Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their effects among different publications. We developed language model-based metaProbiotics rapidly detect bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes probiotic-treated individuals, it revealed the probioticity of intervention strains-derived and...

Many biological properties of phages are determined by phage virion proteins (PVPs), and the poor annotation PVPs is a bottleneck for many areas viral research, such as phylogenetic analysis, host identification, antibacterial drug design. Because high diversity PVP sequences, genome remains particularly challenging bioinformatic task.

Mobilization typing (MOB) is a classification scheme for plasmid genomes based on their relaxase gene. The host ranges of plasmids different MOB categories are diverse, and crucial investigating mobilization, especially the transmission resistance genes virulence factors. However, metagenomic data challenging due to highly fragmented characteristics contigs.

The prediction of the plasmid host range is crucial for investigating dissemination plasmids and transfer resistance virulence genes mediated by plasmids. Several machine learning-based tools have been developed to predict ranges. These trained tested based on bacterial records in related databases. Typically, a genome databases such as National Center Biotechnology Information annotated with only one or few hosts, which does not encompass all possible hosts. Consequently, existing methods...

In the past, symbolic trajectory evaluation (STE) has been shown to be effective for verifying individual array blocks. However, when applying STE verify multiple blocks together as a single system, run-time OBDD (ordered boolean decision diagrams) sizes would often blow up. this paper, we propose use of both an ATPG-based justification engine and simulation facilitate application proof methodology systems. Our method translates given verification problem instance into ATPG objectives,...

In the past, Symbolic Trajectory Evaluation (STE) was shown to be effective for verifying individual array blocks. However, when applying STE verify multiple blocks together as a single system, run-time OBDD sizes would often blow up. this paper, we propose using dual-rail symbolic simulation scheme facilitate application of proof methodology systems. The proposed implicitly partitions given design into control domain and data-path domain, is carried out on both domains. With scheme, during...

Abstract Background MOB typing is a classification scheme that classifies plasmid genomes based on their relaxase gene. The host range of plasmids different categories are diverse and crucial for investigating the mobilization plasmid, especially transmission resistance genes virulence factors. However, metagenomic data challenging due to highly fragmented characteristic contigs. Results We developed MOBFinder, an 11-class classifier classify fragments into 10 non-mobilizable category. first...

In the past, symbolic trajectory evaluation (STE) was shown to be effective for verifying individual array blocks. However, when applying STE verify multiple blocks together as a single system, run-time OBDD sizes would often blow up. this paper, we propose using "dual-rail" simulation scheme facilitate application of proof methodology systems. The proposed implicitly partitions given design into control domain and datapath domain, is carried out on both domains. With scheme, during each can...

Abstract The prediction of the plasmid host range is crucial for investigating dissemination plasmids and transfer resistance virulence genes mediated by plasmids. Several machine learning-based tools have been developed to predict ranges. These trained tested based on bacterial records in related databases. Typically, a genome databases such as NCBI annotated with only one or few hosts, which does not encompass all possible hosts. Consequently, existing methods may significantly...

This paper presents a functional-space decomposition approach to enhance the capability of symbolic simulation. In our simulator, control part and data path circuit is separated, their simulated results are recorded in different domains. A 2-tuple list structure used separate datapath Then, functional sub-space domain can further be decomposed order achieve optimal OBDD size run time. We demonstrate effectiveness based on simulation arithmetic units.

This paper presents a simulation-based methodology for extracting simplified view of design's input-output behavior. Such design can be used to facilitate test pattern justification from the outputs module inputs module. In this paper, extraction simplification is formulated as learning problem. With scheme word-level functions, core problem becomes developing an efficient Boolean learner. We discuss implementation such learner and compare its performance with one best-known algorithms,...

In this paper, we provide a flexible and automatic method to partition the functional space for efficient symbolic simulation. We utilize 2-tuple list representation as basis partitioning space. The is carried out dynamically during simulation based on sizes of OBDDs. develop heuristics choosing optimal points. These intend balance tradeoff between time complexity. demonstrate effectiveness our new approach through experiments floating point adder memory management unit.

ABSTRACT The poor annotation of phage virion protein (PVP) is the bottleneck many areas viral research, such as phylogenetic analysis, host identification and antibacterial drug design. Because high diversity PVP sequences, remains a great challenging bioinformatic task. Based on deep learning, we present DeePVP that contains main module an extended module. aims to identify PVPs from non-PVP over genome, while can further classify predicted into one ten major classes PVP. Compared with...

In this paper, we provide a flexible and automatic method to partition the functional space for efficient symbolic simulation. We utilize 2-tuple list representation as basis partitioning space. The is carried out dynamically during simulation based on sizes of OBDDs. develop heuristics choosing optimal points. These intend balance tradeoff between time complexity. demonstrate effectiveness our new approach through experiments floating point adder memory management unit.

In this article, we propose a symbolic simulation method where Boolean functions can be efficiently manipulated through 2-domain partitioned OBDD data structure. The functional partition is applied by automatically exploring the key decision points implicitly built inside circuit. help to significantly reduce sizes, solving problems that could not solved with monolithic We demonstrate performance of approach several benchmark circuits complex control logics and datapath. based on also in...

In this paper, we propose a symbolic simulation method where Boolean functions can be efficiently manipulated through 2-domain partitioned OBDD data structure. The functional partition is applied based on the key decision points in circuit. We demonstrate that an RTL model extracted automatically to facilitate verification at gate level. experiments show help significantly reduce size both and level circuit, solving problems could not solved with monolithic performance of approach shown...