Biology subjects

Dong, T. N.

Publications and source records attributed to Dong, T. N..

3 recordsLinked to original sources

bioRxiv2023

Ventral hippocampal interneurons govern extinction and relapse of contextual associations

ABSTRACT/SUMMARYContextual associations are critical for survival but must be extinguished when new conditions render them nonproductive. By most accounts, extinction forms a new memory that competes with the original association for control over behavior, but the mechanisms underlying this competition remain largely enigmatic. Here we find the retrieval of contextual fear conditioning and extinction yield contrasting patterns of activity in prefrontal cortex and ventral hippocampus. Within ventral CA1, activation of somatostatin-expressing interneurons (SST-INs) occurs preferentially during extinction retrieval and correlates with differences in input synaptic transmission. Optogenetic manipulation of these cells but not parvalbumin interneurons (PV-INs) elicits bidirectional changes in fear expression following extinction, and the ability of SST-INs to gate fear is specific to the context in which extinction was acquired. A similar pattern of results was obtained following reward-based extinction. These data show that ventral hippocampal SST-INs are critical for extinguishing prior associations and thereby gate relapse of both aversive and appetitive responses.

neuroscience↗

bioRxiv2021

A multitask transfer learning framework for novel virus-human protein interactions

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection. This could further help in developing treatments of viral diseases. The main issues in tackling it as a machine learning problem is the scarcity of training data as well input information of the viral proteins. We overcome these limitations by exploiting powerful statistical protein representations derived from a corpus of around 24 Million protein sequences in a multi task framework. Our experiments on 7 varied benchmark datasets support the superiority of our approach.

bioinformatics↗

bioRxiv2019

Revisiting Feature Selection with Data Complexity for Biomedicine

The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection(FS) methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal set of the most predictive features. However, there still lacks the answer to the question of "which method to use when". In this paper, we study the performance of feature selection methods with respect to the underlying datasets statistics and their data complexity measures. We perform a comparative study of 11 feature selection methods over 27 publicly available datasets evaluated over a range of number of selected features using classification as the downstream task. We take the first step towards understanding the FS methods performance from the viewpoint of data complexity. Specifically, we (empirically) show that as regard to classification, the performance of all studied feature selection methods is highly correlated with the error rate of a nearest neighbor based classifier. We also argue about the non-suitability of studied complexity measures to determine the optimal number of relevant features. While looking closely at several other aspects, we also provide recommendations for choosing a particular FS method for a given dataset.

bioinformatics↗