Tapesh Santra

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Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling(More)
The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks(More)
Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has(More)
In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based on such high dimensional binary features(More)
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses(More)
One of the primary mechanisms of signal transduction in cells is protein phosphorylation. Upon ligand stimulation a series of phosphorylation events take place which eventually lead to transcription. Different sets of phosphorylation events take place due to different stimulating ligands in different types of cells. Knowledge of these phosphorylation events(More)
— Self managing systems are collective systems capable of accomplishing difficult task in dynamic and varied environment without any guidance and control. Some particular examples of this kind of systems are swarms and self organizing maps. These algorithms are being used to solve a great deal of complex problems like clustering, classification,(More)
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