Biomedical subjects

Snehasis Mukhopadhyay

Publications and source records attributed to Snehasis Mukhopadhyay.

6 recordsLinked to original sources

PubMed2005

Mining and survey of simple sequence repeats in expressed sequence tags of dicotyledonous species.

Simple sequence repeat (SSR) markers are widely used in many plant and animal genomes due to their abundance, hypervariability, and suitability for high-throughput analysis. Development of SSR markers using molecular methods is time consuming, laborious, and expensive. Use of computational approaches to mine ever-increasing sequences such as expressed sequence tags (ESTs) in public databases permits rapid and economical discovery of SSRs. Most of such efforts to date focused on mining SSRs from monocotyledonous ESTs. In this study, we have computationally mined and examined the abundance of SSRs in more than 1.54 million ESTs belonging to 55 dicotyledonous species. The frequency of ESTs containing SSRs among species ranged from 2.65% to 16.82%. Dinucleotide repeats were found to be the most abundant followed by tri- or mono-nucleotide repeats. The motifs A/T, AG/GA/CT/TC, and AAG/AGA/GAA/CTT/TTC/TCT were the predominant mono-, di-, and tri-nucleotide SSRs, respectively. Most of the mononucleotide SSRs contained 15-25 repeats, whereas the majority of the di- and tri-nucleotide SSRs contained 5-10 repeats. The comprehensive SSR survey data presented here demonstrates the potential of in silico mining of ESTs for rapid development of SSR markers for genetic analysis and applications in dicotyledonous crops.

Computational Biology↗

PubMed2003

Identification of biological relationships from text documents using efficient computational methods.

The biological literature databases continue to grow rapidly with vital information that is important for conducting sound biomedical research and development. The current practices of manually searching for information and extracting pertinent knowledge are tedious, time-consuming tasks even for motivated biological researchers. Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. The term "object" refers to any biological entity such as a protein, gene, cell cycle, etc. and relationship refers to any dynamic action one object has on another, e.g. protein inhibiting another protein or one object belonging to another object such as, the cells composing an organ. This paper presents a novel approach to extract relationships between multiple biological objects that are present in a text document. The approach involves object identification, reference resolution, ontology and synonym discovery, and extracting object-object relationships. Hidden Markov Models (HMMs), dictionaries, and N-Gram models are used to set the framework to tackle the complex task of extracting object-object relationships. Experiments were carried out using a corpus of one thousand Medline abstracts. Intermediate results were obtained for the object identification process, synonym discovery, and finally the relationship extraction. For the thousand abstracts, 53 relationships were extracted of which 43 were correct, giving a specificity of 81 percent. These results are promising for multi-object identification and relationship finding from biological documents.

Algorithms↗

PubMed2002

An intelligent biological information management system.

MOTIVATION: As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area. RESULTS: BioSifter was tested using 500 documents obtained from PubMed database on two biological problems related to genetic polymorphism and extracorporal shockwave lithotripsy. The results indicate that BioSifter is a powerful tool for biological researchers to automatically retrieve relevant text documents from biological literature based on their interest profile. The results also indicate that the first stage of information management process, i.e. data to information transformation, significantly reduces the size of the information space. The filtered data obtained through BioSifter is relevant as well as much smaller in dimension compared to all the retrieved data. This would in turn significantly reduce the complexity associated with the next level transformation, i.e. information to knowledge.

Abstracting and Indexing↗

PubMed2002

Feature decomposition architectures for neural networks: algorithms, error bounds, and applications.

In recent years, systems consisting of multiple modular neural networks have attracted substantial interest in the neural networks community because of various advantages they offer over a single large monolithic network. In this paper, we propose two basic feature decomposition models (namely, parallel model and tandem model) in which each of the neural network modules processes a disjoint subset of the input features. A novel feature decomposition algorithm is introduced to partition the input space into disjoint subsets solely based on the available training data. Under certain assumptions, the approximation error due to decomposition can be proved to be bounded by any desired small value over a compact set. Finally, the performance of feature decomposition networks is compared with that of a monolithic network in real world bench mark pattern recognition and modeling problems.

Algorithms↗

PubMed2002

A multi-level text mining method to extract biological relationships.

Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. This paper presents a novel approach to extract relationships between multiple biological objects that are present in a text document. The approach involves object identification, reference resolution, ontology and synonym discovery, and extracting object-object relationships. Hidden Markov Models (HMMs), dictionaries, and N-Gram models are used to set the framework to tackle the complex task of extracting object-object relationships. Experiments were carried out using a corpus of one thousand Medline abstracts. Intermediate results were obtained for the object identification process, synonym discovery, and finally the relationship extraction. For a corpus of thousand abstracts, 53 relationships were extracted of which 43 were correct, giving a specificity of 81%. The approach is both adaptable and scalable to new problems as opposed to rule-based methods.

Abstracting and Indexing↗

PubMedDate not supplied

TransMiner: mining transitive associations among biological objects from text.

Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from the scientific literature. TransMiner is a system for finding associations among objects by mining the Medline database of the scientific literature. The direct associations among the objects are discovered based on the principle of co-occurrence in the form of an association graph. The principle of transitive closure is applied to the association graph to find potential transitive associations. The potential transitive associations that are indeed direct are discovered by iterative retrieval and mining of the Medline documents. Those associations that are not found explicitly in the entire Medline database are transitive associations and are the candidates for hypothesis generation. The transitive associations were ranked based on the sum of weight of terms that co-occur with both the objects. The direct and transitive associations are visualized using a graph visualization applet. TransMiner was tested by finding associations among 56 breast cancer genes and among 24 objects in the calpain signal transduction pathway. TransMiner was also used to rediscover associations between magnesium and migraine.

Abstracting and Indexing↗