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Institution

Jadavpur University

EducationKolkata, India
About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Schiff base. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.


Papers
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Journal ArticleDOI
TL;DR: New quercetin conjugated gold nanoparticles (QAunp) were successfully evaluated for the first time against leishmanial macrophage infections.

104 citations

Journal ArticleDOI
TL;DR: This detailed FE model of the pelvis-femur complex was developed using a wide range of mechanical properties in the bone of the complex to simulate a real life sideways fall leading to hip fracture and the modeled trochanteric fracture was consistent with clinical findings and with the findings of previous studies.

104 citations

Journal ArticleDOI
TL;DR: In this paper, the spectral properties of the zwitterionic and cationic forms of rhodamine B have been studied in a number of protic and aprotic solvents with different solvation properties.

104 citations

Journal ArticleDOI
TL;DR: In this article, the dynamics of soil organic C (SOC) are investigated in relation to the system productivity of a 20-year-old rice (Oryza sativa L)-berseem (Trifolium alexandrium L) cropping system with different management strategies in the hot humid, subtropics of India.
Abstract: Labile fractions of soil organic C (SOC) can respond rapidly to changes in C supply and are considered to be important indicators of soil quality. An attempt is made in this paper to investigate into the dynamics of total organic C (C tot), oxidisable organic C (C oc), very labile C (C frac 1), labile C (C frac 2), less labile C (C frac 3), non-labile C (C frac 4), microbial biomass C (C mic), mineralizable C (C min) and particulate organic C (C p) in relation to the system productivity of a 20-year-old rice (Oryza sativa L)–berseem (Trifolium alexandrium L) cropping system with different management strategies [no fertilization, only NPK and NPK + FYM (farmyard manure) applied in different seasons] in the hot humid, subtropics of India. Cultivation over the years caused a net decrease, while balanced fertilization with NPK maintained the SOC. About 62% of the C applied as FYM was stabilized into SOC. The passive pool (C frac 3 + C frac 4) constituted about 55% of the C tot. A larger proportion (63%) of applied C was stabilized in the passive pool of SOC. Of the analysed pools, C frac 1, C mic, C p and C min were influenced most by the treatments imposed and explained higher per cent variability in the yield of the crops.

104 citations

Journal ArticleDOI
TL;DR: The proposed clustering method has been shown to perform better than other well-known clustering algorithms in finding clusters of co-expressed genes efficiently and to consist of genes which belong to the same functional groups.
Abstract: The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel fuzzy majority voting approach is proposed to combine the clustering information from all the solutions in the resultant Pareto-optimal set. This approach first identifies the genes which are assigned to some particular cluster with high membership degree by most of the Pareto-optimal solutions. Using this set of genes as the training set, the remaining genes are classified by a supervised learning algorithm. In this work, we have used a Support Vector Machine (SVM) classifier for this purpose. The performance of the proposed clustering technique has been demonstrated on five publicly available benchmark microarray data sets, viz., Yeast Sporulation, Yeast Cell Cycle, Arabidopsis Thaliana, Human Fibroblasts Serum and Rat Central Nervous System. Comparative studies of the use of different SVM kernels and several widely used microarray clustering techniques are reported. Moreover, statistical significance tests have been carried out to establish the statistical superiority of the proposed clustering approach. Finally, biological significance tests have been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes. The proposed clustering method has been shown to perform better than other well-known clustering algorithms in finding clusters of co-expressed genes efficiently. The clusters of genes produced by the proposed technique are also found to be biologically significant, i.e., consist of genes which belong to the same functional groups. This indicates that the proposed clustering method can be used efficiently to identify co-expressed genes in microarray gene expression data. Supplementary Website The pre-processed and normalized data sets, the matlab code and other related materials are available at http://anirbanmukhopadhyay.50webs.com/mogasvm.html .

104 citations


Authors

Showing all 10999 results

NameH-indexPapersCitations
Subir Sarkar1491542144614
Amartya Sen149689141907
Susumu Kitagawa12580969594
Praveen Kumar88133935718
Rodolphe Clérac7850622604
Rajesh Gupta7893624158
Santanu Bhattacharya6740014039
Swagatam Das6437019153
Anupam Bishayee6223711589
Michael G. B. Drew61131524747
Soujanya Poria5717513352
Madeleine Helliwell543709898
Tapas Kumar Maji542539804
Pulok K. Mukherjee5429610873
Dipankar Chakraborti5411512078
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202385
2022332
20211,949
20201,936
20191,737
20181,807