Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment

07181 Abstracts Collection -- Parallel Universes and Local Patterns

From 1 May 2007 to 4 May 2007 the Dagstuhl Seminar 07181 ``Parallel Universes and Local Patterns\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Rollins Alumni Record, Summer 1977

Dean Charles A. Welch Retire

Algorithms for Highly Symmetric Linear and Integer Programs

This paper deals with exploiting symmetry for solving linear and integer programming problems. Basic properties of linear representations of finite groups can be used to reduce symmetric linear programming to solving linear programs of lower dimension. Combining this approach with knowledge of the geometry of feasible integer solutions yields an algorithm for solving highly symmetric integer linear programs which only takes time which is linear in the number of constraints and quadratic in the dimension.Comment: 21 pages, 1 figure; some references and further comments added, title slightly change

Fuzzy Information Granules in Time Series Data

Often, it is desirable to represent a set of time series through typical shapes in order to detect common patterns. The algorithm presented here compares pieces of a different time series in order to find such similar shapes. The use of a fuzzy clustering technique based on fuzzy c-means allows us to detect shapes that belong to a certain group of typical shapes with a degree of membership. Modifications to the original algorithm also allow this matching to be invariant with respect to a scaling of the time series. The algorithm is demonstrated on a widely known set of data taken from the electrocardiogram (ECG) rhythm analysis experiments performed at the Massachusetts Institute of Technology (MIT) laboratories and on data from protein mass spectrography. © 2004 Wiley Periodicals, Inc

Orometric methods in bounded metric data

A large amount of data accommodated in knowledge graphs (KG) is metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like cities or celestial objects. In this paper, we propose a novel approach that transfers orometric (topographic) measures to bounded metric spaces. While these methods were originally designed to identify relevant mountain peaks on the surface of the earth, we demonstrate a notion to use them for metric data sets in general. Notably, metric sets of items enclosed in knowledge graphs. Based on this we present a method for identifying outstanding items using the transferred valuations functions isolation and prominence. Building up on this we imagine an item recommendation process. To demonstrate the relevance of the valuations for such processes, we evaluate the usefulness of isolation and prominence empirically in a machine learning setting. In particular, we find structurally relevant items in the geographic population distributions of Germany and France. © 2020, The Author(s)

Imaging fictive locomotor patterns in larval Drosophila.

We have established a preparation in larval Drosophila to monitor fictive locomotion simultaneously across abdominal and thoracic segments of the isolated CNS with genetically encoded Ca(2+) indicators. The Ca(2+) signals closely followed spiking activity measured electrophysiologically in nerve roots. Three motor patterns are analyzed. Two comprise waves of Ca(2+) signals that progress along the longitudinal body axis in a posterior-to-anterior or anterior-to-posterior direction. These waves had statistically indistinguishable intersegmental phase delays compared with segmental contractions during forward and backward crawling behavior, despite being ∼10 times slower. During these waves, motor neurons of the dorsal longitudinal and transverse muscles were active in the same order as the muscle groups are recruited during crawling behavior. A third fictive motor pattern exhibits a left-right asymmetry across segments and bears similarities with turning behavior in intact larvae, occurring equally frequently and involving asymmetry in the same segments. Ablation of the segments in which forward and backward waves of Ca(2+) signals were normally initiated did not eliminate production of Ca(2+) waves. When the brain and subesophageal ganglion (SOG) were removed, the remaining ganglia retained the ability to produce both forward and backward waves of motor activity, although the speed and frequency of waves changed. Bilateral asymmetry of activity was reduced when the brain was removed and abolished when the SOG was removed. This work paves the way to studying the neural and genetic underpinnings of segmentally coordinated motor pattern generation in Drosophila with imaging techniques.S.R.P. was supported by a Newton International Fellowship (Royal Society) and a Junior Fellowship (Janelia Research Campus, Howard Hughes Medical Institute). T.G.B. was supported by a Medical Research Council (UK) PhD grant. J.B. was supported by a Henry Dale Fellowship (Royal Society and Wellcome Trust). M.B. was supported by the Isaac Newton Trust.This is the final version of the article. It first appeared from the American Physiological Society via http://dx.doi.org/10.1152/jn.00731.201

Dipeptidylpeptidase IV (CD26) defines leukemic stem cells (LSC) in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a stem cell (SC) neoplasm characterized by the BCR/ABL1 oncogene. Although mechanisms of BCR/ABL1-induced transformation are well-defined, little is known about effector-molecules contributing to malignant expansion and the extramedullary spread of leukemic SC (LSC) in CML. We have identified the cytokine-targeting surface enzyme dipeptidylpeptidase-IV (DPPIV/CD26) as a novel, specific and pathogenetically relevant biomarker of CD34+/CD38─ CML LSC. In functional assays, CD26 was identified as target enzyme disrupting the SDF-1-CXCR4-axis by cleaving SDF-1, a chemotaxin recruiting CXCR4+ SC. CD26 was not detected on normal SC or LSC in other hematopoietic malignancies. Correspondingly, CD26+ LSC decreased to low or undetectable levels during successful treatment with imatinib. CD26+ CML LSC engrafted NOD-SCID-IL-2Rγ−/− (NSG) mice with BCR/ABL1+ cells, whereas CD26─ SC from the same patients produced multilineage BCR/ABL1– engraftment. Finally, targeting of CD26 by gliptins suppressed the expansion of BCR/ABL1+ cells. Together, CD26 is a new biomarker and target of CML LSC. CD26 expression may explain the abnormal extramedullary spread of CML LSC, and inhibition of CD26 may revert abnormal LSC function and support curative treatment approaches in this malignancy

KNIME: the Konstanz Information Miner

The Konstanz Information Miner is a modular environment which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables easy integration of new algorithms, data manipulation or visualization methods as new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture and briefly sketch how new nodes can be incorporated