Browsing by Author "Jadhav, A."

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    Performance assessment of pocket tunnel FET and accumulation mode FET for detection of streptavidin protein
    (Institute of Physics, 2023) Jadhav, A.; Yadav, S.; Pandey, S.K.; Garg, V.; Dwivedi, P.
    In this paper, Dielectrically Modulated (DM) pocket Tunnel Field Effect Transistor (TFET) and Accumulation Mode Field Effect Transistor (AMFET) biosensors are examined for the Sensitivity estimation of different thicknesses of biotarget (Streptavidin)/bioreceptor (Biotin)/silica binding protein (SBP or APTES) biomolecules with a fully filled and partially filled cavity. The sensitivity parameter is based on realistic process detection and is calculated as the ratio of biotarget to bioreceptor drain current for neutral and charged biomolecules. The effect on the sensitivity for a filled cavity is observed for: a) varying the thickness of streptavidin and Biotin for fixed SBP (APTES) thickness, b) varying the thickness of streptavidin and APTES for fixed biotin thickness, for both Pocket TFET and AMFET. The maximum sensitivity is observed in 4 nm thick streptavidin for the front gate voltage V fg: −3.8 V and V fg: −1.6 V for pocket TFET and AMFET, respectively. © 2023 IOP Publishing Ltd.
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    Predicting cross-tissue hormone-gene relations using balanced word embeddings
    (Oxford University Press, 2022) Jadhav, A.; Kumar, T.; Raghavendra, M.; Loganathan, T.; Narayanan, M.
    Motivation: Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing. Results: We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS∗ models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormone's production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue-tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well. © 2022 The Author(s).