Overview
A Novel Approach to Prediction of the 3-Dimensional Structures of Protein Backbones by Neural Networks
Fredholm, Henrik, Bohr, Henrik, Bohr, Jakob, Brunak, Søren, Cotterill, Rodney M. J., Lautrup, Benny, Petersen, Steffen B.
Since Kendrew & Perutz solved the first protein structures, myoglobin and hemoglobin, and explained from the discovered structures how these proteins perform their function, it has been widely recognized that protein function is intimately linked with protein structure[l]. Within the last two decades X-ray crystallographers have solved the 3-dimensional (3D) structures of a steadily increasing number of proteins in the crystalline state, and recently 2D-NMR spectroscopy has emerged as an alternative method for small proteins in solution. Today approximately three hundred 3D structures have been solved by these methods, although only about half of them can be considered as truly different, and only around a hundred of them are solved at high resolution (that is, less than 2A). The number of protein sequences known today is well over 20,000, and this number seems to be growing at least one order of magnitude faster than the number of known 3D protein structures. Obviously, it is of great importance to develop tools that can predict structural aspects of proteins on the basis of knowledge acquired from known 3D structures.
A Novel Approach to Prediction of the 3-Dimensional Structures of Protein Backbones by Neural Networks
Fredholm, Henrik, Bohr, Henrik, Bohr, Jakob, Brunak, Søren, Cotterill, Rodney M. J., Lautrup, Benny, Petersen, Steffen B.
Since Kendrew & Perutz solved the first protein structures, myoglobin and hemoglobin, and explained from the discovered structures how these proteins perform their function, it has been widely recognized that protein function is intimately linked with protein structure[l]. Within the last two decades X-ray crystallographers have solved the 3-dimensional (3D) structures of a steadily increasing number of proteins in the crystalline state, and recently 2D-NMR spectroscopy has emerged as an alternative method for small proteins in solution. Today approximately three hundred 3D structures have been solved by these methods, although only about half of them can be considered as truly different, and only around a hundred of them are solved at high resolution (that is, less than 2A). The number of protein sequences known today is well over 20,000, and this number seems to be growing at least one order of magnitude faster than the number of known 3D protein structures. Obviously, it is of great importance to develop tools that can predict structural aspects of proteins on the basis of knowledge acquired from known 3D structures.
A Novel Approach to Prediction of the 3-Dimensional Structures of Protein Backbones by Neural Networks
Fredholm, Henrik, Bohr, Henrik, Bohr, Jakob, Brunak, Søren, Cotterill, Rodney M. J., Lautrup, Benny, Petersen, Steffen B.
One current aim of molecular biology is determination of the (3D) tertiary structures ofproteins in their folded native state from their sequences of amino acid 523 524 Fredholm, Bohr, Bohr, Brunak, Cotterill, Lautrup, and Thtersen residues. Since Kendrew & Perutz solved the first protein structures, myoglobin and hemoglobin, and explained from the discovered structures how these proteins perform their function, it has been widely recognized that protein function is intimately linkedwith protein structure[l]. Within the last two decades X-ray crystallographers have solved the 3-dimensional (3D) structures of a steadily increasing number of proteins in the crystalline state, and recently 2D-NMR spectroscopy has emerged as an alternative method for small proteins in solution. Today approximately three hundred 3D structures have been solved by these methods, although only about half of them can be considered as truly different, and only around a hundred of them are solved at high resolution (that is, less than 2A). The number of protein sequences known today is well over 20,000, and this number seems to be growing at least one order of magnitude faster than the number of known 3D protein structures. Obviously, it is of great importance to develop tools that can predict structural aspects of proteins on the basis of knowledge acquired from known 3D structures.
Intentions in Communication: A Review
Bratman's definition of intention is papers range from philosophical This review is organized around the jumping-off point for Cohen and analyses of the concept of intention three of the themes that are sounded Levesque's two papers: "Persistence, to algorithms for recognizing plans, in Intentions in Communication: (1) Intention, and Commitment" and from logical formalizations of speech foundational work on intention and "Rational Interaction as the Basis of acts to analyses of intonational contours its relation to speech act theory, (2) Communication."
Improving Human Decision Making through Case-Based Decision Aiding
Case-based reasoning provides both a methodology for building systems and a cognitive model of people. It is consistent with much that psychologists have observed in the natural problem solving people do. Psychologists have also observed, however, that people have several problems in doing analogical or case-based reasoning. Although they are good at using analogs to solve new problems, they are not always good at remembering the right ones. However, computers are good at remembering. I present case-based decision aiding as a methodology for building systems in which people and machines work together to solve problems. The case-based decision-aiding system augments the person's memory by providing cases (analogs) for a person to use in solving a problem. The person does the actual decision making using these cases as guidelines. I present an overview of case-based decision aiding, some technical details about how to implement such systems, and several examples of case-based systems.
A Survey of the Eighth National Conference on Artificial Intelligence: Pulling Together or Pulling Apart?
A survey of 150 papers from the Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90) shows that AI research follows two methodologies, each incomplete with respect to the goals of designing and analyzing AI systems but with complementary strengths. I propose a mixed methodology and illustrate it with examples from the proceedings.
The Use of Artificial Intelligence by the United States Navy: Case Study of a Failure
This article analyzes an attempt to use computing technology, including AI, to improve the combat readiness of a U.S. Navy aircraft carrier. The method of introducing new technology, as well as the reaction of the organization to the use of the technology, is examined to discern the reasons for the rejection by the carrier's personnel of a technically sophisticated attempt to increase mission capability. This effort to make advanced computing technology, such as expert systems, an integral part of the organizational environment and, thereby, to significantly alter traditional decision-making methods failed for two reasons: (1) the innovation of having users, as opposed to the navy research and development bureaucracy, perform the development function was in conflict with navy operational requirements and routines and (2) the technology itself was either inappropriate or perceived by operational experts to be inappropriate for the tasks of the organization. Finally, this article suggests those obstacles that must be overcome to successfully introduce state-of-the-art computing technology into any organization.
A Survey of the Eighth National Conference on Artificial Intelligence: Pulling Together or Pulling Apart?
Fields 3-8 of table 1 of the survey and general results, a discussion represent purposes, specifically, to define of the four hypotheses, and two sections models (field 3), prove theorems about the at the end of the article that contain details of models (field 4), present algorithms (field 5), the survey and statistical analyses. The next analyze algorithms (field 6), present systems section (The Survey) briefly describes the 16 or architectures (field 7), and analyze them substantive questions I asked about each (field 8). These purposes are not mutually paper. One of the closing sections (An Explanation exclusive; for example, many papers that of the Fields in Table 1) discusses the present models also prove theorems about criteria for answering the survey questions the models.
Time and time again: The many ways to represent time
One of the most crucial problems in any computer system that involves representing the world is the representation of time. This includes applications such as databases, simulation, expert systems, and applications of Artificial Intelligence in general. In this brief article, I will give a survey of the basic techniques available for representing time, and then talk about temporal reasoning in a general setting as needed in AI applications. Quite different representations of time are usable depending on the assumptions that can be made about the temporal information to be represented. Can one assume that a timestamp can be assigned to each event, or barring that, that the events are fully ordered?