40224479
Morphological similarity: A 3D molecular similarity method correlated with protein-ligand recognition
Recognition of small molecules by proteins depends on three-dimensional molecular surface complementarity. However, the dominant techniques for analyzing the similarity of small molecules are based on two-dimensional chemical structure, with such techniques often outperforming three-dimensional techniques in side-by-side comparisons of correlation to biological activity. This paper introduces a new molecular similarity method, termed morphological similarity (MS), that addresses the apparent paradox. Two sets of molecule pairs are identified from a set of ligands whose protein-bound states are known crystallographically. Pairs that bind the same protein sites form the first set, and pairs that bind different sites form the second. MS is shown to separate the two sets significantly better than a benchmark 2D similarity technique. Further, MS agrees with crystallographic observation of bound ligand states, independent of information about bound states. MS is efficient to compute and can be practically applied to large libraries of compounds.
5500839
Compass: A shape-based machine learning tool for drug design
SummaryBuilding predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.
6517871
Deriving quantitative conclusions from microarray expression data
MOTIVATION
The last few years have seen the development of DNA microarray technology that allows simultaneous measurement of the expression levels of thousands of genes. While many methods have been developed to analyze such data, most have been visualization-based. Methods that yield quantitative conclusions have been diverse and complex.


RESULTS
We present two straightforward methods for identifying specific genes whose expression is linked with a phenotype or outcome variable as well as for systematically predicting sample class membership: (1) a conservative, permutation-based approach to identifying differentially expressed genes; (2) an augmentation of K-nearest-neighbor pattern classification. Our analyses replicate the quantitative conclusions of Golub et al. (1999; Science, 286, 531-537) on leukemia data, with better classification results, using far simpler methods. With the breast tumor data of Perou et al. (2000; Nature, 406, 747-752), the methods lend rigorous quantitative support to the conclusions of the original paper. In the case of the lymphoma data in Alizadeh et al. (2000; Nature, 403, 503-511), our analyses only partially support the conclusions of the original authors.


AVAILABILITY
The software and supplementary information are available freely to researchers at academic and non-profit institutions at http://cc.ucsf.edu/jain/public
14243133
Generalization Performance in PARSEC - A Structured Connectionist Parsing Architecture
This paper presents PARSEC--a system for generating connectionist parsing networks from example parses. PARSEC is not based on formal grammar systems and is geared toward spoken language tasks. PARSEC networks exhibit three strengths important for application to speech processing: 1) they learn to parse, and generalize well compared to hand-coded grammars; 2) they tolerate several types of noise; 3) they can learn to use multi-modal input. Presented are the PARSEC architecture and performance analyses along several dimensions that demonstrate PARSEC's features. PARSEC's performance is compared to that of traditional grammar-based parsing systems.
13887159
JANUS: Speech-to-Speech Translation Using Connectionist and Non-Connectionist Techniques
We present JANUS, a speech-to-speech translation system that utilizes diverse processing strategies, including connectionist learning, traditional AI knowledge representation approaches, dynamic programming, and stochastic techniques. JANUS translates continuously spoken English and German into German, English, and Japanese. JANUS currently achieves 87% translation fidelity from English speech and 97% from German speech. We present the JANUS system along with comparative evaluations of its interchangeable processing components, with special emphasis on the connectionist modules.
13422669
Incremental Parsing by Modular Recurrent Connectionist Networks
We present a novel, modular, recurrent connectionist network architecture which learns to robustly perform incremental parsing of complex sentences. From sequential input, one word at a time, our networks learn to do semantic role assignment, noun phrase attachment, and clause structure recognition for sentences with passive constructions and center embedded clauses. The networks make syntactic and semantic predictions at every point in time, and previous predictions are revised as expectations are affirmed or violated with the arrival of new information. Our networks induce their own "grammar rules" for dynamically transforming an input sequence of words into a syntactic/semantic interpretation. These networks generalize and display tolerance to input which has been corrupted in ways common in spoken language.
34672891
Estimating Groundwater Pollution Source Location from Observed Breakthrough Curves Using Neural Networks
This paper presents the results of a study aimed at estimating groundwater pollution source location from observed breakthrough curves using neural networks. Two different methods of presenting the breakthrough curves to the ANN are investigated. The feed-forward multi-layer perceptron (MLP) type artificial neural network (ANN) models are employed. The ANNs were trained using the back-propagation training algorithm on simulated data. A new approach for ANN training using back-propagation is employed that considers two different error statistics to prevent over-training or under-training of the ANNs. The preliminary results indicate that the ANNs are very efficient tools for estimating the distance of the potential pollution source from the observation well where breakthrough curve is measured.
15680776
Veterans and internet use: implications for career preparation and development support
This research compares internet usage by veterans of the U.S. armed forces with internet usage by two reference groups - an educational-elite group and an educational-non-elite group. In terms of overall use, veterans are found to use the internet significantly less than members of the educational-elite reference group. This is cause for concern. Somewhat reassuringly, veterans are found to use the internet significantly more than members of the non-elite reference group.Effective and efficient use of the internet can potentially be enormously helpful to veterans in their transition out of armed forces life; and can help them adjust to, and get ahead in, civilian life and careers. This research suggests that agencies that look after veterans' interests, and provide counseling and training services to them, may find it rewarding to focus some of their attention towards increasing veterans' use of the internet. Thus this research has implications for issues related to career preparation and development support for veterans.
43039643
Hybrid neural network models for hydrologic time series forecasting
The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.
18557608
Performance Paradox : Information Technology Investments and Administrative Performance in the Case of the 50 U.S. State Governments
This study introduces the terminology of performance paradox in the context of IT (information technology) investment and use. Although it is somewhat related to the concept of productivity paradox, in the case of performance paradox, the dependent variable (i.e., the measure of performance) is not productivity, but some other measure of performance. This paper presents the results of a study that examined the relationship between IT investments by the 50 state governments of the United States and the administrative performance of these state governments. There are two parts to this study. In the first part, IT investments by state governments were related to multiple measures of state government performance, namely performance in financial management, human resource management, information technology management, capital management, and managing for results. In the second part of the study, IT investments by state governments were related to projected state government budget deficits. State budget deficits are used as a performance measure because they are an indicator of state government planning performance. Structural equation modeling was performed to analyze the data. The results indicate incidence of performance paradox. It appears as though the more U.S. state governments invest in IT, the worse they perform. However, there are also indications that with the passage of time, the relationship between IT investment and performance shows improvement. Thus, over time, the performance paradox becomes less pronounced. In some cases, it appears that past investments in IT actually help future performance.
39123560
High Quality Compression of Educational Videos Using Content-Adaptive Framework
With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.