Stochastic Online Learning with Probabilistic Graph Feedback

We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability $p_{ij}$. Two cases are covered. (a) The one-step case, where after playing arm $i$ the learner observes a sample reward feedback of arm $j$ with independent probability $p_{ij}$. (b) The cascade case where after playing arm $i$ the learner observes feedback of all arms $j$ in a probabilistic cascade starting from $i$ -- for each $(i,j)$ with probability $p_{ij}$, if arm $i$ is played or observed, then a reward sample of arm $j$ would be observed with independent probability $p_{ij}$. Previous works mainly focus on deterministic graphs which corresponds to one-step case with $p_{ij} \in \{0,1\}$, an adversarial sequence of graphs with certain topology guarantees, or a specific type of random graphs. We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability

High-order dynamic Bayesian network learning with hidden common causes for causal gene regulatory network

Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes. Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain. Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed

Correcting the impact of docking pose generation error on binding affinity prediction

International audienceAbstractBackgroundPose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes.ResultsAgainst commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses. In this way, the relationship between Vina-generated protein-ligand poses and their binding affinities is directly learned. As a result, test set performance after this error-correcting procedure is much closer to that of predicting the binding affinity in the absence of pose generation error (i.e. on crystal structures). We evaluated several strategies, obtaining better results for those using a single docked pose per ligand than those using multiple docked poses per ligand.ConclusionsBinding affinity prediction is often carried out on the docked pose of a known binder rather than its co-crystallised pose. Our results suggest than pose generation error is in general far less damaging for binding affinity prediction than it is currently believed. Another contribution of our study is the proposal of a procedure that largely corrects for this error. The resulting machine-learning scoring function is freely available at http://istar.cse.cuhk.edu.hk/rf-score-4.tgzand http://ballester.marseille.inserm.fr/rf-score-4.tgz

Field Evaluation of Four Low-cost PM Sensors and Design, Development and Field Evaluation of A Wearable PM Exposure Monitoring System

To mitigate the significant biases/errors in research studying the associations between PM and health, which are introduced by the coarse/inadequate assessments of PM exposure from conventional PM monitoring paradigm, a personalized monitoring system consisting of a low-cost wearable PM device is proposed. However, due to the absence of a unifying evaluation protocol for low-cost PM sensors, the evaluation results/performance specifications from existing studies/datasheets are of limited reference values when attempting to determine the best candidate for the proposed system. In this regard, the authors appeal to the research community to develop a standardized evaluation protocol for low-cost PM sensors/devices, and a unifying attempt is established in this manuscript by adopting the definitive terminology from international documents and the evaluation metrics regarded as best practices. Collocated on the rooftop of the HKUST Supersite, four empirically selected PM sensors were compared against each other and calibrated against two reference monitors. They were then evaluated against the reference following the protocol. The PlanTower PMS-A003 sensor was selected for the wearable device as it outperformed the others in terms of affordability, portability, detection capability, data quality, as well as humidity and condensation insusceptibility. An automated approach was proposed to identify and remove the condensation associated abnormal measurements. The proposed device has better affordability and portability as well as similar usability and data accessibility compared to those existing devices recognized. The first 10 devices were also evaluated and calibrated at the Supersite. Additional 120 units were manufactured and delivered to the subjects to acquire their daily PM2.5 exposures for investigating the association with subclinical atherosclerosis

Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented. This system continuously monitors the users' ECG information to provide personalized health warnings/feedbacks. The users are able to communicate with their paired health advisors through this system for remote diagnoses, interventions, etc. The implemented wearable ECG devices have been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%). To boost the battery life of the wearable devices, a lossy compression schema utilizing the quasi-periodic feature of ECG signals to achieve compression was proposed. Compared to the recognized schemata, it outperformed the others in terms of compression efficiency and distortion, and achieved at least 2x of CR at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable automated AF diagnosis/screening in the proposed system, a ResNet-based AF detector was developed. For the ECG records from the 2017 PhysioNet CinC challenge, this AF detector obtained an average testing F1=85.10% and a best testing F1=87.31%, outperforming the state-of-the-art