Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Updating of Conditional Linear Gaussian Bayesian Networks

This paper presents a method for online updating of conditional distributions in Bayesian network models with both discrete and continuous variables. The method extends known procedures for updating discrete conditional probability distributions with techniques to cope with conditional Gaussian density functions. The method has a solid foundation for known cases and may be generalised by a heuristic scheme for fractional updating when discrete parents are not known. A fading mechanism is described to prevent the system being too conservative as cases accumulate over long time periods. The eﬀect of the online updating is illustrated by an application to predict the number of waiting patients at the emergency department at Aalborg University Hospital.
Improved feature importance computation for tree models based on the Banzhaf value

The Shapley value – a fundamental game-theoretic solution concept – has recently become one of the main tools used to explain predictions of tree ensemble models. Another well-known game-theoretic solution concept is the Banzhaf value. Although the Banzhaf value is closely related to the Shapley value, its properties w.r. t. feature attribution have not been understood equally well. This paper shows that, for tree ensemble models, the Banzhaf value offers some crucial advantages over the Shapley value while providing similar feature attributions.Inparticular, we first give an optimal O ( TL + n ) time algorithm for computing the Banzhaf value-based attribution of a tree ensemble model’s output. Here, T is the number of trees, L is the maximum number of leaves in a tree, and n is the number of features. In comparison, the state-of-the-art Shapley value-based algorithm runs in O ( TLD 2 + n ) time, where D denotes the maximum depth of a tree in the ensemble. Next, we experimentally compare the Banzhaf and Shapley values for tree ensemble models. Both methods deliver essentially the same average importance scores for the studied datasets using two different tree ensemble models (the sklearn implementation of Decision Trees or xgboost implementation of Gradient Boosting Decision Trees). However, our results indicate that, on top of being computable faster, the Banzhaf is more numerically robust than the Shapley value.
A competitive analysis of online failure-aware assignment

Motivated by a new generation of Internet advertising that has emerged in the live streaming e-commerce markets (e.g., Tiktok) over the past ﬁve years, we study a variant of online bipartite matching problem: advertisers send ad requests to inﬂuencers (aka, key opinion leaders) on a social media platform. Each inﬂuencer has a maximum number of ad requests she can accommodate. We assign a ﬁxed number of inﬂuencers to an advertiser when she enters the platform. The advertiser then matches with each of the assigned inﬂuencers with a probability, which can be thought of as a set of negotiations between the advertiser and the set of assigned inﬂuencers. Unlike the standard online assignment problems, the outcome of any of these matches is not revealed throughout the session (ne-gotiations take time). Our goal is to maximize the expected number of matches between advertisers and inﬂuencers. We put forward a new deterministic algorithm with a competitive ratio of 1 / 2 and prove that no deterministic algorithm can achieve a better competitive ratio. We also show that the competitive ratio can be improved when randomness is allowed. We then study a setting where a match is successful with either probability 0 or a ﬁxed p . We present an optimal randomized algorithm that achieves a competitive ratio of 1 − 1 /e in this setting.
Fixing the Bethe approximation: How structural modifications in a graph improve belief propagation

Belief propagation is an iterative method for inference in probabilistic graphical models. Its well-known relationship to a classical concept from statistical physics, the Bethe free energy, puts it on a solid theoretical foundation. If belief propagation fails to approximate the marginals, then this is often due to a failure of the Bethe approximation. In this work, we show how modifications in a graphical model can be a great remedy for fixing the Bethe approximation. Specifically, we analyze how the removal of edges influences and improves belief propagation, and demonstrate that this positive effect is particularly distinct for dense graphs.
Improving sign-random-projection via count sketch

Computing the angular similarity between pairs of vectors is a core part of various machine learning algorithms. The seminal work of Charikar [Charikar, 2002] ( a.k.a. Sign-Random-Projection (SRP) or SimHash) provides an unbiased estimate for the same. However, SRP suffers from the following limitations: (i) large variance in the similarity estimation, (ii) and high running time while computing the sketch. There are improved variants that address these limitations. However, they are known to improve on only one aspect in their proposal, for e.g. [Yu et al., 2014] suggest a faster algorithm, [Ji et al., 2012, Kang and Wong, 2018] provide estimates with a smaller variance. In this work, we propose a sketching algorithm that addresses both aspects in one algorithm – a faster algorithm along with a smaller variance in the similarity estimation. Moreover, our algorithm is space-efficient as well. We present a rigorous theoretical analysis of our proposal and comple-ment it via experiments on synthetic and real-world datasets.
Deep Dirichlet process mixture models

In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning. The traditional Dirichlet process mixture model can infer the number of mixture components, but its ﬂexibility is restricted since the clustering is performed in the raw feature space. Our method alleviates this limitation by us-ing the ﬂow-based deep neural network to learn more expressive features. DDPM uniﬁes Dirichlet processes and the ﬂow-based model with Monte Carlo expectation-maximization, and uses Gibbs sampling to sample from the posterior. This combination allows our method to exploit the mutually beneﬁcial relation between clustering and feature learning. The effectiveness of DDPM is demonstrated by thorough experiments in various synthetic and real-world datasets.
Virulence Network of Interacting Influenza-Host Protein Domains

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of Influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the Influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between Influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid Influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering Influenza infection mechanisms mediated through protein domain interactions between viral and host proteins.
Pytometry: Flow and mass cytometry analytics in Python

Flow and mass cytometry data are commonly analyzed via manual gating strategies which requires prior knowledge, expertise and time. With increasingly complex experiments with many parameters and samples, traditional manual flow and mass cytometry data analysis becomes cumbersome if not inefficient. At the same time, computational tools developed for the analysis of single-cell RNA-sequencing data have made single cell genomics analysis highly efficient, yet they are mostly inaccessible for the analysis of flow and mass cytometry data due to different data formats, noise assumptions and scales. To bring the advantages of both fields together, we developed Pytometry as an extension to the popular scanpy framework for the analysis of flow and mass cytometry data. We showcase a standard analysis workflow on healthy human bone marrow data, illustrating the applicability of tools developed for the larger feature space of single cell genomics data. Pytometry combines joint analysis of multiple samples and advanced computational applications, ranging from automated pre-processing, cell type annotation and disease classification.
Sexual health knowledge acquisition processes among very young adolescent girls in rural Malawi: Implications for sexual and reproductive health programs

Early adolescence is a period characterized by enormous biological, cognitive, sexual, emotional, and social changes. Sexual curiosity and the desire to acquire sexual health (SH) information are part of these developments. Understanding the SH knowledge acquisition process is critical for designing interventions that can best support very young adolescents (VYAs). This study explored the SH knowledge acquisition processes among VYA girls aged 10 to 14 years who attended the DREAMs Girl Only Clubs (GOCs) and those who did not. The GOCs were a part of a larger comprehensive HIV prevention project called DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe) which provided an evidence-based core package of interventions to VYAs to prevent HIV. In-depth interviews were conducted with 43 VYA girls aged 10-14 years in two rural southern districts of Zomba and Machinga in Malawi. Twenty-three VYA girls were GOC participants and 20 VYAs were not. Guided by the Social Ecological Model, a thematic analysis approach was used to analyze the data with the assistance of Nvivo 12 software. The SH knowledge acquisition processes were the interaction of various factors at the microsystem (biological factors, attitudes), mesosystem (parents, older relatives, friends), and exosystem levels (schools and GOCs). Factors influencing this interaction included the beginning of menstruation, supportive parents/adults on SH issues, trust and confidentiality in SH information sources, and preference for well-informed sources of information. Compared to Non-GOC participants, GOC participants demonstrated an in-depth knowledge of SH issues, and positive sexual behaviors such as HIV testing. Limitations to SH knowledge acquisition were adult messages’ focus on sexual relationship avoidance and on girls who have started menstruation; perception of adults not being knowledgeable about SH and school teachers hiding some SH information. VYA SH interventions that provide VYA with accurate SH information such as the GOCs have the potential to address the limitations that other sources have in reaching VYAs. Integrating such interventions with empowering parents, other adults, and teachers with comprehensive SH information and with skills on how to deliver SH information to VYAs can enhance their SH knowledge acquisition and influence positive behavior change.
NO VMMN TO UNATTENDED ORIENTATION DEVIANT 1 Unattended visual stimuli do not produce prediction error responses despite being initially encoded

Prediction error is a basic component of predictive-coding theory of brain processing. According to the theory, each stage of brain processing of sensory information generates a model of the current sensory input; subsequent input is compared against the model and only if there is a mismatch, a prediction error, is further processing performed. Recently, Smout et al. [1] found that a signature of prediction error, the visual (v) mismatch negativity (MMN), for a fundamental property of visual input—its orientation—was absent without attention on the stimuli. This is remarkable because the weight of evidence for MMNs from audition and vision is that they occur without attention. To resolve this discrepancy, we conducted an experiment addressing two alternative explanations for Smout et al.’s finding: that it was from a lack of reproducibility or that participants’ visual systems did not encode the stimuli when attention was on something else. We conducted a similar experiment to Smout et al.’s. We showed 21 participants sequences of identically oriented Gabor patches, standards, and, unpredictably, otherwise identical, Gabor patches differing in orientation by ±15°, ±30°, and ±60°, deviants. To test whether participants encoded the orientation of the standards, we varied the numbers of standards preceding a deviant, allowing us to search for a decrease in activity with the number of repetitions of standards—repetition suppression. We diverted participants’ attention from the oriented stimuli with a central, letter-detection task. We reproduced Smout et al.’s finding of no vMMN without attention, strengthening their finding. We also found that our participants showed repetition suppression: they did encode the stimuli pre-attentively. We also found early processing of deviants. We discuss whether this earlier processing of deviants may be why no further processing, in the vMMN time window, occurs.
Influences of rare protein-coding genetic variants on the human plasma proteome in 50,829 UK Biobank participants

Combining human genomics with proteomics is becoming a powerful tool for drug discovery. Associations between genetic variants and protein levels can uncover disease mechanisms, clinical biomarkers, and candidate drug targets. To date, most population-level proteogenomic studies have focused on common alleles through genome-wide association studies (GWAS). Here, we studied the contribution of rare protein-coding variants to 1,472 plasma proteins abundances measured via the Olink Explore 1536 assay in 50,829 UK Biobank human exomes. Through a variant-level exome-wide association study (ExWAS), we identified 3,674 rare and significant protein quantitative trait loci (pQTLs), of which 76% were undetected in a prior GWAS performed on the same cohort, and we found that rare pQTLs are less likely to be random in their variant effect annotation. In gene-based collapsing analyses, we identified an additional 166 significant gene-protein pQTL signals that were undetected through single-variant analyses. Of the total 456 protein-truncating variant (PTV)-driven cis-pQTLs in the gene-based collapsing analysis, 99.3% were associated with decreased protein levels. We demonstrate how this resource can identify allelic series and propose biomarkers for several candidate therapeutic targets, including GRN, HSD17B13, NLRC4, and others. Finally, we introduce a new collapsing analysis framework that combines PTVs with missense cis-pQTLs that are associated with decreased protein abundance to bolster genetic discovery statistical power. Our results collectively highlight a considerable role for rare variation in plasma protein abundance and demonstrate the utility of plasma proteomics in gene discovery and unravelling mechanisms of action.
Functional annotation of the animal genomes: An integrated annotation resource for the horse

The genomic sequence of the horse has been available since 2009, providing critical resources for discovering important genomic variants regarding both animal health and population structures. However, to fully understand the functional implications of these variants, detailed annotation of the horse genome is required. Due to the limited availability of functional data for the equine genome, as well as the technical limitations of short-read RNA-seq, existing annotation of the equine genome contains limited information about important aspects of gene regulation, such as alternate isoforms and regulatory elements, which are either not transcribed or transcribed at a very low level. To solve above problems, the Functional Annotation of the Animal Genomes (FAANG) project proposed a systemic approach to tissue collection, phenotyping, and data generation, adopting the blueprint laid out by the Encyclopedia of DNA Elements (ENCODE) project. Here we detail the first comprehensive overview of gene expression and regulation in the horse, presenting 39,625 novel transcripts, 84,613 candidate cis-regulatory elements (CRE) and their target genes, 332,115 open chromatin regions genome wide across a diverse set of tissues. We showed substantial concordance between chromatin accessibility, chromatin states in different genic features and gene expression. This comprehensive and expanded set of genomics resources will provide the equine research community ample opportunities for studies of complex traits in the horse.
Metagenomic analysis unravels novel taxonomic differences in the uterine microbiome between healthy mares and mares with endometritis

The application of high throughput technologies has enabled unravelling of unique differences between healthy mares and mares with endometritis at transcriptomic and proteomic levels. However, differences in the uterine microbiome are yet to be investigated. The present study was aimed at evaluating the differences in uterine microbiome between healthy mares and mares with endometritis. Low-volume lavage (LVL) samples were collected from the uterus of 30 mares classified into healthy (n=15) and endometritis (n=15) based on their reproductive history, intrauterine fluid accumulation, gross appearance of LVL samples, endometrial cytology, and bacterial culture. The samples were subjected to metagenomic analysis using 16S rRNA sequencing. Notable differences in the uterine microbiome were observed between healthy mares and mares with endometritis at various taxonomic levels. In healthy mares, the most abundant phylum, class, order, and family were Firmicutes, Bacilli, Bacillales, and Paenibacillaceae, respectively. In contrast, the most abundant corresponding taxonomic levels in mares with endometritis were Proteobacteria, Gammaproteobacteria, Enterobacterales, and Enterobacteriaceae, respectively. At the genus level, Brevibacillus and Paenibacillus were more abundant in healthy mares, whereas Escherichia, Salmonella, and Klebsiella were more abundant in mares with endometritis. In healthy mares, Brevibacillus brevis was the most abundant species, followed by Brevibacillus choshinensis and Paenibacillus sp JDR-2. However, in mares with endometritis, Escherichia coli was the most abundant species, followed by Salmonella enterica and Klebsiella pneumoniae. These results indicate novel differences in the uterine microbiome between healthy mares and mares with endometritis. The findings can potentially help formulate new approaches to prevent or treat equine endometritis.
A bioinformatics approach to systematically analyze the molecular patterns of monkeypox virus-host cell interactions

Monkeypox has been spreading worldwide since May 2022, when the World Health Organization (WHO) declared the outbreak of a “Public health emergency of international concern”. The spread of monkeypox has posed a serious threat to the health of people all over the world but few studies have been carried out on it, and the molecular mechanism of monkeypox after infection remains unclear. We therefore implemented a transcriptome analysis to identify signaling pathways and biomarkers in monkeypox-infected cells to help understand monkeypox-host cell interactions. In this study, the dataset GSE36854 and GSE11234 were obtained from GEO. Among them, 84 significantly different genes were identified in the dataset GSE36854, followed by KEGG, GO analysis protein-protein interaction (PPI) construction and Hub gene extraction. We also analyzed the expression regulation patterns of hub genes and screened the drugs targeting the hub genes. The results showed that monkeypox-infected cells significantly activated the cellular immune response, and induced inflammatory response. IER3, IFIT2, IL11, ZC3H12A, EREG, IER2, NFKBIE, FST, IFIT1 and AREG were the top 10 hub genes, in which anti-viral gene IFIT1 and IFIT2 were significantly suppressed. AP-26113 and itraconazole promoting the expression of IFIT1 and IFIT2 may be used as new candidates for the treatment of monkeypox viral infection. Our results provide a new entry point for understanding the mode of interaction between monkeypox virus and its host.
Development and validation of a type 2 diabetes machine learning classification model for EHR-based diagnostics and clinical decision support

Background Undiagnosed type 2 diabetes continues to represent a significant challenge for all national healthcare systems. Although diagnostic criteria and laboratory screening procedures are well-established, clinical tests have limitations, and in many cases, diagnosis confirmation and more accurate interpretation of the test results are required. Machine learning methods, when applied to clinical outcome risk prediction, demonstrate great effectiveness, as they recognize specific patterns in data dynamics and thus can be used for the identification of at-risk cases where diabetes and complications can be delayed or even prevented. The aim of this study was to develop a type 2 diabetes machine learning model capable of efficient early identification of diabetes presence based on the results of common laboratory tests. Methods Real-world medical data from electronic medical records were subjected to a multistage processing, including feature selection, missing values imputation. The machine learning algorithms adopted in this study were XGBoost, multilayer perceptron, ridge classifier, ridge classifier with polynomial features, bootstrap aggregating, dynamic ensemble selection, stacked generalization. An external dataset was analyzed via the same workflow to validate the initial results. The study was designed in accordance with the TRIPOD statement. Results We have developed a machine learning classification model for type 2 diabetes that possesses several important advantages over conventional clinical methods (specifically, FINDRISC, ADA risk score). Performance metrics for the diabetes diagnostic model were 0.96 AUC, 92% specificity, and 89% sensitivity (mean values). Conclusions The study results potentially have major clinical implication and provide a contribution to the field of conventional diabetes risk assessment tools. Being specifically trained on real-world laboratory data and based on satisfactory external validation results, the present diagnostic type 2 diabetes model demonstrates high generalizability and can serve as a medical decision support and health monitoring tool.