Dimitris K. Agrafiotis

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We introduce stochastic proximity embedding (SPE), a novel self-organizing algorithm for producing meaningful underlying dimensions from proximity data. SPE attempts to generate low-dimensional Euclidean embeddings that best preserve the similarities between a set of related observations. The method starts with an initial configuration, and iteratively(More)
We present a new feature selection algorithm for structure-activity and structure-property correlation based on particle swarms. Particle swarms explore the search space through a population of individuals that adapt by returning stochastically toward previously successful regions, influenced by the success of their neighbors. This method, which was(More)
Combinatorial chemistry and high-throughput screening have caused afundamental shift in the way chemists contemplate experiments.Designing a combinatorial library is a controversial art thatinvolves a heterogeneous mix of chemistry, mathematics, economics,experience, and intuition. Although there seems to be little agreementas to what constitutes an ideal(More)
Modern science confronts us with massive amounts of data: expression profiles of thousands of human genes, multimedia documents, subjective judgments on consumer products or political candidates, trade indices, global climate patterns, etc. These data are often highly structured, but that structure is hidden in a complex set of relationships or(More)
BACKGROUND AND PURPOSE Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. METHODS In patients(More)
Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A(More)
The multitude of potential drug targets emerging from genome sequencing demands new approaches to drug discovery. A chemogenomics strategy, which involves the generation of small-molecule compounds that can be used both as tools to probe biological mechanisms and as leads for drug-property optimization, provides a highly parallel, industrialized solution.(More)
We describe a novel diversity metric for use in the design of combinatorial chemistry and high-throughput screening experiments. The method estimates the cumulative probability distribution of intermolecular dissimilarities in the collection of interest and then measures the deviation of that distribution from the respective distribution of a uniform sample(More)
A novel greedy algorithm for the design of focused combinatorial arrays is presented. The method is applicable when the objective function is decomposable to individual molecular contributions and makes use of a heuristic that allows the independent evaluation and ranking of candidate reagents in each variation site in the combinatorial library. The(More)
The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of(More)