In our initial research phase, Omar Tazi and I established a probabilistic forecasting framework, utilizing Bayesian Networks to map dependencies between oil price movements and key macroeconomic ...
Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly ...
Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose ...
Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks
Among diverse tools available for analyzing temporal sequences, Dynamic Bayesian Network (DBN) has been one of the most widely used to infer regulatory relationships in systems biology. The standard ...
In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer ...
Meiotic recombination is known to vary over 1,000-fold in many eukaryotic organisms, including maize. This regional genomic variation has enormous consequences for plant breeders, who rely on meiotic ...
Although no historical information exists about the Indus civilization (flourished ca. 2600–1900 B.C.), archaeologists have uncovered about 3,800 short samples of a script that was used throughout the ...
De novo sequencing of peptides poses one of the most challenging tasks in data analysis for proteome research. In this paper, a generative hidden Markov model (HMM) of mass spectra for de novo peptide ...
Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence 'labeling' problems 1,2. They provide a conceptual toolkit for building complex models just by ...
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