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Biomedical subjects

Amit Bahl

Publications and source records attributed to Amit Bahl.

7 recordsLinked to original sources
PubMed2006

Clinical review - small cell carcinoma of the bladder.

OBJECTIVES: To review the published literature on the diagnosis and management of small cell carcinoma of the bladder (SCCB). METHODS: Papers were identified by searches of PubMed using the terms "small cell", "bladder" and "carcinoma". Additional papers were identified from review of references of relevant articles. RESULTS: SCCB comprises less than 1% of bladder malignancies. It is an aggressive tumour that commonly presents at an advanced stage, in an elderly population. Consequently, patients are often not fit for anti-neoplastic therapy. In fit patients, the bedrock of treatment in the majority of cases is platinum-based systemic chemotherapy, which was the only factor predictive of improved outcome on multivariate analysis in one large review. The use of neoadjuvant chemotherapy has been associated with favourable results and may therefore be the preferred approach when scheduling treatment. Options for local management comprise surgery or radiotherapy (sequentially or concurrently with chemotherapy), both of which are potentially curative in selected cases. However, the subsequent frequent development of urothelial malignancies with bladder-sparing approaches should be considered when planning treatment, particularly in younger patients. Prognosis of SCCB overall is poor, the median survival of all cases varies from 4 to 23 months, and overall survival at 5 years from 10% to 40% of patients. CONCLUSIONS: SCCB is a rare and aggressive tumour with a poor prognosis. Future efforts should be directed at its early detection and the development of more effective systemic therapies.

Antineoplastic Agents↗
PubMed2004

Using prior knowledge to improve genetic network reconstruction from microarray data.

The use of Bayesian Network methods to recover transcriptional regulatory networks from static microarray data is an active area of bioinformatics research. However, early work in this area lacked realistic analysis of the effects of data set size on learning performance and ignored the potentially immense benefits of using prior biological knowledge. More recent work which has utilized such information has tended to focus on qualitative descriptions of the results. In this paper, we construct a detailed, realistic model for glucose homeostasis and use this model to generate static, synthetic gene expression data. We then use a Bayesian Network method to reconstruct this genetic network from the synthetic microarray data utilizing various amounts and types of prior knowledge. By quantitatively analyzing the effects of data set size and the incorporation of different types of prior biological knowledge on our ability to reconstruct the original network, we show that characteristic portions of genetic networks can be reconstructed from microarray data. Incorporating prior knowledge into the learning scheme greatly reduces the data required, allowing these reverse engineering techniques to be used to learn regulatory interactions from microarray data sets of realistic size.

Bayes Theorem↗
PubMed2003

PlasmoDB: the Plasmodium genome resource. A database integrating experimental and computational data.

PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates the recently completed P. falciparum genome sequence and annotation, as well as draft sequence and annotation emerging from other Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for intra- and inter-species comparisons. Sequence information is integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects and proteomics studies. The relational schema used to build PlasmoDB, GUS (Genomics Unified Schema) employs a highly structured format to accommodate the diverse data types generated by sequence and expression projects. A variety of tools allow researchers to formulate complex, biologically-based, queries of the database. A stand-alone version of the database is also available on CD-ROM (P. falciparum GenePlot), facilitating access to the data in situations where internet access is difficult (e.g. by malaria researchers working in the field). The goal of PlasmoDB is to facilitate utilization of the vast quantities of genomic-scale data produced by the global malaria research community. The software used to develop PlasmoDB has been used to create a second Apicomplexan parasite genome database, ToxoDB (http://ToxoDB.org).

Animals↗
PubMed2002

PlasmoDB: the Plasmodium genome resource. An integrated database providing tools for accessing, analyzing and mapping expression and sequence data (both finished and unfinished).

PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates finished and draft genome sequence data and annotation emerging from Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for cross-species comparisons. Sequence information is also integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects. The relational schemas used to build PlasmoDB [Genomics Unified Schema (GUS) and RNA Abundance Database (RAD)] employ a highly structured format to accommodate the diverse data types generated by sequence and expression projects. A variety of tools allow researchers to formulate complex, biologically based queries of the database. A version of the database is also available on CD-ROM (Plasmodium GenePlot), facilitating access to the data in situations where Internet access is difficult (e.g. by malaria researchers working in the field). The goal of PlasmoDB is to enhance utilization of the vast quantities of data emerging from genome-scale projects by the global malaria research community.

Animals↗