Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Fast approximate quadratic programming for graph matching.

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of efficiently and accurately matching the large graphs common in big data, we present our graph matching algorithm, the Fast Approximate Quadratic assignment algorithm. We empirically demonstrate that our algorithm is faster and achieves a lower objective value on over 80% of the QAPLIB benchmark library, compared with the previous state-of-the-art. Applying our algorithm to our motivating example, matching C. elegans connectomes (brain-graphs), we find that it efficiently achieves performance

Relative accuracy—defined to be log10(f^FAQ/f^X)—of all the four algorithms compared with FAQ.

<p>Note that FAQ is better than all the other algorithms on ≈ 94% of the benchmarks. The abscissa is the log number of vertices. The gray dot indicates the mean improvement of FAQ over the other algorithms.</p

Comparison of FAQ with optimal objective function value and previous state-of-the-art for directed graphs.

<p>The best (lowest) value is in <b>bold</b>. Asterisks indicate achievement of the global minimum. The number of vertices for each problem is the number in its name (second column).</p

Absolute wall time for running each of the five algorithms on all 137 QAPLIB benchmarks.

<p>We fit a line on this log-log plot for each algorithm; the slope is displayed beside each line. The FAQ slope is much better than the PATH slope, and worse than the others. Note, however, the time for RANK and U appears to be superlinear on this log-log plot, suggesting that perhaps as the number of vertices increases, PATH might be faster.</p