MOTIVATION: The assessment of aberrant transcription events in rare disease patients holds great promise for enhancing the prioritization of causative genes-a strategy already widely adopted in clinical settings to improve diagnostic accuracy. Nevertheless, the accurate identification of causal genes remains a substantial challenge. RESULTS: We propose AXOLOTL, a novel ensemble method for identifying aberrant gene expression events in RNA expression matrices. AXOLOTL effectively accounts for gene correlation by incorporating coexpression constraints. We demonstrated the superior performance of AXOLOTL on representative RNA-seq datasets, including those from the GTEx healthy cohort, mitochondrial disease cohorts, and collagen VI-related dystrophy cohorts. Furthermore, we applied AXOLOTL to real-world cases of neurological disorders and demonstrated its ability to accurately identify aberrant gene expression and facilitate the prioritization of pathogenic variants. AVAILABILITY AND IMPLEMENTATION: AXOLOTL is freely available on GitHub (https://github.com/xuwenjian85/axolotl) and Zenodo (https://doi.org/10.5281/zenodo.17940844).

Subtype classification of medical images is a key challenge in medical diagnosis. However, due to scarce labeled data, data quality issues, and poor generalization ability of existing models, certain limitations exist. This study proposes a dynamic prototype multi-model fusion (DPF) framework based on a feature screening mechanism. The framework integrates ResNet and Vision Transformer architectures to extract complementary feature representations and constructs robust prototypes through distance-based threshold setting and voting mechanisms. A feature screening layer distinguishes high-quality from lowquality unlabeled samples using similarity and occurrence maps, while a collaborative training strategy utilizes both sample types to optimize prototype representations. The dynamic prototype expansion mechanism enables real-time adaptation to data distribution changes through self-training. Comprehensive experiments are conducted on two medical imaging datasets, including one public breast MRI dataset and one private CT liver dataset. The proposed framework achieves superior performance with accuracy improvements over supervised baselines and existing semi-supervised methods. The results verify the framework's effectiveness in addressing medical image heterogeneity, sample quality variations, and limited labeled data availability.

To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural heterogeneity, and information incompleteness of resume texts. To this end, we propose MHSNet, an multi-level identity verification framework that fine-tunes BGE-M3 using contrastive learning. With the fine-tuned BGE-M3, MHSNet generates multi-level sparse and dense representations for resumes, enabling the computation of corresponding multi-level semantic similarities. Moreover, the state-aware Mixture-of-Experts (MoE) is employed in MHSNet to handle diverse incomplete resumes. Experimental results verify the effectiveness of MHSNet.

The manufacturing environment and activities with a large volume and variety of product data have put forward higher requirements for the proof and verification of identity information. Achieving decentralized digital identity management in the Industrial Internet of Things (IIoT) helps to improve the performance of relevant proofs and authentication. The Decentralized Identity (DID) system serves as a bridge between the physical and digital worlds, assigning digital identities to physical entities to facilitate their participation in online activities. However, faced with the huge number of manufacturing entities accessing the DID system, the number of DID documents in the system has proliferated. It is still a big challenge to improve the scalability of the system while ensuring the efficiency of information access and verification. In this paper, we propose a blockchain-based verifiable decentralized identity system for IIoT. First, we propose a blockchain-based system architecture with a specially designed storage structure for DID documents. Specifically, we design a structure based on Merkle Tree that visually summarises the physical associations of manufacturing entities and reduces access overhead. Second, we design a multiblock storage structure within the blockchain, which establishes inter-block jumps based on the associated DID, effectively improving the query efficiency of the system. Finally, we design a verification scheme that enables users to verify the integrity of the identity data of the proof provider. We implemented the system framework and conducted experiments to evaluate the performance of our system. The experimental results proved the effectiveness of the system.

In computational social systems, recommendation functionality plays a pivotal role in influencing user behavior, enhancing user experience, and driving engagement. To help recommendation functionality to better suggest relevant content or items, the social platforms usually utilize large-scale knowledge discovery techniques to analyze trends in user interactions and extract patterns from large datasets. Click-through rate (CTR) prediction is crucial in recommendation systems for measuring effectiveness, understanding user behavior, training and optimizing models, impacting business outcomes, enhancing personalization, and identifying issues. It provides actionable insights that assist in continuously refining and improving the recommendation process. Traditional deep learning-based CTR prediction models cannot work well for recommendation in social systems due to the data sparsity and the long-tail data problems since the representation learned from the user behavior is basically dominated by the major part of the data. In this article, we propose a multitask-based self-supervised learning model (MTSSL) that can better deal with sparse and long-tail user interaction data. Specifically, we first transform the CTR prediction task into the multitask joint learning framework with a set of shared subnetworks. Each subnetwork learns a representation of the entire user data, and hence, the sparse and long-tail data would have opportunity to fall into the best matched representation space of historical user behavior. Moreover, two kinds of self-supervision signals are employed to guide the learning of the representations. Extensive experiments over four user interaction datasets demonstrate the superiority of our proposed MTSSL over state-of-art models for recommendations. In terms of online A/B test, our model achieves around 3% better performance than the counterparts.

The parallel measurement of transcriptome and proteome revealed unmatched profiles. Since proteomic analysis is more expensive and challenging than transcriptomic analysis, the question of how to use messenger RNA (mRNA) expression data to predict protein level is extremely important. Here, we comprehensively evaluated 13 machine learning models on inferring protein expression levels using RNA expression profile. A total of 20 proteogenomic datasets from three mainstream proteomic platforms with >2500 samples of 13 human tissues were collected for model evaluation. Our results highlighted that the appropriate feature selection methods combined with classical machine learning models could achieve excellent predictive performance. The voting ensemble model outperformed other candidate models across datasets. Adding the mRNA proxy model to the regression model further improved the prediction performance. The dataset and gene characteristics could affect the prediction performance. Finally, we applied the model to the brain transcriptome of cerebral cortex regions to infer the protein profile for better understanding the functional characteristics of the brain regions. This benchmarking work not only provides useful hints on the inherent correlation between transcriptome and proteome, but also has practical value of the transcriptome-based prediction of protein expression levels.

Emerging spatial/mobile crowdsourcing service platforms enable workers (i.e., crowd) to complete spatial crowdsourcing tasks (e.g., taking photos, verifying data on-site, delivery) that are tagged with rewards, time and location features. In this paper, we develop online route recommendation service for a mobile crowdsourcing worker, such that he can (i) reach his destination on time and (ii) receive the maximum reward from spatial crowdsourcing tasks along the route. We show that no online algorithm can compute the optimal route. Then, we propose effective heuristics to compute routes with high reward, and present efficient techniques to accelerate their computation. Experimental results on real datasets show that our proposed heuristics incur low response time and produce high-reward routes (yielding 80 percent of the optimal reward for on-site tasks and 70 percent of the optimal reward for delivery tasks).

MOTIVATION: Gene expression profiling is widely used in basic and cancer research but still not feasible in many clinical applications because tissues, such as brain samples, are difficult and not ethnical to collect. Gene expression in uncollected tissues can be computationally inferred using genotype and expression quantitative trait loci. No methods can infer unmeasured gene expression of multiple tissues with single tissue gene expression profile as input. RESULTS: Here, we present a Bayesian ridge regression-based method (B-GEX) to infer gene expression profiles of multiple tissues from blood gene expression profile. For each gene in a tissue, a low-dimensional feature vector was extracted from whole blood gene expression profile by feature selection. We used GTEx RNAseq data of 16 tissues to train inference models to capture the cross-tissue expression correlations between each target gene in a tissue and its preselected feature genes in peripheral blood. We compared B-GEX with least square regression, LASSO regression and ridge regression. B-GEX outperforms the other three models in most tissues in terms of mean absolute error, Pearson correlation coefficient and root-mean-squared error. Moreover, B-GEX infers expression level of tissue-specific genes as well as those of non-tissue-specific genes in all tissues. Unlike previous methods, which require genomic features or gene expression profiles of multiple tissues, our model only requires whole blood expression profile as input. B-GEX helps gain insights into gene expressions of uncollected tissues from more accessible data of blood. AVAILABILITY AND IMPLEMENTATION: B-GEX is available at https://github.com/xuwenjian85/B-GEX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.

BACKGROUND: Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly. RESULTS: To address this challenge, we developed NFPscanner, a web server for deciphering gene networks with pathway associations. Adapted from a recently reported knowledge-based framework called network fingerprint, NFPscanner integrates the annotated pathways of 7 databases, 4 algorithms, and 2 graphical visualization modules into a webtool. It implements 3 types of network analysis: Fingerprint: Deciphering gene networks and highlighting inherent pathway modules Alignment: Discovering functional associations by finding optimized node mapping between 2 gene networks Enrichment: Calculating and visualizing gene ontology (GO) and pathway enrichment for genes in networks Users can upload gene networks to NFPscanner through the web interface and then interactively explore the networks' functions. CONCLUSIONS: NFPscanner is open-source software for non-commercial use, freely accessible at http://biotech.bmi.ac.cn/nfs .

Sorting is a crucial operation that could be used to implement SQL operators such as GROUP BY, ORDER BY, and SQL:2003 PARTITION BY. Queries with multiple attributes in those clauses are common in real workloads. When executing queries of that kind, state-of-the-art main-memory column-stores require one round of sorting per input column. With the advent of recent fast scans and denormalization techniques, that kind of multi-column sorting could become a bottleneck. In this paper, we propose a new technique called "code massaging", which manipulates the bits across the columns so that the overall sorting time can be reduced by eliminating some rounds of sorting and/or by improving the degree of SIMD data level parallelism. Empirical results show that a main-memory column-store with code massaging can achieve speedup of up to 4.7X, 4.7X, 4X, and 3.2X on TPC-H, TPC-H skew, TPC-DS, and real workload, respectively.

Due to the fact that existing database systems are increasingly more difficult to use, improving the quality and the usability of database systems has gained tremendous momentum over the last few years. In particular, the feature of explaining why some expected tuples are missing in the result of a query has received more attention. In this paper, we study the problem of explaining missing answers to top-k queries in the context of SQL (i.e., with selection, projection, join, and aggregation). To approach this problem, we use the query-refinement method. That is, given as inputs the original top-k SQL query and a set of missing tuples, our algorithms return to the user a refined query that includes both the missing tuples and the original query results. Case studies and experimental results show that our algorithms are able to return high quality explanations efficiently.

A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial preference (i.e., her current location and future locations) and non-spatial preference (i.e., her interest). Existing LANF systems simply send the most relevant geo-tagged messages to their users. Unfortunately, the major limitation of such an existing approach is that, a news feed may contain messages related to the same location (i.e., point-of-interest) or the same category of locations (e.g., food, entertainment or sport). We argue that diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities. In this paper, we propose D-MobiFeed; a new LANF system enables a user to specify the minimum number of message categories (h) for the messages in a news feed. In D-MobiFeed, our objective is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized. To achieve this objective, we formulate the problem into two parts, namely, a decision problem and an optimization problem. For the decision problem, we provide an exact solution by modeling it as a maximum flow problem and proving its correctness. The optimization problem is solved by our proposed three-stage heuristic algorithm. We conduct a user study and experiments to evaluate the performance of D-MobiFeed using a real data set crawled from Foursquare. Experimental results show that our proposed three-stage heuristic scheduling algorithm outperforms the brute-force optimal algorithm by at least an order of magnitude in terms of running time and the relative error incurred by the heuristic algorithm is below 1 percent. D-MobiFeed with the location prediction method effectively improves the relevance, diversity, and efficiency of news feeds.

Efficient main-memory index structures are crucial to main-memory database systems. Adaptive Radix Tree (ART) is the most recent in-memory index structure. ART is designed to avoid cache miss, leverage SIMD data parallelism, minimize branch mis-prediction, and have small memory footprint. When an in-memory index structure like ART has significantly few cache misses and branch mis-predictions, it is natural to question whether misses in Translation Lookaside Buffer (TLB) matters. In this paper, we try to confirm whether this is the case and if the answer is positive, what are the measures that we can take to alleviate that and how effective they are.

Data market is an emerging type of cloud service that enables a data owner to sell their data sets in a public cloud. Buyers who are interested in a certain dataset can access the data in the mar-ket via a RESTful API. Accessing data in the data market may not be free. For example, it costs USD 12 per month to obtain 100 “transactions ” from the WorldWide Historical Weather dataset in Windows Azure Data Marketplace, where a transaction is a unit of result size (e.g., a query result of 4400 records would consume 44 transactions as Windows Azure Data Marketplace confines one transaction to 100 records). Therefore, in this paper, we present PayLess, a system that helps data buyers to optimize their queries so that they can obtain the query results by paying less to the data sellers. Experiments over synthetic data and real data sets in Win-dows Azure Marketplace show that PayLess can cost-effectively handle SQL query processing over data markets. 1.

Scan and lookup are two core operations in main memory column stores. A scan operation scans a column and returns a result bit vector that indicates which records satisfy a filter. Once a column scan is completed, the result bit vector is converted into a list of record numbers, which is then used to look up values from other columns of interest for a query. Recently there are several in-memory data layout proposals that aim to improve the performance of in-memory data processing. However, these solutions all stand at either end of a trade-off --- each is either good in lookup performance or good in scan performance, but not both. In this paper we present ByteSlice, a new main memory storage layout that supports both highly efficient scans and lookups. ByteSlice is a byte-level columnar layout that fully leverages SIMD data-parallelism. Micro-benchmark experiments show that ByteSlice achieves a data scan speed at less than 0.5 processor cycle per column value --- a new limit of main memory data scan, without sacrificing lookup performance. Our experiments on TPC-H data and real data show that ByteSlice offers significant performance improvement over all state-of-the-art approaches.

Recently, Amazon has announced Redshift, a Parallel-Database-as-a Service (PDaaS). Redshift adopts the "virtual cluster" approach to implement multitenancy, which has the merit of hard isolation among tenants (i.e., tenants do not interfere even when sharing resources). However, that benefit comes with poor resource utilization due to the significant redundancy incurred in the resources. In this demonstration, we present Thrifty, a Parallel-Database-as-a-Service operated using the "shared-process" approach. Compared with Redshift, each tenant in Thrifty does not occupy an exclusive amount of resource but share the database processes together, leading to better resource utilization. To avoid contention among tenants, Thrifty uses a proper cluster design, a tenant placement scheme, and a query routing mechanism to achieve soft isolation. In the demonstration, an attendee will be invited to register with Thrifty as a tenant to rent a parallel database instance. Then the attendee will be allowed to view the dashboard of a Thrifty's administrator. Next, the attendee will be invited to control (e.g., increase) the workload of the tenant so as to see how Thrifty carries out online re- consolidation and elastic scaling.

A temporary social network (TSN) is confined to a specific place (e.g., hotel and shopping mall) or activity (e.g., concert and exhibition) in which the TSN service provider allows nearby third party vendors (e.g., restaurants and stores) to advertise their goods or services to its registered users. However, simply broadcasting all the vendors' advertisements to all the users in the TSN may cause the service provider to lose its fans. In this paper, we present Capacity-Aware Location-Based Advertising (CALBA), which is a framework designed for TSNs to select vendors as advertising sources for mobile users. In CALBA we measure the relevance of a vendor to a user by considering their geographical proximity and the user's preferences. Our goal is to maximize the overall relevance of selected vendors for a user with the constraint that the total advertising frequency of the selected vendors should not exceed the user's specified capacity. First, we model the snapshot selection problem as 0-1 knapsack and solve it using an approximation method. Then, CALBA keeps track of the selection result for moving users by employing a safe region technique that can reduce its computational cost. We also propose three pruning rules and a unique access order to effectively prune vendors which could not affect a safe region, in order to improve the efficiency of the client-side computation. We evaluate the performance of CALBA based on a real location-based social network data set crawled from Foursquare. Experimental results show that CALBA outperforms a naïve approach which periodically invokes the snapshot vendor selection.

A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. The location prediction function is designed to predict a mobile user's locations based on an existing path prediction algorithm. The relevance measure function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. To ensure that MobiFeed can scale up to a larger number of messages, we design a heuristic news feed scheduler.

UNLABELLED: The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. AVAILABILITY: PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape.org/plugins/index.php or http://biotech.bmi.ac.cn/PerturbationAnalyzer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.