Biomedical subjects

Dan Shen

Publications and source records attributed to Dan Shen.

7 recordsLinked to original sources

PubMed2006

Analysis of the temperature-sensitive mutation of Escherichia coli pantothenate kinase reveals YbjN as a possible protein stabilizer.

Pantothenate kinase (PanK), a key regulatory enzyme in the coenzyme A (CoA) biosynthetic pathway, catalyzes the rate-limiting phosphorylation of pantothenic acid to form phosphopantothenate during CoA biosynthesis. Escherichia coli ts9 strain manifests temperature-sensitive phenotype on LB media due to its mutation in the coaA gene (coaA1). Sequencing analysis revealed that coaA1 arises from a single base pair mutation that results in an amino acid change, L236F. This change, located proximate to the ATP binding site of CoaA, destabilizes both enzymatic activity and structural integrity or stability of the mutant protein in vitro. Spontaneously, revertants of ts9 were occasionally found on LB medium plates. Two groups of revertants were isolated: for those that can grow at 40 degrees C, a reversion of the original amino acid mutation L236F to L236L or other amino acid (such as L236C) occurs; for those that can grow at 37 degrees C but not 40 degrees C, a mutation at another gene or intergenic suppression is strongly indicated. Towards genetic identification of genes that might interact with coaA1, ybjN, which encodes a putative sensory transduction regulator protein, and whose over-expression is capable of ameliorating the temperature-sensitive phenotype of the structurally unstable CoaA1 or CoaA[L236F], was isolated. Over-expression of ybjN appears to suppress the temperature-sensitive phenotype of several other temperature-sensitive mutations, including coaA14 (carried by DV51 strain), coaA15 (carried by DV70 strain), and ilu-1, suggesting it not only helps CoaA1, but possibly works as a general stabilizer for some other unstable proteins.

Adenosine Triphosphate↗

PubMed2005

Recognition of protein/gene names from text using an ensemble of classifiers.

This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we incorporate three post-processing modules, including an abbreviation resolution module, a protein/gene name refinement module and a simple dictionary matching module, into the system to further improve the performance. Evaluation shows that our system achieves the best performance from among 10 systems with a balanced F-measure of 82.58 on the closed evaluation of the BioCreative protein/gene name recognition task (Task 1A).

Biomedical Research↗

PubMed2005

Periplasmic protein HdeA exhibits chaperone-like activity exclusively within stomach pH range by transforming into disordered conformation.

The extremely acidic environment of the mammalian stomach, with a pH range usually between 1 and 3, represents a stressful challenge for enteric pathogenic bacteria such as Escherichia coli before they enter into the intestine. The hdeA gene of E. coli was found to be acid inducible and was revealed by genetic studies to be important for the acid survival of the strain. This study was performed in an attempt to characterize the mechanism of the activity of the HdeA protein. Our data provided in this report strongly suggest that HdeA employs a novel strategy to modulate its chaperone activity: it possesses an ordered conformation that is unable to bind denatured substrate proteins under normal physiological conditions (i.e. at neutral pH) and transforms into a globally disordered conformation that is able to bind substrate proteins under stress conditions (i.e. at a pH below 3). Furthermore, our data indicate that HdeA exposes hydrophobic surfaces that appear to be involved in the binding of denatured substrate proteins at extremely low pH values. In light of our observations, models are proposed to explain the action of HdeA in both a physiological and a molecular context.

Escherichia coli Proteins↗

PubMed2005

Encoding strategy for maximum noise tolerance bidirectional associative memory.

In this paper, the basic bidirectional associative memory (BAM) is extended by choosing weights in the correlation matrix, for a given set of training pairs, which result in a maximum noise tolerance set for BAM. We prove that for a given set of training pairs, the maximum noise tolerance set is the largest, in the sense that this optimized BAM will recall the correct training pair if any input pattern is within the maximum noise tolerance set and at least one pattern outside the maximum noise tolerance set by one Hamming distance will not converge to the correct training pair. This maximum tolerance set is the union of the maximum basins of attraction. A standard genetic algorithm (GA) is used to calculate the weights to maximize the objective function which generates a maximum tolerance set for BAM. Computer simulations are presented to illustrate the error correction and fault tolerance properties of the optimized BAM.

Electricity↗

PubMed2005

Yeast model uncovers dual roles of mitochondria in action of artemisinin.

Artemisinins, derived from the wormwood herb Artemisia annua, are the most potent antimalarial drugs currently available. Despite extensive research, the exact mode of action of artemisinins has not been established. Here we use yeast, Saccharamyces cerevisiae, to probe the core working mechanism of this class of antimalarial agents. We demonstrate that artemisinin's inhibitory effect is mediated by disrupting the normal function of mitochondria through depolarizing their membrane potential. Moreover, in a genetic study, we identify the electron transport chain as an important player in artemisinin's action: Deletion of NDE1 or NDI1, which encode mitochondrial NADH dehydrogenases, confers resistance to artemisinin, whereas overexpression of NDE1 or NDI1 dramatically increases sensitivity to artemisinin. Mutations or environmental conditions that affect electron transport also alter host's sensitivity to artemisinin. Sensitivity is partially restored when the Plasmodium falciparum NDI1 ortholog is expressed in yeast ndi1 strain. Finally, we showed that artemisinin's inhibitory effect is mediated by reactive oxygen species. Our results demonstrate that artemisinin's effect is primarily mediated through disruption of membrane potential by its interaction with the electron transport chain, resulting in dysfunctional mitochondria. We propose a dual role of mitochondria played during the action of artemisinin: the electron transport chain stimulates artemisinin's effect, most likely by activating it, and the mitochondria are subsequently damaged by the locally generated free radicals.

Animals↗

PubMed2004

Recognizing names in biomedical texts: a machine learning approach.

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. RESULTS: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern, such as prefix and suffix; (3) part-of-speech; (4) head noun trigger; (5) special verb trigger and (6) name alias feature. All the features are integrated effectively and efficiently through a hidden Markov model (HMM) and a HMM-based named entity recognizer. In addition, a k-Nearest Neighbor (k-NN) algorithm is proposed to resolve the data sparseness problem in our system. Finally, we present a pattern-based post-processing to automatically extract rules from the training data to deal with the cascaded entity name phenomenon. From our best knowledge, PowerBioNE is the first system which deals with the cascaded entity name phenomenon. Evaluation shows that our system achieves the F-measure of 66.6 and 62.2 on the 23 classes of GENIA V3.0 and V1.1, respectively. In particular, our system achieves the F-measure of 75.8 on the "protein" class of GENIA V3.0. For comparison, our system outperforms the best published result by 7.8 on GENIA V1.1, without help of any dictionaries. It also shows that our HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated HMM, support vector machines, C4.5, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem. Moreover, evaluation on GENIA V3.0 shows that the post-processing for the cascaded entity name phenomenon improves the F-measure by 3.9. Finally, error analysis shows that about half of the errors are caused by the strict annotation scheme and the annotation inconsistency in the GENIA corpus. This suggests that our system achieves an acceptable F-measure of 83.6 on the 23 classes of GENIA V3.0 and in particular 86.2 on the "protein" class, without help of any dictionaries. We think that a F-measure of 90 on the 23 classes of GENIA V3.0 and in particular 92 on the "protein" class, can be achieved through refining of the annotation scheme in the GENIA corpus, such as flexible annotation scheme and annotation consistency, and inclusion of a reasonable biomedical dictionary. AVAILABILITY: A demo system is available at http://textmining.i2r.a-star.edu.sg/NLS/demo.htm. Technology license is available upon the bilateral agreement.

Abstracting and Indexing↗

PubMed2004

Enhancing HMM-based biomedical named entity recognition by studying special phenomena.

The purpose of this research is to enhance an HMM-based named entity recognizer in the biomedical domain. First, we analyze the characteristics of biomedical named entities. Then, we propose a rich set of features, including orthographic, morphological, part-of-speech, and semantic trigger features. All these features are integrated via a Hidden Markov Model with back-off modeling. Furthermore, we propose a method for biomedical abbreviation recognition and two methods for cascaded named entity recognition. Evaluation on the GENIA V3.02 and V1.1 shows that our system achieves 66.5 and 62.5 F-measure, respectively, and outperforms the previous best published system by 8.1 F-measure on the same experimental setting. The major contribution of this paper lies in its rich feature set specially designed for biomedical domain and the effective methods for abbreviation and cascaded named entity recognition. To our best knowledge, our system is the first one that copes with the cascaded phenomena.

Abbreviations as Topic↗