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Institution

Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.


Papers
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Journal ArticleDOI
26 Jul 2018-PLOS ONE
TL;DR: Results demonstrated that the method is able to produce cancer-specific panels of microRNAs that are promising candidates for a subsequent in vitro validation, and enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that mi RNAs can be used to evaluate cancer severity.
Abstract: MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3' UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation.

10 citations

Journal ArticleDOI
TL;DR: In this article, a model justifying the near-ideal electrical transport in carbon nanotubes (SWNTs) networks is presented, where the field-dependent resistive properties of the networks are calculated using a numerical solver based on the derived individual resistances of SWNTs and the intertube couplings.
Abstract: With the development of separation and sorting techniques, highly enriched semiconducting single-walled carbon nanotubes (SWNTs) have become widely accessible, which has led to the rapid growth of high-performance solution-processed SWNT-based thin-film field-effect transistors (TFTs) showing capabilities comparable to the ideal single-SWNT FETs. With such improvements, theoretical studies and detailed analyses of these networks have become necessary. In this work, a model justifying the near-ideal electrical transport in SWNT networks is presented. The field-dependent resistive properties of the networks are calculated using a numerical solver based on the derived individual resistances of SWNTs and the intertube couplings. The model is capable of simulating mixed SWNT networks consisting of both metallic and semiconducting nanotubes of varying chiralities. Our analysis reveals that the high electrical currents in networks could be largely attributed to the suppression of phonon scattering and strong intertube couplings in highly dense SWNT networks (>30–40 SWNTs $/{\mu m}^{2}$ ). Comparisons between the simu- lated and experimental results indicate good agreement thereby demonstrating the accuracy of the proposed model.

10 citations

Journal ArticleDOI
TL;DR: The results show that a higher dose of antibiotics is required to inhibit Staphylococcus aureus biofilm formation in the sessile phase than in the planktonic phase.

10 citations

Book ChapterDOI
01 Jan 2014
TL;DR: In this paper, a mathematical model based on Quantum Dielectric Theory was used to calculate the direct E0 energy gaps of bismuth containing ternary alloys, and the variation of E0 with x for In Sb1-xBix and GaSb11-XBix are in good agreement with the experimental results.
Abstract: A mathematical model based on Quantum Dielectric Theory has been used to calculate the direct E0 energy gaps of bismuth containing ternary alloys. The variation of E0 with x for In Sb1–xBix and GaSb1–xBix are in good agreement with the experimental results. The composition dependence of E0 at different temperatures is also found out for some other ternary alloys like InPBi and AlSbBi.

10 citations

Journal ArticleDOI
TL;DR: The relation between Bell-CHSH violation and factorization of Hilbert space is considered in this paper, where a state which is local in the sense of the CHSH inequality under a certain factorizatio...
Abstract: The relation between Bell-CHSH violation and factorization of Hilbert space is considered here. That is, a state which is local in the sense of the Bell-CHSH inequality under a certain factorizatio...

10 citations


Authors

Showing all 581 results

NameH-indexPapersCitations
Debnath Bhattacharyya395786867
Samiran Mitra381985108
Dipankar Chakravorty353695288
S. Saha Ray342173888
Tai-hoon Kim335264974
Anindya Sen291093472
Ujjal Debnath293353828
Anirban Mukhopadhyay291693200
Avijit Ghosh281212639
Mrinal K. Ghosh26642243
Biswanath Bhunia23751466
Jayati Datta23551520
Nabarun Bhattacharyya231361960
Pinaki Bhattacharya191141193
Dwaipayan Sen18711086
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20227
2021110
202087
201992
201883
2017103