Large Language Models (LLMs) offer significant advantages in Natural Language Processing, leading to recent studies on their application in Knowledge Tracing (KT). By lever-aging LLMs' pre-trained knowledge and incorporating student interaction data into prompts, the cold-start problem can be alleviated, enabling effective student modeling with limited prior information. In this study, we investigate how self-consistency prompting can enhance KT in real-world teaching scenarios. To this end, we utilize four types of datasets, each containing learning processes for solving exercises in a fixed order within specific mathematical domains. Given the limited size of these datasets, a training-free approach using LLMs is particularly beneficial. Our first experiment applies self-consistency prompting and average aggregation to KT, demonstrating improved AUC. The second experiment explores the impact of additional textual information-such as exercise statements, solution processes, correct answers, and image descriptions-on performance. Our findings highlight the potential of LLM-based KT models in adaptive learning environments and their ability to generalize with limited training data.

SUMMARY: For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20 000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within reasonable time and memory limits in personal computing environments. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://bioconductor.org/packages/gpart. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation. Results: We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods. Availability and implementation: Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Proteins interact with each other to regulate their functionality and localization. The accumulated protein interaction evidences are represented by protein interaction network using a graph abstraction. Topological properties of protein interaction networks have been explored to characterize proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. While one group of researchers have made efforts to simulate current protein interaction starting from hypothetical infant state of protein interaction networks through suggested evolutionary models, another group of researchers have made efforts to estimate the phylogenetic age of proteins from evolutionary relationship. Recently, these efforts gave rise to the database of phylogenetic age of proteins and this allows many researchers to estimate phylogenetic age of proteins of their interest easily. As seen by their terms, the evolutionary model of protein interaction networks and phylogenetic age of proteins are closely related, thus topological properties of protein interactions, which is important in studies of the evolutionary models, can be linked to the phylogenetic age of proteins. In this paper, we construct a weighted human protein interaction network from a human protein interaction network, which is provided by BioGRID database. The weight of an edge is defined as the number of triangles which contains this edge in the protein interaction network and we call this weight as the triangle score. From the weighted protein interaction network, we extract proteins that are incident to an edge that has a high triangle score. We obtain phylogenetic age of proteins and measure various statistical values to observe correlation between phylogenetic age and the triangle score. As a result, we show that the proteins, that participate in interactions with high triangle score, are old in terms of phylogenetic age. Also we show that for interactions within a same phylogenetic age category tend to have higher triangle scores than the interactions with only one of its participant protein contained in the given age category.

Linkage disequilibrium structure (LD) is the main source of the study of population genetics and disease-gene association. Especially, analyzing extended long haplotypes carrying a derived allele and examining LD block patterns can provide evidence for positive selection. We investigated the LD block structure of East Asian, European, and African populations for the previously reported sites of positive selection by comparing LD block construction results based on 1000 Genomes Project data. We confirmed that differences of LD block size in EDAR, LCT, PCDH15, and LARGE region among different populations is consistent with previous reports. We found new evidence for positive selection in SLC30A19, PDE11A and BCAS3 in East Asian and European populations based on the LD block patterns.

MOTIVATION: Killer immunoglobulin-like receptor (KIR) genes vary considerably in their presence or absence on a specific regional haplotype. Because presence or absence of these genes is largely detected using locus-specific genotyping technology, the distinction between homozygosity and hemizygosity is often ambiguous. The performance of methods for haplotype inference (e.g. PL-EM, PHASE) for KIR genes may be compromised due to the large portion of ambiguous data. At the same time, many haplotypes or partial haplotype patterns have been previously identified and can be incorporated to facilitate haplotype inference for unphased genotype data. To accommodate the increased ambiguity of present-absent genotyping of KIR genes, we developed a hybrid approach combining a greedy algorithm with the Expectation-Maximization (EM) method for haplotype inference based on previously identified haplotypes and haplotype patterns. RESULTS: We implemented this algorithm in a software package named HAPLO-IHP (Haplotype inference using identified haplotype patterns) and compared its performance with that of HAPLORE and PHASE on simulated KIR genotypes. We compared five measures in order to evaluate the reliability of haplotype assignments and the accuracy in estimating haplotype frequency. Our method outperformed the two existing techniques by all five measures when either 60% or 25% of previously identified haplotypes were incorporated into the analyses. AVAILABILITY: The HAPLO-IHP is available at http://www.soph.uab.edu/Statgenetics/People/KZhang/HAPLO-IHP/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.