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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
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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
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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
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01 Jan 2014TL;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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Debnath Bhattacharyya | 39 | 578 | 6867 |
Samiran Mitra | 38 | 198 | 5108 |
Dipankar Chakravorty | 35 | 369 | 5288 |
S. Saha Ray | 34 | 217 | 3888 |
Tai-hoon Kim | 33 | 526 | 4974 |
Anindya Sen | 29 | 109 | 3472 |
Ujjal Debnath | 29 | 335 | 3828 |
Anirban Mukhopadhyay | 29 | 169 | 3200 |
Avijit Ghosh | 28 | 121 | 2639 |
Mrinal K. Ghosh | 26 | 64 | 2243 |
Biswanath Bhunia | 23 | 75 | 1466 |
Jayati Datta | 23 | 55 | 1520 |
Nabarun Bhattacharyya | 23 | 136 | 1960 |
Pinaki Bhattacharya | 19 | 114 | 1193 |
Dwaipayan Sen | 18 | 71 | 1086 |