Data Mining Practical Machine Learning Tools and Techniques

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

I read this book the same weekend that the Packers took on the Rams, and the experience of the latter event, obviously, colored my judgment. Although I abhor anything that smacks of being a handbook (like, \"How to Earn a Merit Badge in Neurosurgery\") because too many volumes in biomedical science already evince a boyscout-like approach, I must confess that parts of this volume are fast, scholarly, and significant, with certain reservations. I like parts of this well-illustrated book because Dr. Sj6strand, without so stating, develops certain subjects on technique in relation to the acquisition of judgment and sophistication. And this is important! So, given that the author (like all of us) is somewhat deficient in some areas, and biased in others, the book is still valuable if the uninitiated reader swallows it in a general fashion, realizing full well that what will be required from the reader is a modulation to fit his vision, propreception, adaptation and response, and the kind of problem he is undertaking. A major deficiency of this book is revealed by comparison of its use of physics and of chemistry to provide understanding and background for the application of high resolution electron microscopy to problems in biology. Since the volume is keyed to high resolution electron microscopy, which is a sophisticated form of structural analysis, but really morphology in a modern guise, the physical and mechanical background of The instrument and its ancillary tools are simply and well presented. The potential use of chemical or cytochemical information as it relates to biological fine structure , however, is quite deficient. I wonder when even sophisticated morphol-ogists will consider fixation a reaction and not a technique; only then will the fundamentals become self-evident and predictable and this sine qua flon will become less mystical. Staining reactions (the most inadequate chapter) ought to be something more than a technique to selectively enhance contrast of morphological elements; it ought to give the structural addresses of some of the chemical residents of cell components. Is it pertinent that auto-radiography gets singled out for more complete coverage than other significant aspects of cytochemistry by a high resolution microscopist, when it has a built-in minimal error of 1,000 A in standard practice? I don't mean to blind-side (in strict football terminology) Dr. Sj6strand's efforts for what is \"routinely used in our laboratory\"; what is done is usually well done. It's just that …

Methods in Enzymology.

Implicit Solvation Models: Equilibria, Structure, Spectra, and Dynamics.

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

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Computational Methods in Drug Discovery

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A Tool for Interrogation of Macromolecular Structure

Plastic evolution is a new perspective of the evolutionary process conjugating the effect, on one hand, of the acquired characters and, on the other, of the relationships that emerge among the principle of evolutionary indeterminacy, the principle of morphologic determination, and natural selection. Plastic evolution is applied to design the coordination index Ic. Ic is used to characterize the valence-isoelectronic series of cyclopentadiene, benzene, toluene, and styrene and compared to charge indices for dipole moment. The parameters needed to calculate Ic are minus the standard enthalpy of formation T, molecular surface area S, and molecular weight W. With the exception of the O heteromolecule, Ic allows differentiating quantitatively the styrene series as well as the cyclopentadiene from the toluene series. Ic improves the multivariable regression equations for dipole moment and is a predictive index when it is used together with the topological charge indices. The correction introduced in the dipole ...

A Chemical Index Inspired by Biological Plastic Evolution: Valence-Isoelectronic Series of Aromatics.

Trichomonas vaginalis (Tv) is the causative agent of the most common, nonviral, sexually transmitted disease in women and men world-wide. Since 1959 metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However resistance to MTZ in some patients and the great cost associated to the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel trichomonacidal chemicals. The 
obtained models, using non-stochastic and stochastic indices, were able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in training (test) sets, respectively. These results validate the models for use in the ligand-based virtual screening. Also they showed large Matthews’ correlation coefficients (C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidenced the robustness of the obtained models. Later, both models were applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classified 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is a more important criterion for validating the models. In addition, these classification functions were applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds were synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Besides, out of the seven compounds that were screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) showed pronounced cytocidal 
activity at the concentration of 100µg/ml at 24h (48h) within the range of 98.66%-100% (99.40%-100%) while only two molecules (chemicals VA7-37 and VA7-38) showed high cytocidal activity at the concentration of 10µg/ml at 24h (48h): 98.38% (94.23%) and 
97.59% (98.10%) correspondingly. The LDA-assisted QSAR models presented here could significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with trichomonacidal activity.

Quick Access to Potential Trichomonacidals through Bond Linear Indices-Trained Ligand-Based virtual Screening Models

Corrigendum: Corrigendum to: Solvent features of cluster single-wall C, BC 2N and BN nanotubes, cones and horns [Microelectron. Eng. 108 (2013) 127-133]

QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragons descriptors and linear discriminant analysis (LDA) are presented here. A dataset of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active dataset was processed by k-means cluster analysis to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model (Eq. 3) in the training set. External validation processes to assess the robustness and predictive power of the obtained model was carried out. This external prediction set had an accuracy of 99.44%. After that, the developed were used in ligand-based virtual screening of tyrosinase inhibitors from the literature and never considered in either training or predicting series. In this case, all screened chemicals were correctly classified by the LDA-based QSAR models. As a final point, these fitted models were used in the screening of new bipiperidines series as new tyrosinase inhibitors. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed a good correspondence. These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitors compounds.

Francisco Torrens

Papers

A Tool for Interrogation of Macromolecular Structure

A Chemical Index Inspired by Biological Plastic Evolution: Valence-Isoelectronic Series of Aromatics.

Quick Access to Potential Trichomonacidals through Bond Linear Indices-Trained Ligand-Based virtual Screening Models

Corrigendum: Corrigendum to: Solvent features of cluster single-wall C, BC 2N and BN nanotubes, cones and horns [Microelectron. Eng. 108 (2013) 127-133]

The Dragon Method in the Computational Identification of Novel Tyrosinase Inhibitors. Results Supported by Experimental Assays