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Cerebral Cortex (New York, NY) (1)
Clinical immunology (Orlando, Fla.) (1)
Journal of biomolecular screening (1)
The open bioinformatics journal (1)
Svojanovsky, Stan (3)
Smith, Peter G. (2)
Abdou, Nabih I (1)
Boudrias, Marie-Hélène (1)
Chakrabarti, Swapan (1)
Cheney, Paul D. (1)
Georg, Gunda I. (1)
Greenwell, Cindy (1)
Kimler, Bruce F. (1)
Lee, Sang-Pil (1)
Lushington, Gerald H. (1)
Mathur, Sachin (1)
Rider, Virginia (1)
Slavik, Romana (1)
Smith, Peter (1)
Srinivas, Adagarla B. (1)
Svojanovsky, Stan R. (1)
Visvanathan, Mahesh (1)
Walters, Emily (1)
Wilson, George S. (1)
Yoo, Byunggil (1)
Year of Publication
Artificial Neural Network Based Analysis of High Throughput Screening Data for Improved Prediction of Active Compounds
Georg, Gunda I.
Wilson, George S.
Smith, Peter G.
Journal of biomolecular screening
Artificial Neural Networks (ANNs) are trained using High Throughput Screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a Methionine Aminopeptidases Inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to non-active compounds, RA/N, was 0.0321. Back-propagation ANNs were trained and validated using Principal Components derived from the physico-chemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a three-fold gain in RA/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was utilized to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a ten-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.
pattern classification; neural networks; generalization property
Forelimb Muscle Representations and Output Properties of Motor Areas in the Mesial Wall of Rhesus Macaques
Cheney, Paul D.
Cerebral Cortex (New York, NY)
In this study, forelimb organizations and output properties of the supplementary motor area (SMA) and the dorsal cingulate motor area (CMAd) were assessed and compared with primary motor cortex (M1). Stimulus-triggered averages of electromyographic activity from 24 muscles of the forelimb were computed from layer V sites of 2 rhesus monkeys performing a reach-to-grasp task. No clear segregation of the forelimb representation of proximal and distal muscles was found in SMA. In CMAd, sites producing poststimulus effects in proximal muscles tended to be located caudal to distal muscle sites, although the number of effects was limited. For both SMA and CMAd, facilitation effects were more prevalent in distal than in proximal muscles. At an intensity of 60 μA, the mean latencies of M1 facilitation effects were 8 and 12.1 ms shorter and the magnitudes ∼10 times greater than those from SMA and CMAd. Our results show that corticospinal neurons in SMA and CMAd provide relatively weak input to spinal motoneurons compared with the robust effects from M1. However, a small number of facilitation effects from SMA and CMAd had latencies as short as the shortest ones from M1 suggesting a minimum linkage to motoneurons as direct as that from M1.
corticospinal neuron; EMG; forelimb; motor control; primate; supplementary motor area
1 Estradiol Targets T Cell Signaling Pathways in Human Systemic Lupus
Abdou, Nabih I
Kimler, Bruce F.
Clinical immunology (Orlando, Fla.)
The major risk factor for developing systemic lupus erythematosus (SLE) is being female. The present study utilized gene profiles of activated T cells from females with SLE and healthy controls to identify signaling pathways uniquely regulated by estradiol that could contribute to SLE pathogenesis. Selected downstream pathway genes (+/− estradiol) were measured by real time polymerase chain amplification. Estradiol uniquely upregulated six pathways in SLE T cells that control T cell function including interferon-α signaling. Measurement of interferon-α pathway target gene expression revealed significant differences (p = 0.043) in DRIP150 (+/− estradiol) in SLE T cell samples while IFIT1 expression was bimodal and correlated moderately (r = 0.55) with disease activity. The results indicate that estradiol alters signaling pathways in activated SLE T cells that control T cell function. Differential expression of transcriptional coactivators could influence estrogen-dependent gene regulation in T cell signaling and contribute to SLE onset and disease pathogenesis.
SLE; estradiol; interferon-α; T cell signaling; microarray
GOAPhAR: An Integrative Discovery Tool for Annotation, Pathway Analysis
Srinivas, Adagarla B.
Lushington, Gerald H.
Smith, Peter G.
The open bioinformatics journal
We have developed the web based tool GOAPhAR (Gene Ontology, Annotations and Pathways for Array Research), that integrates information from disparate sources regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, Gene Ontology, publicly available microarray datasets and tools for statistically validating clusters in microarray data. Genes of interest can be input as Affymetrix probe identifiers, Genbank, or Unigene identifiers for human, mouse or rat genomes. Results are provided in a user friendly interface with hyperlinks to the sources of information.
GOAPhAR: Gene ontology, Annotations and pathways for array research
Results 1-4 (4)
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