Aggregate Signal Research Articles

Supervariants identification for breast cancer.

In genome-wide association studies, signals associated with rare variants and interactions between genes are hard to detect even when the sample size is in tens of thousands. To overcome these problems, we examine the concept of supervariant. Like the classic concept of the gene, a supervariant is a combination of alleles in multiple loci, but the contributing loci can be anywhere in the genome. We hypothesize that supervariants are easy to detect and the aggregated signals are more stable in their associations with the disease than that from a single nucleoid polymorphism. Using the UK Biobank databases, we develop a ranking and aggregation method for identifying supervariants. Specifically, we examine 9,377 breast cancer cases with 46,861 controls matched by sex and age. In our simulations, the use of supervariants outperforms single-nucleotide polymorphism-based association method in detecting rare variants and signals with interactive structure. In real data analysis, we identify supervariants on Chromosomes 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 16, and 22 which cover previously reported loci that have associations with breast or other cancers, and several novel loci on Chromosomes 2, 5, 9, and 12. These findings demonstrate the validity of supervariants and its potential of discovering replicable and novel results for complex disease.

Abstract 4587: Utility of recombinant MUC4 domains as potential therapeutic agents

Abstract Pancreatic cancer (PC) is a highly aggressive malignancy with limited therapeutic options and is characterized by aberrant overexpression of mucins. Mucin 4 (MUC4) is a multi-domain transmembrane mucin which is differentially overexpressed in pancreatic cancer. MUC4 expression increases progressively during PC and high expression correlates with poor prognosis. MUC4 functionally contributes to pathobiology and aggressiveness of the disease by promoting cell proliferation, migration and inducing chemotherapy resistance. MUC4 is putatively cleaved at GDPH site protein resulting in two subunits, MUC4α and MUC4β. MUC4α comprises of highly glycosylated tandem repeat region, followed by NIDO and AMOP domains. The NIDO domain is known to play a role in cancer metastasis and tumor growth by interacting with other proteins. MUC4β comprises of vWD domain, three EGF-like domains, and a transmembrane domain. Following cleavage, MUC4β remains tethered to the cell membrane as a functional protein, and is thus an attractive target for therapeutic intervention. We have cloned and expressed various recombinant domains of MUC4 for the generation of monoclonal antibodies and development of therapeutic vaccine. We observed that recombinant MUC4β (rMUC4β) interacts with several cellular proteins including Ezrin, EGFR, HER2 and HER3. On the basis of these protein-protein interactions, we hypothesized that exogenous soluble MUC4β acts as a competitive inhibitor of endogenous MUC4 and disrupts its tumor-promoting functions. To determine the utility of rMUC4β as a therapeutic agent, we examined its effect on the proliferation, aggregation, migration, apoptosis and downstream signaling pathways in MUC4 expressing (CD18/HPAF) and non-expressing (MiaPaCa) human pancreatic cancer cell lines. We observed that rMUC4β induced alterations in cell morphology, including higher membrane blebbing, detachment from the plate, and disrupted homotypic cell-cell interactions. Further, exogenous MUC4β significantly inhibited (p value=0.0001) the proliferation of MUC4 expressing CD18/HPAF cells in a dose and time dependent manner. In contrast, no significant change was observed in growth rates and cells-cell interactions in MUC4 negative MiaPaCa cells. We also observed that MUC4β induced significantly higher apoptosis (> 15%) in MUC4 expressing cells as compared to MUC4 negative. Additionally, treatment of MUC4 expressing cells with rMUC4β inhibited their migration by up to 30 percent in a dose-dependent manner while no significant effect was observed in MUC4 non-expressing cells. Recombinant MUC4β treatment resulted in decreased phosphorylation of ERK-1 and AKT. Overall these studies suggest that rMUC4β can competitively inhibit some of the functions of endogenous MUC4 and disrupt the growth and migration promoting signaling pathways. Thus, MUC4β is an attractive therapeutic agent that can potentially inhibit tumorigenesis and metastasis in MUC4-positive PC. Citation Format: Mansi Gulati. Utility of recombinant MUC4 domains as potential therapeutic agents [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4587.

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

Residential Household Non-Intrusive Load Monitoring via Smart Event-based Optimization

Home energy management requires accurate information about the appliances’ consumption pattern. This information can help consumers save energy, control their usage by shifting their usage to off-peak hours and reduce their electricity costs. Non-intrusive load monitoring (NILM) in which the power consumption profile of appliances are extracted from the aggregated signal of a household, provides this information. For the NILM problem, machine learning approaches as the training-based solutions require large training datasets for an accurate disaggregation and the optimization-based approaches employs prior information about the characteristics of appliances. This paper proposes a novel event-based optimization algorithm. In its first stage, the prior information about appliances is extracted from the events of the consumption profiles of appliances by means of clustering. Then, a new event-based down-sampling method and transition filtering are designed for decreasing the computation time of optimization. At the last stage of the proposed algorithm, post-processing considering ON duration of appliances and varying states are proposed to increase the accuracy of the power profile reconstruction. The proposed approach was successfully tested for the low-frequency dataset of a house from the REDD. Numerical results show the advantages of the proposed algorithm, marked improvement over classification-based NILM considering small training dataset and its applicability in disaggregating the power consumption measured by the smart meter.

Mealtime: A circadian disruptor and determinant of energy balance?

Circadian rhythms play a critical role in the physiological processes involved in energy metabolism and energy balance (EB). A large array of metabolic processes, including the expression of many energy-regulating endocrine hormones, display temporal rhythms that are driven by both the circadian clock and food intake. Mealtime has been shown to be a compelling zeitgeber in peripheral tissue rhythms. Inconsistent signalling to the periphery, because of mismatched input from the central clock vs time of eating, results in circadian disruption in which central and/or peripheral rhythms are asynchronously time shifted or their amplitudes reduced. A growing body of evidence supports the negative health effects of circadian disruption, with strong evidence in murine models that mealtime-induced circadian disruption results in various metabolic consequences, including energy imbalance and weight gain. Increased weight gain has been reported to occur even without differences in energy intake, indicating an effect of circadian disruption on energy expenditure. However, the translation of these findings to humans is not well established because the ability to undertake rigorously controlled dietary studies that explore the chronic effects on energy regulation is challenging. Establishing the neuroendocrine changes in response to both acute and chronic variations in mealtime, along with observations in populations with routinely abnormal mealtimes, may provide greater insight into underlying mechanisms that influence long-term weight management under different meal patterns. Human studies should explore mechanisms through relevant biomarkers; for example, cortisol, leptin, ghrelin and other energy-regulating neuroendocrine factors. Mistiming between aggregate hormonal signals, or between hormones with their receptors, may cause reduced signalling intensity and hormonal resistance. Understanding how mealtimes may impact on the coordination of endocrine factors is essential for untangling the complex regulation of EB. Here a review is provided on current evidence of the impacts of mealtime on energy metabolism and the underlying neuroendocrine mechanisms, with a specific focus on human research.

Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), describes various processes aiming to identify the individual contribution of appliances, given the aggregate power signal. In this paper, a non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation is introduced. The proposed model, CoBiLSTM, harnesses the representational power of deep recurrent Long Short-Term Memory (LSTM) neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for: non-causality and adaptivity to contextual factors (e.g., seasonality). A Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, driven by a self-training adaptive mechanism. Furthermore, the proposed model is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increases. Experimental results indicate the proposed method’s superiority compared to the current state of the art.

Derivation of a Dynamic Market Risk Signal Using Kernel PCA and Machine Learning

Kernel principal component analysis (PCA) is an extension of the conventional PCA method that employs a kernel transformation whereby hidden patterns in possibly multidimensional data may be detected and extracted more explicitly. In this article, the author applies the method of kernel PCA to a currency prediction case study and derives an aggregate market signal. It is observed that this signal has desirable information-compression properties and may be used as a predictive risk indicator in the return prediction models. Used alongside common drivers of exchange rates, a kernel PCA signal enhances in-sample and out-of-sample risk-adjusted performance across a range of machine learning strategies. In particular, the author observes that a kernel PCA signal remains robust and predictive during volatile market conditions. The kernel PCA signal may be used as a machine learning feature to inform and support data-driven risk management strategies. TOPICS:Big data/machine learning, derivatives Key Findings • This article develops a risk signal based on the method of kernel PCA. • Kernel PCA conveys desirable information on market dynamics. • Currency machine learning strategies benefit from this kernel PCA feature.

Surface reaction strategy for Raman probing trace cadmium ion

Prevalent contamination of water by cadmium ion (Cd2+) brings environmental risk, which threatening to human health including renal dysfunction, reduced lung capacity, and some cancers. It calls for rapid, sensitive and selective protocol to analyze Cd2+ on field. In this work, specific Raman probe is rationally designed by gold nanoparticles (AuNPs) functionalized with tripeptide glutathione (GSH) and Rhodamine 6G (R6G) (denoted as R6G/GSH/AuNPs), which is explored for indirectly determining trace Cd2+ in river water. Based on chelating interaction between GSH and Cd2+ to form tetrahedral Cd(SG)4, which trends easy detachment from R6G/GSH/AuNPs. With increasing Cd2+ concentration, the Raman probe without protection of GSH happens due aggregation and Raman signal of R6G increases correspondingly. The R6G/GSH/AuNPs-based Raman detection of Cd2+ in river water shows rapidness, excellent sensitivity, selectivity and good repeatability. Limit of detection of 10 ppb with a widely linear range of 0.5–20 ppm could be achieved. It could be perspective that such Raman probe could be extended to be used for on-site monitoring trace Cd2+ in river water system.

Wideband Linearization of a Carrier Aggregation Transmitter Using Analog Signal Injection and 2-D Digital Predistortion

We introduce a transmitter (TX) architecture and a linearization technique for concurrent transmission of two intraband noncontiguous carrier aggregation signals in the 5 GHz frequency band. The technique is based on low-power and narrowband analog signal injection to compensate for out-of-band (OOB) third-order intermodulation distortions (IMD3), together with 2-D digital predistortion to address error vector magnitude (EVM) degradation caused by in-band intermodulation and cross-modulation distortions. The hybrid analog-digital design enables wideband linearization over the entire third-order distortion bandwidth (BW) using practical sampling rate requirements from the digital-to-analog converter (DAC) and analog-to-digital converter (ADC). The system’s performance was evaluated using a class AB power amplifier implemented in the 180 nm CMOS technology and two VHT 802.11ac Wi-Fi signals with a 20 MHz modulation BW. The linearization technique achieves 7–12 dB cancellation of OOB IMD3 products along with 6–9 dB of in-band EVM correction at 8 dBm average output power over a BW of up to 1.2 GHz, with only 60 MHz BW requirement from the DACs and ADCs.

Camphor and Borneol as the Male-Produced Sex Pheromone of the Shield Bug, Orsilochides leucoptera (Hemiptera: Scutelleridae).

The ability of stink bugs to release high amounts of strong-smelling and irritating defensive compounds is related to their metathoracic gland (MTG), which is an exocrine gland with defensive, sexual, alarm and aggregation signal functions. Orsilochides leucoptera (Scutelleridae) is a widespread species in the Neotropical region that feeds on plants of the families Malvaceae, Poaceae and Euphorbiaceae. A series of compounds (ketones, alcohols and esters) have been identified in the MTGs among the three species of Scutelleridae whose MTG secretions have been investigated thus far; however, no sex pheromone compounds have been described for any scutellerid species. The aim of this work was to study sex pheromone communication within this family of stink bugs, and identify the compounds present in the MTG of O. leucoptera. Analysis by gas chromatography/mass spectrometry (GC/MS) revealed two male specific compounds identified as (R)-camphor (1) and (R)-borneol (2), which were attractive to females in Y-tube olfactometer bioassays. Also, GC/MS analysis of secretions from MTG of males and females of O. leucoptera, identified eight additional compounds: 2-(E)-hexenal (3), (E)-4-oxo-2-hexenal (4), (E)-hex-2-enyl acetate (5), (R)-linalool (6), (R)-α-terpineol (7), dodecane (8), 1-tridecene (9) and n-tridecane (10). From these, (R)-α-terpineol was detected only in the gland of males, and is probably a biosynthetic intermediate of the pheromone components. Most of the MTG compounds identified in O. leucoptera have been identified in other heteropteran species. Camphor is often a toxic and repellent compound for insects. However, we report it, for the first time, as a sex pheromone component of an insect.

CD95 Structure, Aggregation and Cell Signaling.

CD95 is a pre-ligand-associated transmembrane (TM) receptor. The interaction with its ligand CD95L brings to a next level its aggregation and triggers different signaling pathways, leading to cell motility, differentiation or cell death. This diversity of biological responses associated with a unique receptor devoid of enzymatic property raises the question of whether different ligands exist, or whether the fine-tuned control of CD95 aggregation and conformation, its distribution within certain plasma membrane sub-domains or the pattern of post-translational modifications account for this such broad-range of cell signaling. Herein, we review how the different domains of CD95 and their post-translational modifications or the different forms of CD95L can participate in the receptor aggregation and induction of cell signaling. Understanding how CD95 response goes from cell death to cell proliferation, differentiation and motility is a prerequisite to reveal novel therapeutic options to treat chronic inflammatory disorders and cancers.

Identification of a phagosomal F-actin structure that evades dendritic cell immunity to Cryptococcus gattii

Abstract Hypervirulent Cryptococcus gattii is a major cause of life-threatening cryptococcosis in immunocompetent individuals and responsible for the ongoing outbreak in the Pacific Northwest. This deadly fungus is known to subvert dendritic cell (DC) maturation and concomitant T cell immunity via immune evasion mechanisms that are poorly understood. Here, we show that primary human DC can phagocytose endemic yeasts but trafficking to the late phagolysosome is blocked by retention of a filamentous actin (F-actin) cage surrounding the phagosomes. Structural studies by super resolution microscopy revealed a novel highly branched F-actin cage that physically interfered with lysosomal fusion. Mechanistically, we demonstrate that C. gattii F-actin cage promotes immune evasion by silencing the canonical RelA signaling of the NF-κB pathway required for DC costimulation and T cell activation. Disruption of the F-actin cage through targeted inhibition or by TNF-α signaling reprogrammed quiescent DC to immunocompetent antigen-presenting cells (APCs) and revealed the existence of a negative feedback loop between periphagosomal F-actin aggregation and pro-inflammatory NF-κB signaling. Collectively, our results have uncovered a unique mechanism of DC immune subversion by organisms such as hypervirulent C. gattii and revealed the potential clinical significance of the immunomodulatory function of phagosomal F-actin in host-pathogen interaction.

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

Localization of Fluorescent Targets in Deep Tissue With Expanded Beam Illumination for Studies of Cancer and the Brain.

Imaging fluorescence through millimeters or centimeters of tissue has important in vivo applications, such as guiding surgery and studying the brain. Often, the important information is the location of one of more optical reporters, rather than the specifics of the local geometry, motivating the need for a localization method that provides this information. We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result shows that localization can quickly provide tumor depth information, which could reduce damage to healthy tissue during fluorescence-guided surgery. Second, another near infrared fluorescent agent (ATTO647N) is injected into the brain of a rat, and localized through the intact skull and surface tissue. This result will enable studies of protein aggregation and neuron signaling.

Altered molecular signatures during kidney development after intrauterine growth restriction of different origins

This study was performed to identify transcriptional alterations in male intrauterine growth restricted (IUGR) rats during and at the end of nephrogenesis in order to generate hypotheses which molecular mechanisms contribute to adverse kidney programming. IUGR was induced by low protein (LP) diet throughout pregnancy, bilateral uterine vessel ligation (LIG), or intrauterine stress (IUS) by sham operation. Offspring of unimpaired dams served as controls. Significant acute kidney damage was ruled out by negative results for proteins indicative of ER-stress, autophagy, apoptosis, or infiltration with macrophages. Renal gene expression was examined by transcriptome microarrays, demonstrating 53 (LP, n = 12; LIG, n = 32; IUS, n = 9) and 134 (LP, n = 10; LIG, n = 41; IUS, n = 83) differentially expressed transcripts on postnatal days (PND) 1 and 7, respectively. Reduced Pilra (all IUGR groups, PND 7), Nupr1 (LP and LIG, PND 7), and Kap (LIG, PND 1) as well as increased Ccl20, S100a8/a9 (LIG, PND 1), Ifna4, and Ltb4r2 (IUS, PND 7) indicated that inflammation-related molecular dysregulation could be a “common” feature after IUGR of different origins. Network analyses of transcripts and predicted upstream regulators hinted at proinflammatory adaptions mainly in LIG (arachidonic acid-binding, neutrophil aggregation, toll-like-receptor, NF-kappa B, and TNF signaling) and dysregulation of AMPK and PPAR signaling in LP pups. The latter may increase susceptibility towards obesity-associated kidney damage. Western blots of the most prominent predicted upstream regulators confirmed significant dysregulation of RICTOR in LP (PND 7) and LIG pups (PND 1), suggesting that mTOR-related processes could further modulate kidney programming in these groups of IUGR pups.Key messagesInflammation-related transcripts are dysregulated in neonatal IUGR rat kidneys.Upstream analyses indicate renal metabolic dysregulation after low protein diet.RICTOR is dysregulated after low protein diet and uterine vessel ligation.

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation (MBM) is expected to be a prominent modulation scheme, which has access to the high data rate by using radio frequency (RF) mirrors and fewer transmit antennas. Associated with multiuser multiple input multiple output (MIMO), the MBM scheme achieves better performance than other conventional multiuser MIMO schemes. In this paper, the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed. This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots, which exploits the structured sparsity of these aggregate MBM signals. Under this kind of scenario, a multiuser detector with low complexity based on the compressive sensing (CS) theory to gain better detection performance is proposed. This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit (CoSaMP) and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals. By exploiting these sparsity, the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity. Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.

Performance Evaluation under Adverse Selection and Correlation Ambiguity

We develop a model wherein a risk-neutral but ambiguity-averse principal contracts with a risk-averse agent who has a risky project. Both the agent and the principal can observe the project output and a public signal. The correlation between the output and the public signal is private information to the agent but is an ambiguous random variable to the principal. Then, apart from moral hazard, the optimal contract takes into consideration both adverse selection and ambiguity aversion simultaneously. Due to the classic trade-off between rent and efficiency, the principal lowers contract power for the agent with a low correlation project (the l-type agent) and compensates her for luck. However, aversion to correlation ambiguity counteracts with this rent reducing effect by making the principal weight the l-type agent more. Consequently, although ambiguity lessens the principal's value, it could improve social welfare by increasing efficiency of the l-type agent. We further extend the model by incorporating an aggregate signal whose variance depends on the ambiguous distribution of all the projects and allowing the agents to be ambiguity-averse. In this case, the principal has to respect the agent's model choice and compensates her for ambiguity premium, which again decreases contract power. With ambiguity-sharing, the pair of optimal separating contracts is metamorphosed compared to those in the baseline model.

Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration

Population growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for developing techniques for efficient management and distribution of the available energy. Non-Intrusive Load Monitoring (NILM) techniques have provided valuable information about energy consumption for power generation companies as well as consumers. Such information is important for making decisions related to sustainable use of energy resources. This study proposes an automated system based on Artificial Neural Network for performing some of the NILM tasks. A stacked neural network was developed to extract features of power signals of appliances to identify those in operation during a given period. This information is then used to disaggregate individual appliance loads through the total aggregate signal, and consumption is calculated through numerical integration. The system was tested using real data from two databases about appliances with On/Off, multi-level, and variable consumption patterns collected in low frequency. The performance metrics, resulting from identification and disaggregation tasks, demonstrate the efficiency of the proposed system.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signalling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals).

Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology.

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with statistical learning methods that do not use biological knowledge. In this review, we survey current knowledge-guided statistical learning methods, including both supervised learning and unsupervised learning, and their applications to precision oncology, and we discuss future research directions.