On optimal comparability editing with applications to molecular diagnostics

<p>Abstract</p> <p>Background</p> <p>The C<smcaps>OMPARABILITY</smcaps> E<smcaps>DITING</smcaps> problem appears in the context of hierarchical disease classification based on noisy data. We are given a directed graph <it>G </it>representing hierarchical relationships between patient subgroups. The task is to identify the minimum number of edge insertions or deletions to transform <it>G </it>into a transitive graph, that is, if edges (<it>u</it>, <it>v</it>) and (<it>v</it>, <it>w</it>) are present then edge (<it>u</it>, <it>w</it>) must be present, too.</p> <p>Results</p> <p>We present two new approaches for the problem based on fixed-parameter algorithmics and integer linear programming. In contrast to previously used heuristics, our approaches compute provably optimal solutions.</p> <p>Conclusion</p> <p>Our computational results demonstrate that our exact algorithms are by far more efficient in practice than a previously used heuristic approach. In addition to the superior running time performance, our algorithms are capable of enumerating all optimal solutions, and naturally solve the weighted version of the problem.</p

Value parameters rationalization of supply chains material flows of the building industry enterprises

Стаття присвячена розгляду математичної моделі управління матеріальними запасами із узагальненими номенклатурними випадками, що дозволяє оптимізувати витрати підприємства на етапі переміщення та зберігання продукції від постачальників у необхідну точку попиту (будівельний майданчик, склад будівельної організації, філію тощо), враховує багатономенклат урність поставки продукції із суттєво розширеною параметричною базою, зокрема, площу складування, обмеження щодо мінімального розміру і вартості доставленої продук ції, витрати на зберігання, можливі обсяги замовлення, втрачену вигоду і т.д.The article is devoted to the mathematical inventory control models with generalized nomenclature cases, to optimize the cost of the enterprise at the stage of moving and storage products from suppliers in the desired point of demand (construction site, a warehouse of a const ruction company, subsidiaries, etc.), takes into account a large range of products significantly enhanced parametric basis, in particular, storage area, restrictions on minimum size and cost of the delivered products, storage costs, possible volumes of orders, loss of profit, etc

Combinatorial Approaches for Mass Spectra Recalibration

Mass spectrometry has become one of the most popular analysis techniques in Proteomics and Systems Biology. With the creation of larger datasets, the automated recalibration of mass spectra becomes important to ensure that very peak in the sample spectrum is correctly assigned to some peptide and protein. Algorithms for recalibrating mass spectra have to be robust with respect to wrongly assigned peaks, as well as efficient due to the amount of mass spectrometry data. The recalibration of mass spectra leads us to the problem of finding an optimal matching between mass spectra under measurement errors. We have developed two deterministic methods that allow robust computation of such a matching: The first approach uses a computational geometry interpretation of the problem, and tries to find two parallel lines with constant distance that stab a maximal number of points in the plane. The second approach is based on finding a maximal common approximate subsequence, and improves existing algorithms by one order of magnitude exploiting the sequential nature of the matching problem. We compare our results to a computational geometry algorithm using a topological line-sweep

MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter ‘u’. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation

SOM-based Peptide Prototyping for Mass Spectrometry Peak Intensity Prediction

In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type based on Self-Organizing Maps (SOMs) for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by nu-Support Vector Regression and show how the SOM learning architecture provides a basis for peptide feature profiling and visualisation