12 research outputs found

    29th Annual Computational Neuroscience Meeting: CNS*2020

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    Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

    Modelling human choices: MADeM and decision‑making

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    Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

    27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

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    Multiscale sample entropy for time resolved epileptic seizure detection and fingerprinting

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    Early detection of epileptic seizures is still a challenge in the state-of-the-art. The proposed method exploits multiresolution sample entropy for both seizure detection and fingerprinting. First, a SVM classifier is used to detect the seizures' onset with high temporal accuracy, then the seizures fingerprints across the subband structure are derived exploiting sample entropy non stationarity. Over 8 hours of EEG data recordings from patients suffering from temporal lobe epilepsy were used for training and testing the system, and validation was performed based on annotation by one expert neurophysiologist. All the seizures were successfully detected and provides an effective time-scale fingerprinting of their evolution. A prominent impact in high (\u3b3) frequency band was observed whose neurophysiological ground is currently under investigation. \ua9 2014 IEEE

    Image informatics approaches to advance cancer drug discovery

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    High content image-based screening assays utilise cell based models to extract and quantify morphological phenotypes induced by small molecules. The rich datasets produced can be used to identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid biological understanding with the use of tool compounds. Here I present my work developing and applying high-content image based screens of small molecules across a panel of eight genetically and morphologically distinct breast cancer cell lines. I implemented machine learning models to predict compound mechanism of action from morphological data and assessed how well these models transfer to unseen cell lines, comparing the use of numeric morphological features extracted using computer vision techniques against more modern convolutional neural networks acting on raw image data. The application of cell line panels have been widely used in pharmacogenomics in order to compare the sensitivity between genetically distinct cell lines to drug treatments and identify molecular biomarkers that predict response. I applied dimensional reduction techniques and distance metrics to develop a measure of differential morphological response between cell lines to small molecule treatment, which controls for the inherent morphological differences between untreated cell lines. These methods were then applied to a screen of 13,000 lead-like small molecules across the eight cell lines to identify compounds which produced distinct phenotypic responses between cell lines. Putative hits from a subset of approved compounds were then validated in a three-dimensional tumour spheroid assay to determine the functional effect of these compounds in more complex models, as well as proteomics to determine the responsible pathways. Using data generated from the compound screen, I carried out work towards integrating knowledge of chemical structures with morphological data to infer mechanistic information of the unannotated compounds, and assess structure activity relationships from cell-based imaging data

    Mobile Phones as Cognitive Systems

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    Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

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    Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words
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