While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizons poses significant challenges, hindering iLQR from being an effective differentiable controller. This paper introduces DiLQR, a framework that facilitates differentiation through iLQR, allowing it to serve as a trainable and differentiable module, either as or within a neural network. A novel aspect of this framework is the analytical solution that it provides for the gradient of an iLQR controller through implicit differentiation, which ensures a constant backward cost regardless of iteration, while producing an accurate gradient. We evaluate our framework on imitation tasks on famous control benchmarks. Our analytical method demonstrates superior computational performance, achieving up to $\textbf{128x}$ speedup and a minimum of $\textbf{21x}$ speedup compared to automatic differentiation. Our method also demonstrates superior learning performance ($\mathbf{10^6x}$) compared to traditional neural network policies and better model loss with differentiable controllers that lack exact analytical gradients. Furthermore, we integrate our module into a larger network with visual inputs to demonstrate the capacity of our method for high-dimensional, fully end-to-end tasks. Codes can be found on the project homepage https://sites.google.com/view/dilqr/.

Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges, such as poor sample efficiency, generalization, and the need for well-designed reward functions to guide learning effectively. On the other hand, controllers based on complete system dynamics do not require data. This paper addresses the intermediate situation where there is not enough model information for complete controller design, but there is enough to suggest that a model-free approach is not the best approach either. By carefully decoupling known and unknown information about the system dynamics, we obtain an embedded controller guided by our partial model and thus improve the learning efficiency of an RL-enhanced approach. A modular design allows us to deploy mainstream RL algorithms to refine the policy. Simulation results show that our method significantly improves sample efficiency compared with standard RL methods on continuous control tasks, and also offers enhanced performance over traditional control approaches. Experiments on a real ground vehicle also validate the performance of our method, including generalization and robustness.

Visual anomaly detection aims at classifying and locating the regions that deviate from the normal appearance. Embedding-based methods and reconstruction-based methods are two main approaches for this task. The embedding-based methods typically predict the anomaly by measuring the distances between the deep representations of the test samples and a limited number of nominal samples, which enables these methods to be efficient but struggle in providing a fine-grained pixel-level anomaly location. The reconstruction-based methods rely on the pixel-level reconstruction errors to locate the anomaly, thereby the anomaly predictions are fine-grained. However, there are repetitive feature extractions and usually extra modules to guarantee the quality of the reconstructed images, resulting in unsatisfactory detection efficiency. In a nutshell, the prior methods are either not efficient or not precise enough for the industrial detection. To deal with this problem, we derive POUTA (Produce Once Utilize Twice for Anomaly detection), which improves both the accuracy and efficiency by reusing the discriminant information potential in the reconstructive network. We observe that the encoder and decoder representations of the reconstructive network are able to stand for the features of the original and reconstructed image respectively. And the discrepancies between the symmetric reconstructive representations provides roughly accurate anomaly information. To refine this information, a coarse-to-fine process is proposed in POUTA, which calibrates the semantics of each discriminative layer by the high-level representations and supervision loss. Equipped with the above modules, POUTA is endowed with the ability to provide a more precise anomaly location than the prior arts. Besides, the representation reusage also enables to exclude the feature extraction process in the discriminative network, which reduces the parameters and improves the efficiency. Extensive experiments show that, POUTA is superior or comparable to the prior methods with even less cost. Furthermore, POUTA also achieves better performance than the state-of-the-art few-shot anomaly detection methods without any special design, showing that POUTA has strong ability to learn representations inherent in the training data.

The CCCTC-binding factor (CTCF) mediates transcriptional regulation and implicates epigenetic modifications in cancers. However, the systematically unveiling inverse regulatory relationship between CTCF and epigenetic modifications still remains unclear, especially the mechanism by which histone modification mediates CTCF binding. Here, we developed a systematic approach to investigate how epigenetic changes affect CTCF binding. Through integration analysis of CTCF binding in 30 cell lines, we concluded that CTCF generally binds with higher intensity in normal cell lines than that in cancers, and higher intensity in genome regions closed to transcription start sites. To facilitate the better understanding of their associations, we constructed linear mixed-effect models to analyze the effects of the epigenetic modifications on CTCF binding in four cancer cell lines and six normal cell lines, and identified seven epigenetic modifications as potential epigenetic patterns that influence CTCF binding intensity in promoter regions and six epigenetic modifications in enhancer regions. Further analysis of the effects in different locations revealed that the epigenetic regulation of CTCF binding was location-specific and cancer cell line-specific. Moreover, H3K4me2 and H3K9ac showed the potential association with immune regulation of disease. Taken together, our method can contribute to improve the understanding of the epigenetic regulation of CTCF binding and provide potential therapeutic targets for treating tumors associated with CTCF.

Vision-based detection and estimation technologies play crucial roles in target tracking for autonomous vehicles. However, for vision system it is difficult to continuously collect the image information due to occlusion, feature matching failure, etc. This paper is concerned with the target tracking problem for a Mecanum wheeled mobile robot (MWMR) with camera in the presence of intermittent image losses. A switching scheme with two control strategies has been developed to deal with the problem. During the period when the target image is acquired, a stabilizing controller is designed to ensure the convergence of tracking error. When the target image is lost, a predictor is used to provide the controller with estimated tracking error, by which a regular graphic region containing the real position of the target is determined. The stability condition for the tracking system with minimum dwell-time during which the image is acquired has been obtained utilizing the region. Simulation results verify the effectiveness of the proposed methods.

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/.

MOTIVATION: miRNAs play crucial roles in human diseases and newly discovered could be targeted by small molecule (SM) drug compounds. Thus, the identification of small molecule drug compounds (SM) that target dysregulated miRNAs in cancers will provide new insight into cancer biology and accelerate drug discovery for cancer therapy. RESULTS: In this study, we aimed to develop a novel computational method to comprehensively identify associations between SMs and miRNAs. To this end, exploiting multiple molecular interaction databases, we first established an integrated SM-miRNA association network based on 690 561 SM to SM interactions, 291 600 miRNA to miRNA associations, as well as 664 known SM to miRNA targeting pairs. Then, by performing Random Walk with Restart algorithm on the integrated network, we prioritized the miRNAs associated to each of the SMs. By validating our results utilizing an independent dataset we obtained an area under the ROC curve greater than 0.7. Furthermore, comparisons indicated our integrated approach significantly improved the identification performance of those simple modeled methods. This computational framework as well as the prioritized SM-miRNA targeting relationships will promote the further developments of targeted cancer therapies. CONTACT: [email protected], [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Alzheimer's disease (AD) is a severe neurodegenerative disease of the central nervous system that may be caused by perturbation of regulatory pathways rather than the dysfunction of a single gene. However, the pathology of AD has yet to be fully elucidated. RESULTS: In this study, we systematically analyzed AD-related mRNA and miRNA expression profiles as well as curated transcription factor (TF) and miRNA regulation to identify active TF and miRNA regulatory pathways in AD. By mapping differentially expressed genes and miRNAs to the curated TF and miRNA regulatory network as active seed nodes, we obtained a potential active subnetwork in AD. Next, by using the breadth-first-search technique, potential active regulatory pathways, which are the regulatory cascade of TFs, miRNAs and their target genes, were identified. Finally, based on the known AD-related genes and miRNAs, the hypergeometric test was used to identify active pathways in AD. As a result, nine pathways were found to be significantly activated in AD. A comprehensive literature review revealed that eight out of nine genes and miRNAs in these active pathways were associated with AD. In addition, we inferred that the pathway hsa-miR-146a→STAT1→MYC, which is the source of all nine significantly active pathways, may play an important role in AD progression, which should be further validated by biological experiments. Thus, this study provides an effective approach to finding active TF and miRNA regulatory pathways in AD and can be easily applied to other complex diseases.

UNLABELLED: The inappropriate expression of microRNAs (miRNAs) is closely related with disease diagnosis, prognosis and therapy response. Recently, many studies have demonstrated that bioactive small molecules (or drugs) can regulate miRNA expression, which indicates that targeting miRNAs with small molecules is a new therapy for human diseases. In this study, we established the SM2miR database, which recorded 2925 relationships between 151 small molecules and 747 miRNAs in 17 species after manual curation from nearly 2000 articles. Each entry contains the detailed information about small molecules, miRNAs and evidences of their relationships, such as species, miRBase Accession number, DrugBank Accession number, PubChem Compound Identifier (CID), expression pattern of miRNA, experimental method, tissues or conditions for detection. SM2miR database has a user-friendly interface to retrieve by miRNA or small molecule. In addition, we offered a submission page. Thus, SM2miR provides a fairly comprehensive repository about the influences of small molecules on miRNA expression, which will promote the development of miRNA therapeutics. AVAILABILITY: SM2miR is freely available at http://bioinfo.hrbmu.edu.cn/SM2miR/.

The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.