Computational methods in Bioinformatics: Introduction, Review, and Challenges

Biotechnology is emerging as a new driving force for the global economy in the 21st century. An important engine for biotechnology is Bioinformatics. Bioinformatics has revolutionized biology research and drug discovery. Bioinformatics is an amalgamation of biological sciences, computer science, applied math, and systems science. The report provides a brief introduction to molecular biology for non-biologists, with focus on understanding the basic biological problems which triggered the exponentially growing research activities in the bioinformatics fields. The report provides as well a comprehensive literature review of the main challenging problems, and the current tools and algorithms. In particular, the problems of gene modeling, and gene prediction, similarity search, multiple alignments of proteins, and the protein folding problems are highlighted. The report discusses as well how such tools as dynamic programming, hidden Markov models, statistical analysis, clustering, decision trees, fuzzy theory, and neural networks have been applied in solving these problems

Computational methods in Bioinformatics: Introduction, Review, and Challenges

Biotechnology is emerging as a new driving force for the global economy in the 21st century. An important engine for biotechnology is Bioinformatics. Bioinformatics has revolutionized biology research and drug discovery. Bioinformatics is an amalgamation of biological sciences, computer science, applied math, and systems science. The report provides a brief introduction to molecular biology for non-biologists, with focus on understanding the basic biological problems which triggered the exponentially growing research activities in the bioinformatics fields. The report provides as well a comprehensive literature review of the main challenging problems, and the current tools and algorithms. In particular, the problems of gene modeling, and gene prediction, similarity search, multiple alignments of proteins, and the protein folding problems are highlighted. The report discusses as well how such tools as dynamic programming, hidden Markov models, statistical analysis, clustering, decision trees, fuzzy theory, and neural networks have been applied in solving these problems

Discriminative, generative, and imitative learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 201-212).I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the Expectation-Maximization (EM) algorithm for latent discriminative learning (or latent MED).(cont.) While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior.by Tony Jebara.Ph.D

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 356 reports, articles and other documents introduced into the NASA scientific and technical information system in June 1982

GVSU Undergraduate and Graduate Catalog, 2020-2021

Grand Valley State University 2020-2021 undergraduate and graduate course catalog. Course catalogs are published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1095/thumbnail.jp

Virginia Commonwealth University Undergraduate and Professional Programs Bulletin [1998]

Undergraduate and professional bulletin for Virginia Commonwealth University for the academic year 1998-1999. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for undergraduate and professional programs