The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the enormous search space containing the candidates and the substantial computational cost of high-fidelity property prediction models make screening practically challenging. In this work, we propose a general framework for constructing and optimizing a high-throughput virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return on computational investment. Based on both simulated and real-world data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate virtual screening without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

Recent advances in objective-based uncertaintyquantification (objective-UQ) have shown that such a goal-driven approach for quantifying model uncertainty is extremely usefulin real-world problems that aim at achieving specific objectives based on complex uncertain systems. Central to this objective-UQ is the concept of mean objective cost of uncertainty (MOCU), which provides effective means of quantifying the impact of uncertainty on the operational goals at hand. MOCU is especially useful for optimal experimental design (OED) as the potential efficacy of an experimental (or data acquisition) campaign can be quantified by estimating the MOCU that is expected to remain after the campaign. However, MOCU-based OED tends to be computationally expensive, which limits its practical applicability. In this paper, we propose a novel machine learning (ML) scheme that can significantly accelerate MOCU computation and expedite MOCU-based experimental design. The main idea is to use an ML model to efficiently search for the optimal robust operator under model uncertainty, a necessary step for computing MOCU. We apply the proposed ML-based OED acceleration scheme to design experiments aimed at optimally enhancing the control performance of uncertain Kuramoto oscillator models. Our results show that the proposed scheme results in up to ${154}$ -fold speed improvement without any degradation of the OED performance.

Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important similarities and critical differences between networks in terms of their topology and composition. A major impediment to advancing network alignment techniques has been the lack of gold-standard benchmarks that can be used for accurate and comprehensive performance assessment of such algorithms. The original NAPAbench (network alignment performance assessment benchmark) was developed to address this problem, and it has been widely utilized by many researchers for the development, evaluation, and comparison of novel network alignment techniques. In this work, we introduce NAPAbench 2—a major update of the original NAPAbench that was introduced in 2012. NAPAbench 2 includes a completely redesigned network synthesis algorithm that can generate protein-protein interaction (PPI) network families whose characteristics closely match those of the latest real PPI networks. Furthermore, the network synthesis algorithm comes with an intuitive GUI that allows users to easily generate PPI network families with an arbitrary number of networks of any size, according to a flexible user-defined phylogeny. In addition, NAPAbench 2 provides updated benchmark datasets—created using the redesigned network synthesis algorithm—which can be used for comprehensive performance assessment of network alignment algorithms and their scalability.

16S rRNA amplicon sequencing has been an effective method for profiling microbial taxonomy in microbiome research, as it offers lower per-sample costs and higher sample throughput than shotgun metagenomics. Although 16S rRNA sequencing offers clear advantages over shotgun sequencing, it depends on precise trimming of low-quality bases at the 3′ ends of reads. Given the widespread use of 16S rRNA amplicon sequencing, there is an increasing demand for analysis tools that can identify errors in the 3′ region of reads and remove erroneous bases. While various algorithms for predicting trim locations are widely employed, most are command-line standalone tools, which pose challenges for users with limited computational background or resources. Furthermore, in the absence of biological or experimental priors such as amplicon size, trim position predictions may be unreliable. Here, we introduce PixelCut, a fully automated trim-position prediction framework that requires no hyperparameters or prior biological information for accurate prediction. Unlike most available algorithms that operate on raw FASTQ data, PixelCut analyzes the per-base quality report generated by FastQC to infer trimming positions. Based on the recommended quality score threshold from the quality report, PixelCut inspects the quality scores across bases and automatically determines the recommended trim position using character recognition techniques based on computer vision. We have also developed a user-friendly web application to make the method accessible to those without programming expertise, while offering a command-line version for advanced users. Through comprehensive computer simulations, we show that PixelCut produces taxonomic profiling results that are consistent with those from popular trim-location prediction algorithms.