• Open Access close
• Research data close
• ZENODO close
• Digital Humanities and Cultural Her... close
• Neuroinformatics close

We have generated and made publicly available two very large networks of molecular interactions: 49,493 mouse-specific and 52,518 human-specific interactions. These networks were generated through automated analysis of 368,331 full-text research articles and 8,039,972 article abstracts from the PubMed database, using the GeneWays system. Our networks cover a wide spectrum of molecular interactions, such as bind, phosphorylate, glycosylate, and activate; 207 of these interaction types occur more than 1,000 times in our unfiltered, multi-species data set. Because mouse and human genes are linked through an orthological relationship, human and mouse networks are amenable to straightforward, joint computational analysis. Using our newly generated networks and known associations between mouse genes and cerebellar malformation phenotypes, we predicted a number of new associations between genes and five cerebellar phenotypes (small cerebellum, absent cerebellum, cerebellar degeneration, abnormal foliation, and abnormal vermis). Using a battery of statistical tests, we showed that genes that are associated with cerebellar phenotypes tend to form compact network clusters. Further, we observed that cerebellar malformation phenotypes tend to be associated with highly connected genes. This tendency was stronger for developmental phenotypes and weaker for cerebellar degeneration. Data from: Looking at Cerebellar Malformations through Text-Mined Interactomes of Mice and HumansSee original publication and documentation files included in this archive. Archive format is POSIX 'tar' archive compressed using GNU zip (gzip). The included data files are tab-separated ASCII. Some Matlab scripts are provided.iossifov-2009.tgz

Temporal integration in the visual system causes fast-moving objects to generate static, oriented traces (‘motion streaks’), which could be used to help judge direction of motion. While human psychophysics and single-unit studies in non-human primates are consistent with this hypothesis, direct neural evidence from the human cortex is still lacking. First, we provide psychophysical evidence that faster and slower motions are processed by distinct neural mechanisms: faster motion raised human perceptual thresholds for static orientations parallel to the direction of motion, whereas slower motion raised thresholds for orthogonal orientations. We then used functional magnetic resonance imaging to measure brain activity while human observers viewed either fast (‘streaky’) or slow random dot stimuli moving in different directions, or corresponding static-oriented stimuli. We found that local spatial patterns of brain activity in early retinotopic visual cortex reliably distinguished between static orientations. Critically, a multivariate pattern classifier trained on brain activity evoked by these static stimuli could then successfully distinguish the direction of fast (‘streaky’) but not slow motion. Thus, signals encoding static-oriented streak information are present in human early visual cortex when viewing fast motion. These experiments show that motion streaks are present in the human visual system for faster motion. Psychophysical adaptation dataRaw contrast thresholds, and threshold elevations in decibels (dB) after adaptation to either fast (13 deg/s) or slow (1.6 deg/s) motion, testing with either parallel or orthogonal low-contrast gratings. The stimuli were programmed in Matlab version 7.4, using the Psychophysics Toolbox, and thresholds were fitted using custom software written in Matlab.Psychophysical_adapt_anonymised.xlsxRanked SVM for all conditionsRanked accuracies from the support vector machine analysis described in the paper for all of the classification analyses. The first column of each tab contains the subject number (1-8), the second column the number of voxels selected from each region (1 - 250), and the remaining columns give the proportion of accuracy for each region of interest.Ranked_SVM_all_Conditions.xlsx