Predicting the Availability of Hematopoietic Stem Cell Donors Using Machine Learning.

Hematopoietic stem cell transplantation (HSCT) is firmly established as an important curative therapy for patients with hematologic malignancies and other blood disorders. Apart from finding HLA-matched donors during the HSCT process, donor availability remains a key consideration as the time taken from diagnosis to transplant is recognized to adversely affect patient outcome. In this study, we aimed to develop and validate a machine learning approach to predict the availability of stem cell donors.

Linear Search by a Pair of Distinct-Speed Robots.

Two mobile robots are initially placed at the same point on an infinite line. Each robot may move on the line in either direction not exceeding its maximal speed. The robots need to find a stationary target placed at an unknown location on the line.

The Complexity of Optimal Design of Temporally Connected Graphs.

We study the design of small cost temporally connected graphs, under various constraints. We mainly consider undirected graphs of vertices, where each edge has an associated set of discrete availability instances (labels). A journey from vertex to vertex is a path from to where successive path edges have strictly increasing labels.

Randomized probe selection algorithm for microarray design.

DNA microarray technology, originally developed to measure the level of gene expression, has become one of the most widely used tools in genomic study. The crux of microarray design lies in how to select a unique probe that distinguishes a given genomic sequence from other sequences. Due to its significance, probe selection attracts a lot of attention.

On realizing shapes in the theory of RNA neutral networks.

It is known (Reidys et al., 1997b. Bull.