Swati Mehta

Bio: Swati Mehta is an academic researcher. The author has contributed to research in topics: Support vector machine & Statistical learning theory. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Application of support vector machines in viral biology

Abstract: Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.

Early prediction of developing spontaneous activity in cultured neuronal networks.

Abstract: Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.

A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour in C. elegans

Abstract: The chemical basis of smell remains an unsolved problem, with ongoing studies mapping perceptual descriptor data from human participants to the chemical structures using computational methods. These approaches are, however, limited by linguistic capabilities and inter-individual differences in participants. We use olfactory behaviour data from the nematode C. elegans, which has isogenic populations in a laboratory setting, and employ machine learning approaches for a binary classification task predicting whether or not the worm will be attracted to a given monomolecular odorant. Among others, we use architectures based on Natural Language Processing methods on the SMILES representation of chemicals for molecular descriptor generation and show that machine learning algorithms trained on the descriptors give robust prediction results. We further show, by data augmentation, that increasing the number of samples increases the accuracy of the models. From this detailed analysis, we are able to achieve accuracies comparable to that in human studies and infer that there exists a non trivial relationship between the features of chemical structures and the nematode9s behaviour.