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Biomedical subjects

M F Ochs

Publications and source records attributed to M F Ochs.

6 recordsLinked to original sources
PubMed2002

Application of Bayesian decomposition for analysing microarray data.

MOTIVATION: Microarray and gene chip technology provide high throughput tools for measuring gene expression levels in a variety of circumstances, including cellular response to drug treatment, cellular growth and development, tumorigenesis, among many other processes. In order to interpret the large data sets generated in experiments, data analysis techniques that consider biological knowledge during analysis will be extremely useful. We present here results showing the application of such a tool to expression data from yeast cell cycle experiments. RESULTS: Originally developed for spectroscopic analysis, Bayesian Decomposition (BD) includes two features which make it useful for microarray data analysis: the ability to assign genes to multiple coexpression groups and the ability to encode biological knowledge into the system. Here we demonstrate the ability of the algorithm to provide insight into the yeast cell cycle, including identification of five temporal patterns tied to cell cycle phases as well as the identification of a pattern tied to an approximately 40 min cell cycle oscillator. The genes are simultaneously assigned to the patterns, including partial assignment to multiple patterns when this is required to explain the expression profile. AVAILABILITY: The application is available free to academic users under a material transfer agreement. Go to http://bioinformatics.fccc.edu/ for more details.

Algorithms↗
PubMed2002

BeoBLAST: distributed BLAST and PSI-BLAST on a Beowulf cluster.

UNLABELLED: BeoBLAST is an integrated software package that handles user requests and distributes BLAST and PSI-BLAST searches to nodes of a Beowulf cluster, thus providing a simple way to implement a scalable BLAST system on top of relatively inexpensive computer clusters. Additionally, BeoBLAST offers a number of novel search features through its web interface, including the ability to perform simultaneous searches of multiple databases with multiple queries, and the ability to start a search using the PSSM generated from a previous PSI-BLAST search on a different database. The underlying system can also handle automated querying for high throughput work. AVAILABILITY: Source code is available under the GNU public license at http://bioinformatics.fccc.edu/

Computer Communication Networks↗
PubMed2001

Signal transduction driving technology driving signal transduction: factors in the design of targeted therapies.

A significant number of human diseases can be attributed to defects in cellular signal transduction pathways. Large-scale proteomics projects now in progress seek to better define critical components of signal transduction networks, to enable more intelligent design of therapeutic agents that can specifically correct disease-specific signaling alterations by targeting individual proteins. A complicating factor in this endeavor is the fact that intracellular signaling involves many diverse mechanisms that in sum finely modulate the activity of individual proteins in response to different biological inputs. Ability to develop reagents that selectively correct disease-associated signaling activities, while leaving intact benign or essential activities, encompassed within a single protein requires an intimate knowledge of pathway-specific control mechanisms. To illustrate these points, we provide examples of some of the complex control mechanisms regulating the Cas proteins, which contribute to integrin-dependent biological response. We then discuss issues involved in systematically incorporating information related to complex control mechanisms in proteomic databases. Finally, we describe some recent instances in which protein interaction technologies have been specifically adapted to identify small molecule agents that regulate protein response in physiologically desirable ways, and discuss issues relevant to future drug discovery efforts.

Adaptor Proteins, Signal Transducing↗
PubMed1999

A new method for spectral decomposition using a bilinear Bayesian approach.

A frequent problem in analysis is the need to find two matrices, closely related to the underlying measurement process, which when multiplied together reproduce the matrix of data points. Such problems arise throughout science, for example, in imaging where both the calibration of the sensor and the true scene may be unknown and in localized spectroscopy where multiple components may be present in varying amounts in any spectrum. Since both matrices are unknown, such a decomposition is a bilinear problem. We report here a solution to this problem for the case in which the decomposition results in matrices with elements drawn from positive additive distributions. We demonstrate the power of the methodology on chemical shift images (CSI). The new method, Bayesian spectral decomposition (BSD), reduces the CSI data to a small number of basis spectra together with their localized amplitudes. We apply this new algorithm to a 19F nonlocalized study of the catabolism of 5-fluorouracil in human liver, 31P CSI studies of a human head and calf muscle, and simulations which show its strengths and limitations. In all cases, the dataset, viewed as a matrix with rows containing the individual NMR spectra, results from the multiplication of a matrix of generally nonorthogonal basis spectra (the spectral matrix) by a matrix of the amplitudes of each basis spectrum in the the individual voxels (the amplitude matrix). The results show that BSD can simultaneously determine both the basis spectra and their distribution. In principle, BSD should solve this bilinear problem for any dataset which results from multiplication of matrices representing positive additive distributions if the data overdetermine the solutions.

Adenosine Triphosphate↗
PubMed1988

Comparison of simian immunodeficiency virus isolates.

Information on the extent of genetic variability among non-human primate lentiviruses related to human immunodeficiency virus (HIV) is sorely lacking. Here we describe the isolation of two molecular clones from the simian immunodeficiency virus (SIV) and their use to derive restriction endonuclease maps of five SIV isolates from rhesus macaques and one from a cynomolgus macaque. Although similar, all six viral isolates are readily distinguishable; the single isolate from a cynomolgus macaque is the most different. The restriction endonuclease map of one macaque isolate (SIVMAC-251) is identical to that published by others for STLV-IIIAGM of African green monkeys and for HTLV-IV of humans. Nucleotide sequences from the envelope region of cloned SIVMAC-251 have more than 99% identify to previously published sequences for STLV-IIIAGM (refs 2, 4) and HTLV-IV (ref. 4). These results and other observations provide strong evidence that isolates previously referred to as STLV-IIIAGM and HTLV-IV by others are not authentic, but were derived from cell cultures infected with SIVMAC-251.

Acquired Immunodeficiency Syndrome↗