, and different antibody preparations were all examined. A combination of 0.22 [mu]m diameter latex particles sensitized with both 0.5 and 1.0 mg/ml immunoglobulin and blocked with a [beta]-casein and glycerol mixture resulted in the production of the best reactive latex reagents. Immunoglobulin adsorption studies yielded valuable information that was used to optimize the binding of the antibodies to the latex particles. Four different antibody purification methods had no significant effect in the elimination of nonspecific agglutination reactions. Antibody characterization by polyacrylamide gel electrophoresis, Ouchterlony immunodiffusion, enzyme immunoassays, and Western blot analysis revealed that the antibody preparations were heterologous and reacted with many different proteins. The cross-reactions among the antibody preparations and nontarget proteins indicated that better methods of antibody production must be used to provide more specific reagents for the latex agglutination assay

Beyond chemically defined – Characterization of chemically defined cell culture medium for the cultivation of CHO cells

Krattenmacher F. Beyond chemically defined – Characterization of chemically defined cell culture medium for the cultivation of CHO cells. Bielefeld: Universität Bielefeld; 2020.Chemically defined media (CDM) for cell culture are routinely used in industrial processes for recombinant protein production from mammalian expression systems as for example Chinese hamster ovary (CHO) cells. As CDM are nowadays considered as the industry standard the focus has shifted from implementation and improvement of performance to additionally their chemical behavior and the impact on process robustness. Since CDM are highly concentrated aqueous mixtures of versatile chemical compounds one particular problem in this context is the high risk for chemical reactions and instability. Therefore, a major focus of this thesis is the generation of understanding for chemical interactions of CDM compounds and especially the establishment of analytical technologies for the purpose of media characterization. Thus, a mixed mode liquid chromatography tandem mass spectrometry (LC-QqQ-MS) method that is able to simultaneously quantify the majority of media compounds has been developed and validated. This powerful method has been applied to characterize the chemical behavior of feed media under process relevant conditions as preparation and storage. Further on line and off line analytics have been applied to gain insight into CDM chemistry. The application of probes measuring standard parameters have shown the dynamic behavior of chemical key parameters during CDM powder hydration. A Particle probe, such as the focused beam reflectance measurement (FBRM), has been shown to be useful for dissolution behavior investigations of different media recipes or powder compositions. However, it is rather difficult to establish the technology for batch to batch comparison or the monitoring of deviations from the standard preparation conditions. Media preparations with simplified media powders revealed that the compounds ascorbic acid and phosphates cause an apparent drop in dissolved oxygen concentration upon iron compound addition. The combination of the experiments with the newly developed LC QqQ MS method confirmed the comparability of chemical behavior in different media matrixes of most of the CDM compounds but highlighted some differences. Furthermore, measurements with the LC-QqQ-MS showed that the effect of preparation temperature and relevant storage conditions on media stability were negligible. In contrast, measurement of samples over storage time identified unstable compounds. A closer look at the media after storage showed that some formulations formed precipitate during storage and the collection of the solid material on filter membranes revealed their different appearance. Investigations of the material with specialized analytics proved that their identity was heterogeneous. One precipitate that was drawing attention on itself was of silver color and could be shown to consist of Sulphur

Ways of Monsoon Air: Entanglements and Stories of Matter, Space, and Time

The air of the monsoon is a powerful force of matter that makes, co-constitutes and is made by its many worlds. Having emerged from the context of the Monsoon Assemblages project, this doctoral thesis asks how the air of the monsoon re-orients, informs, animates and confronts the way we view Delhi and how the city animates, opens up and assists in the distribution of its matter and politics through the monsoon. Through the process of the work, the thesis travels to a variety of locations, temporalities, matters and times to engage with the sticky complexity of the liveliness of (and living because of) monsoonal atmosphere. I develop something that I call A Monsoon Air Methodology which I propose is a way of meandering with and because of monsoonal capacities and forms – in inviting generosity of the way different knowledges view the monsoon, and letting monsoonal sway mediate those stories – in concluding that the monsoon is a knowledge system too. Enveloped between an introduction with notes for a methodology and a conclusion are three chapters. They are about the winter haze, an invasive plant species and the question of the death of monsoonal time – amidst a range of linkages and materials. The work is very interdisciplinary and gathers a variety of methods and approaches in engaging and deepening an understanding of the role of the monsoon and anthropogenic materiality as they agentially mingle in the co-production of narrative, writing, worlds, possibilities, pasts and the broader implication of monsoonal thought – investing in its opacity, survivability, uncertainity, multispecies ecology and permeation. Through this work, I ask how thinking and sensing through the monsoon and its ways – can open up, share, distribute and make insights of matters, places and times, for liveability, in these precarious troubles of the Anthropocene