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Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry‐based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label‐free kinase‐centric and substrate‐centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase–substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild‐type versus mutant, +/− drug), (iii) pre‐ and on‐treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient‐derived tumor xenografts with INKA‐guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate‐based single‐sample tool KARP, and underscore target potential of high‐ranking kinases, encouraging further exploration of INKA's functional and clinical value. Synopsis INKA (Integrative Inferred Kinase Activity) is an integrative data analysis approach ranking kinase activities in mass spectrometry‐based phosphoproteome data derived from single samples. INKA reveals oncogenes, differential kinase activity and drug targets. INKA combines kinase‐centric and substrate‐centric information and enables ranking kinase activities and visualizing kinase‐substrate networks in a single biological sample. INKA shows superior performance over its four components. INKA can be applied to both label‐free count and intensity data and was modified to accommodate labeling data. INKA can be used both for single‐sample and differential analysis and provides a versatile tool that can condense complex phosphoproteome data to actionable results. Graphical Abstract INKA (Integrative Inferred Kinase Activity) is an integrative data analysis approach ranking kinase activities in mass spectrometry‐based phosphoproteome data derived from single samples. INKA reveals oncogenes, differential kinase activity and drug targets.

Background Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches. Methods In this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases. Specifically, given a set of reference samples which were regarded as background, a novel index called single-sample Kullback–Leibler divergence (sKLD), was proposed to explore and quantify the disturbance on the background caused by a case sample. The pre-disease state is then signaled by the significant change of sKLD. Results The novel algorithm was developed and applied to both numerical simulation and real datasets, including lung squamous cell carcinoma, lung adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, colon adenocarcinoma, and acute lung injury. The successful identification of pre-disease states and the corresponding dynamical network biomarkers for all six datasets validated the effectiveness and accuracy of our method. Conclusions The proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis.

Detecting trends in population size fluctuations is a major focus in ecology, evolution, and conservation biology. Populations of colonial waterbirds have been monitored using demographic approaches to determine annual census size (Na). We propose the addition of genetic estimates of the effective number of breeders (Nb) as indirect measures of the risk of loss of genetic diversity to improve the evaluation of demographics and increase the accuracy of trend estimates in breeding colonies. Here, we investigated which methods of the estimation of Nb are more precise under conditions of moderate genetic diversity, limited sample sizes and few microsatellite loci, as often occurs with natural populations. We used the wood stork as a model species and we offered a workflow that researchers can follow for monitoring bird breeding colonies. Our approach started with simulations using five estimators of Nb and the theoretical results were validated with empirical data collected from breeding colonies settled in the Brazilian Pantanal wetland. In parallel, we estimated census size using a corrected method based on counting active nests. Both in simulations and in natural populations, the approximate Bayesian computation (ABC) and sibship assignment (SA) methods yielded more precise estimates than the linkage disequilibrium, heterozygosity excess, and molecular coancestry methods. In particular, the ABC method performed best with few loci and small sample sizes, while the other estimators required larger sample sizes and at least 13 loci to not underestimate Nb. Moreover, according to our Nb/Na estimates (values were often ≤0.1), the wood stork colonies evaluated could be facing the loss of genetic diversity. We demonstrate that the combination of genetic and census estimates is a useful approach for monitoring natural breeding bird populations. This methodology has been recommended for populations of rare species or with a known history of population decline to support conservation efforts. We propose the addition of genetic estimates of the effective number of breeders (Nb) as indirect measures of the risk of loss of genetic diversity to improve the evaluation of demographics and increase the accuracy of trend estimates in breeding colonies. According to our Nb/Na estimates (values were often ≤0.1), the wood stork colonies settled in the Brazilian Pantanal wetland could be facing the loss of genetic diversity. We demonstrate that the combination of genetic and census estimates is a useful approach for monitoring natural breeding bird populations, offering a workflow that researchers can follow for monitoring bird breeding colonies.

Background Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). Results We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z -score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z -score, PLAGE and ssGSEA can be unstable when less data are available ( N S  < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. In some practical situations, each identity has only a single sample available for training. Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models. Therefore, in recent years, researchers have attempted to unleash more potential of deep learning and improve the model recognition performance in the single sample situation. While several comprehensive surveys have been conducted on traditional single sample face recognition approaches, emerging deep learning based methods are rarely involved in these reviews. Accordingly, we focus on the deep learning-based methods in this paper, classifying them into virtual sample methods and generic learning methods. In the former category, virtual images or virtual features are generated to benefit the training of the deep model. In the latter one, additional multi-sample generic sets are used. There are three types of generic learning methods: combining traditional methods and deep features, improving the loss function, and improving network structure, all of which are covered in our analysis. Moreover, we review face datasets that have been commonly used for evaluating single sample face recognition models and go on to compare the results of different types of models. Additionally, we discuss problems with existing single sample face recognition methods, including identity information preservation in virtual sample methods, domain adaption in generic learning methods. Furthermore, we regard developing unsupervised methods is a promising future direction, and point out that the semantic gap as an important issue that needs to be further considered.

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

The tumor microenvironment (TME) consists of heterogeneous cell populations, including malignant cells and nonmalignant cells that support tumor proliferation, invasion, and metastasis through extensive cross talk. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy. Gene expression data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Immune cell infiltration was determined by single-sample Gene Set Enrichment Analysis (ssGSEA) depending on the integrated immune gene sets from published studies. Univariate analysis was used to determine the prognostic value of the infiltrated immune cells. Least absolute shrinkage and selection operator (LASSO) regression was performed to screen for the most survival-relevant immune cells. An immune-cell characteristic score (ICCS) model was constructed by using multivariate Cox regression analysis. The immune cell infiltration patterns across 32 cancer types were identified, and patients in the high immune cell infiltration cluster had worse overall survival (OS) but better progression-free interval (PFI) compared to the low immune cell infiltration cluster. However, immune cell infiltration showed inconsistent prognostic value depending on the cancer type. High immune cell infiltration (High CI) indicated a worse prognosis in brain lower grade glioma (LGG), glioblastoma multiforme (GBM), and uveal melanoma (UVM), and favorable prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), sarcoma (SARC), and skin cutaneous melanoma (SKCM). LUAD prognosis was significantly influenced by the infiltration of 13 immune cell types, with high infiltration of all but Type 2 T helper (Th2) cells correlating with a favorable prognosis. The ICCS model based on six most survival-relevant immune cell populations was generated that classified patients into low- and high-ICCS groups with good and poor prognoses, respectively. The multivariate and stratified analyses further revealed that the ICCS was an independent prognostic factor for LUAD. The infiltration of immune cells in 32 cancer types was quantified, and considerable heterogeneity was observed in the prognostic relevance of these cells in different cancer types. An ICCS model was constructed for LUAD with competent prognostic performance, which can further deepen our understanding of the TME of LUAD and can have implications for immunotherapy.

Background Analysis of somatic mutations provides insight into the mutational processes that have shaped the cancer genome, but such analysis currently requires large cohorts. We develop deconstructSigs, which allows the identification of mutational signatures within a single tumor sample. Results Application of deconstructSigs identifies samples with DNA repair deficiencies and reveals distinct and dynamic mutational processes molding the cancer genome in esophageal adenocarcinoma compared to squamous cell carcinomas. Conclusions deconstructSigs confers the ability to define mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors with implications for precision cancer medicine.

Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.

Background Network analysis is a fundamental tool for elucidating microbial interactions, which are crucial for understanding the mechanisms that shape ecosystem structure and function. However, aggregated co-abundance/co-occurrence network approaches that infer pairwise relationships among biological entities from large sample collections often overlook sample-specific interaction patterns. To address this limitation, we developed MicroSSNet, an R package designed for analyzing microbial networks, including both aggregated and single-sample networks. Results We designed MicroSSNet primarily to fill the current gap in bioinformatics tools for constructing single-sample networks (SSNs) from microbiome data, and we evaluated both the performance and limitations of ssPCC-based SSNs using simulated and real datasets. Through Monte Carlo simulations, we assessed the statistical behavior of ssPCC and highlighted scenarios in which ssPCC is less powerful. We then applied MicroSSNet to two distinct datasets: a human gut metagenomic dataset and a soil 16S rRNA gene dataset. In the human gut dataset, SSNs revealed unique edges not detected in the aggregated network. In the soil dataset, SSN features showed some predictive value for group classification. However, SSN-derived patterns should be interpreted cautiously, as they may not exclusively reflect true interaction changes. MicroSSNet additionally implements a full aggregated-network workflow, including bipartite networks and extensive topological property analysis. Conclusions Together, MicroSSNet offers a framework for constructing and analyzing both single-sample and aggregated microbial networks. In this work, we also highlight the potential and limitations of single-sample network approaches, supporting their application as exploratory tools in microbiome research across individual and population levels. The package is freely available on GitHub ( https://github.com/TangZecheng622/MicroSSNet ).