arXiv subjects

Aleksandar Milosavljevic

Publications and source records attributed to Aleksandar Milosavljevic.

3 recordsLinked to original sources

arXiv2025

Rewriting for Symmetric Monoidal Categories with Commutative (Co)Monoid Structure

String diagrams are pictorial representations for morphisms of symmetric monoidal categories. They constitute an intuitive and expressive graphical syntax, which has found application in a very diverse range of fields including concurrency theory, quantum computing, control theory, machine learning, linguistics, and digital circuits. Rewriting theory for string diagrams relies on a combinatorial interpretation as double-pushout rewriting of certain hypergraphs. As previously studied, there is a `tension' in this interpretation: in order to make it sound and complete, we either need to add structure on string diagrams (in particular, Frobenius algebra structure) or pose restrictions on double-pushout rewriting (resulting in 'convex' rewriting). From the string diagram viewpoint, imposing a full Frobenius structure may not always be natural or desirable in applications, which motivates our study of a weaker requirement: commutative monoid structure. In this work we characterise string diagram rewriting modulo commutative monoid equations, via a sound and complete interpretation in a suitable notion of double-pushout rewriting of hypergraphs.

cs.LO↗

arXiv2022

tmVar 3.0: an improved variant concept recognition and normalization tool

Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision. We propose tmVar 3.0: an improved variant recognition and normalization tool. Compared to its predecessors, tmVar 3.0 is able to recognize a wide spectrum of variant related entities (e.g., allele and copy number variants), and to group different variant mentions belonging to the same concept in an article for improved accuracy. Moreover, tmVar3 provides additional variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar3 exhibits a state-of-the-art performance with over 90% accuracy in F-measure in variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar3 is freely available for download. We have also processed the entire PubMed and PMC with tmVar3 and released its annotations on our FTP. Availability: ftp://ftp.ncbi.nlm.nih.gov/pub/lu/tmVar3

cs.CL↗

arXiv2003

Clone-array pooled shotgun mapping and sequencing: design and analysis of experiments

This paper studies sequencing and mapping methods that rely solely on pooling and shotgun sequencing of clones. First, we scrutinize and improve the recently proposed Clone-Array Pooled Shotgun Sequencing (CAPSS) method, which delivers a BAC-linked assembly of a whole genome sequence. Secondly, we introduce a novel physical mapping method, called Clone-Array Pooled Shotgun Mapping (CAPS-MAP), which computes the physical ordering of BACs in a random library. Both CAPSS and CAPS-MAP construct subclone libraries from pooled genomic BAC clones. We propose algorithmic and experimental improvements that make CAPSS a viable option for sequencing a set of BACs. We provide the first probabilistic model of CAPSS sequencing progress. The model leads to theoretical results supporting previous, less formal arguments on the practicality of CAPSS. We demonstrate the usefulness of CAPS-MAP for clone overlap detection with a probabilistic analysis, and a simulated assembly of the Drosophila melanogaster genome. Our analysis indicates that CAPS-MAP is well-suited for detecting BAC overlaps in a highly redundant library, relying on a low amount of shotgun sequence information. Consequently, it is a practical method for computing the physical ordering of clones in a random library, without requiring additional clone fingerprinting. Since CAPS-MAP requires only shotgun sequence reads, it can be seamlessly incorporated into a sequencing project with almost no experimental overhead.

q-bio.GN↗