Virtualization obfuscators are commonly employed to safeguard proprietary code or to impede malware analysis. Despite significant efforts to combat these obfuscators over the past decade, code virtualization continues to be an exceedingly effective obfuscation technique. At the core of modern virtualization obfuscators are the virtual machines (VMs), which employ a variety of diversification techniques to complicate their internal structures. Due to its intricate and diverse nature, reverse engineering one VM is a time-consuming task and is not useful in cracking other VMs. Yet, despite the success of these VMs, there has been no systematic study of their diversification techniques, creating a knowledge gap that needs to be addressed to enhance VM deobfuscation. This work aims to bridge the above gap. First, we categorize and unveil the techniques under the hood of VM diversification, from the perspectives of VM interpretation, byte-code organization, and handler permutation/relocation. This systematic knowledge about modern virtualization is a crucial contribution to the field. Second, we develop an automated tool to identify the VM diversification techniques adopted by state-of-the-art virtualization obfuscators. The results demystify how the VM diversification methods are deployed in practice. Third, our research also involves patching current deobfuscation tools using the newly revealed knowledge of VM diversification to overcome their weaknesses. This outcome highlights how the results of our study pave the way for next-generation VM deobfuscation.

MOTIVATION: Trajectory inference methods are essential for extracting temporal ordering from static single-cell transcriptomic profiles, thus facilitating the accurate delineation of cellular developmental hierarchies and cell-fate transitions. However, numerous existing methods treat trajectory inference as an unsupervised learning task, rendering them susceptible to technical noise and data sparsity, which often lead to unstable reconstructions and ambiguous lineage assignments. RESULTS: Here, we introduce BayesTraj, a semi-supervised Bayesian framework that incorporates prior knowledge of lineage topology and marker-gene expression to robustly reconstruct differentiation trajectories from scRNA-seq data. BayesTraj models cellular differentiation as a probabilistic mixture of latent lineages and captures marker-gene dynamics through parametric functions. Posterior inference is conducted using Hamiltonian Monte Carlo (HMC), yielding estimates of pseudotime, lineage proportions, and gene activation parameters. Evaluations on both simulated and real datasets with diverse branching structures demonstrate that BayesTraj consistently outperforms state-of-the-art methods in pseudotime inference. In addition, it provides per-cell branch-assignment probabilities, enabling the quantification of differentiation potential using Shannon entropy and the detection of lineage-specific gene expression via Bayesian model comparison. AVAILABILITY AND IMPLEMENTATION: BayesTraj is written in R and available at https://github.com/SDU-W-Zhanglab/BayesTraj and has been archived on Zenodo (DOI: 10.5281/zenodo.16758038).

MOTIVATION: DNA methylation patterns provide precise and accurate estimates of biological age due to their robustness and predictable changes associated with aging processes. Although several methylation aging clocks have been developed in recent years, they are primarily designed for DNA methylation array data, which has limited CpG coverage and detection sensitivity compared to bisulfite sequencing data. RESULTS: Here, we present BS-clock, a novel DNA methylation clock for human aging based on bisulfite sequencing data. Using BS-seq data from 529 samples retrieved from four tissues, our BS-clock achieves higher correlations with chronological age in multiple tissue types compared to existing array-based clocks. Our study revealed age-dependent aging rates across different age stages and disease conditions, and overall low cross-tissue prediction capability by applying the model trained on one tissue type to others. In summary, BS-clock overcomes limitations of array-based techniques, offering genome-wide CpG site coverage and more robust and accurate aging quantification. This research paves the way for advanced epigenetic studies of aging and holds promise for developing targeted interventions to promote healthy aging. AVAILABILITY AND IMPLEMENTATION: All analysis codes for reproducing the results of the study are publicly available at https://github.com/hucongcong97/BS-clock.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

Obfuscation has been widely employed to protect software from the malicious reverse analysis. However, its security risks have not previously been studied in detail. For example, most obfuscation methods introduce large blocks of opaque code that are black boxes to normal users. In this paper, we show that, indeed, obfuscation can increase the attack risk. Existing gadget search tools, while able to find more gadgets in obfuscated code, do not succeed in assembling them into more exploits. However, these tools use strict pattern matching, greedy searching strategies, and only very simple gadgets. We develop Gadget-Planner, a more flexible approach to building code-reuse attacks that overcomes previous limitations via symbolic execution and automated planning. In a study across both benchmark and real-world programs, this approach finds many more exploit payloads on obfuscated programs, both in terms of number and diversity.

MOTIVATION: Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. RESULTS: Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein-protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. AVAILABILITY AND IMPLEMENTATION: The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.

BACKGROUND: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. RESULTS: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. CONCLUSION: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

MOTIVATION: The increasing amount of time-series single-cell RNA sequencing (scRNA-seq) data raises the key issue of connecting cell states (i.e. cell clusters or cell types) to obtain the continuous temporal dynamics of transcription, which can highlight the unified biological mechanisms involved in cell state transitions. However, most existing trajectory methods are specifically designed for individual cells, so they can hardly meet the needs of accurately inferring the trajectory topology of the cell state, which usually contains cells assigned to different branches. RESULTS: Here, we present CStreet, a computed Cell State trajectory inference method for time-series scRNA-seq data. It uses time-series information to construct the k-nearest neighbor connections between cells within each time point and between adjacent time points. Then, CStreet estimates the connection probabilities of the cell states and visualizes the trajectory, which may include multiple starting points and paths, using a force-directed graph. By comparing the performance of CStreet with that of six commonly used cell state trajectory reconstruction methods on simulated data and real data, we demonstrate the high accuracy and high tolerance of CStreet. AVAILABILITY AND IMPLEMENTATION: CStreet is written in Python and freely available on the web at https://github.com/TongjiZhanglab/CStreet and https://doi.org/10.5281/zenodo.4483205. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The rapid development of computer science and technology represented by artificial intelligence is fundamentally affecting man's lifestyle. Big data is pushing forward the development of artificial intelligence to unprecedented levels. The typical application, as one small element of the big data, refers to the knowledge engineering which sets the knowledge map as the core. A knowledge map is the knowledge base of the Semantic Web. Semantic web stands for a network describing things in the way that is understood by computers. The purpose of it is to enable computers to understand data and build a knowledge base of computers. By modeling the things that can be described, filling the attributes of things and expanding the connection with other things, the knowledge base can be built. This paper aims to propose a semantic information metadata model based on the ontology of traditional Chinese medicine and construct an ontology database with metadata model as the knowledge description method to realize keyword search by using a combination of ontology and Chinese medical knowledge data. Finally, achieving ontology database construction and visualization of ontologies.

MOTIVATION: In cancer genomics research, one important problem is that the solid tissue sample obtained from clinical settings is always a mixture of cancer and normal cells. The sample mixture brings complication in data analysis and results in biased findings if not correctly accounted for. Estimating tumor purity is of great interest, and a number of methods have been developed using gene expression, copy number variation or point mutation data. RESULTS: We discover that in cancer samples, the distributions of data from Illumina Infinium 450 k methylation microarray are highly correlated with tumor purities. We develop a simple but effective method to estimate purities from the microarray data. Analyses of the Cancer Genome Atlas lung cancer data demonstrate favorable performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: The method is implemented in InfiniumPurify, which is freely available at https://bitbucket.org/zhengxiaoqi/infiniumpurify. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

INSIGHT, an innovate graduate STEM Fellowship Program integrates sensor technology and computer science within in a K-12 standards-based science, technology, and engineering curricula. Graduate STEM Fellows are teamed with science, technology, and physical education teachers for two years to carry out hands-on classroom activities utilizing technology and engineering practice with a focus on the use of sensors, computing, and information technology aligned with K-12 state curriculum standards. One of the projects main goals is the establishment of sensor, computing, and information technology as a foundational high school skill by accelerating the integration of sensor technology content into K-12 classrooms. This project encourages participation in engineering and technology from a wider, more diverse group of students from rural Kansas. This paper shares detailed examples of summer institute and academic-year K-12 activities that have been successful. It also provides a preliminary assessment of the project.