Biomedical subjects

Gideon Dror

Publications and source records attributed to Gideon Dror.

5 recordsLinked to original sources

PubMed2006

Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity.

The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular studies, we currently have characteristic connectivity and gene expression signatures for most of its neurons. By using two complementary analysis assays we show that the expression signature of a neuron carries significant information about its synaptic connectivity signature, and identify a list of putative genes predicting neural connectivity. The current study rigorously quantifies the relation between gene expression and synaptic connectivity signatures in the C. elegans nervous system and identifies subsets of neurons where this relation is highly marked. The results presented and the genes identified provide a promising starting point for further, more detailed computational and experimental investigations.

Animals↗

PubMed2006

Assessing the number of ancestral alternatively spliced exons in the human genome.

BACKGROUND: It is estimated that between 35% and 74% of all human genes undergo alternative splicing. However, as a gene that undergoes alternative splicing can have between one and dozens of alternative exons, the number of alternatively spliced genes by itself is not informative enough. An additional parameter, which was not addressed so far, is therefore the number of human exons that undergo alternative splicing. We have previously described an accurate machine-learning method allowing the detection of conserved alternatively spliced exons without using ESTs, which relies on specific features of the exon and its genomic vicinity that distinguish alternatively spliced exons from constitutive ones. RESULTS: In this study we use the above-described approach to calculate that 7.2% (+/- 1.1%) of all human exons that are conserved in mouse are alternatively spliced in both species. CONCLUSION: This number is the first estimation for the extent of ancestral alternatively spliced exons in the human genome.

Algorithms↗

PubMed2006

Facial attractiveness: beauty and the machine.

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

Algorithms↗

PubMed2004

Accurate identification of alternatively spliced exons using support vector machine.

MOTIVATION: Alternative splicing is a major component of the regulatory action on mammalian transcriptomes. It is estimated that over half of all human genes have more than one splice variant. Previous studies have shown that alternatively spliced exons possess several features that distinguish them from constitutively spliced ones. Recently, we have demonstrated that such features can be used to distinguish alternative from constitutive exons. In the current study, we used advanced machine learning methods to generate robust classifier of alternative exons. RESULTS: We extracted several hundred local sequence features of constitutive as well as alternative exons. Using feature selection methods we find seven attributes that are dominant for the task of classification. Several less informative features help to slightly increase the performance of the classifier. The classifier achieves a true positive rate of 50% for a false positive rate of 0.5%. This result enables one to reliably identify alternatively spliced exons in exon databases that are believed to be dominated by constitutive exons.

Algorithms↗

PubMed2004

Dynamic proximity of spatio-temporal sequences.

Recurrent networks can generate spatio-temporal neural sequences of very large cycles, having an apparent random behavior. Nonetheless a proximity measure between these sequences may be defined through comparison of the synaptic weight matrices that generate them. Following the dynamic neural filter (DNF) formalism we demonstrate this concept by comparing teacher and student recurrent networks of binary neurons. We show that large sequences, providing a training set well exceeding the Cover limit, allow for good determination of the synaptic matrices. Alternatively, assuming the matrices to be known, very fast determination of the biases can be achieved. Thus, a spatio-temporal sequence may be regarded as spatio-temporal encoding of the bias vector. We introduce a linear support vector machine (SVM) variant of the DNF in order to specify an optimal weight matrix. This approach allows us to deal with noise. Spatio-temporal sequences generated by different DNFs with the same number of neurons may be compared by calculating correlations of the synaptic matrices of the reconstructed DNFs. Other types of spatio-temporal sequences need the introduction of hidden neurons, and/or the use of a kernel variant of the SVM approach. The latter is being defined as a recurrent support vector network (RSVN).

Action Potentials↗