bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discriminating early- and late-stage cancers using multiple kernel learning on gene sets
Authors: Rahimi Arezou , Gönen Mehmet 
Published: April 2018

Abstract

Motivation
Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets.

Results
In this study, we addressed the problem of separating early- and late-stage cancers from each other using their gene expression profiles. We proposed to use a multiple kernel learning (MKL) formulation that makes use of pathways/gene sets (i) to obtain satisfactory/improved predictive performance and (ii) to identify biological mechanisms that might have an effect in cancer progression. We extensively compared our proposed MKL on gene sets algorithm against two standard machine learning algorithms, namely, random forests and support vector machines, on 20 diseases from the Cancer Genome Atlas cohorts for two different sets of experiments. Our method obtained statistically significantly better or comparable predictive performance on most of the datasets using significantly fewer gene expression features. We also showed that our algorithm was able to extract meaningful and disease-specific information that gives clues about the progression mechanism.

Availability and implementation
Our implementations of support vector machine and multiple kernel learning algorithms in R are available at https://github.com/mehmetgonen/gsbc together with the scripts that replicate the reported experiments.
Improved pathway reconstruction from RNA interference screens by exploiting off-target effects
Authors: Srivatsa Sumana , Kuipers Jack , Schmich Fabian , Eicher Simone , Emmenlauer Mario , Dehio Christoph , Beerenwinkel Niko 
Published: April 2018

Abstract

Motivation
Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs.

Results
Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial gene knockdown data. Our model employs an adaptive simulated annealing search algorithm for simultaneous inference of network structure and error rates inherent to the data. Evaluation of pc-NEMs on simulated data with varying number of phenotypic effects and noise levels as well as real data demonstrates improved reconstruction compared to classical NEMs. Application to Bartonella henselae infection RNAi screening data yielded an eight node network largely in agreement with previous works, and revealed novel binary interactions of direct impact between established components.

Availability and implementation
The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git.

Supplementary information
Supplementary data are available at Bioinformatics online.
DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Authors: Li Yu , Xu Fan , Zhang Fa , Xu Pingyong , Zhang Mingshu , Fan Ming , Li Lihua , Gao Xin , Han Renmin 
Published: April 2018

Abstract

Motivation
Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.

Results
Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster.

Availability and implementation
The main program is available at https://github.com/lykaust15/DLBI

Supplementary information
Supplementary data are available at Bioinformatics online.
Classifying tumors by supervised network propagation
Authors: Zhang Wei , Ma Jianzhu , Ideker Trey 
Published: April 2018

Abstract

Motivation
Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.

Results
To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS2), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS2 is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes.

Availability and implementation
The NBS2 package is freely available at: https://github.com/wzhang1984/NBSS.

Supplementary information
Supplementary data are available at Bioinformatics online.
Learning with multiple pairwise kernels for drug bioactivity prediction
Authors: Cichonska Anna , Pahikkala Tapio , Szedmak Sandor , Julkunen Heli , Airola Antti , Heinonen Markus , Aittokallio Tero , Rousu Juho 
Published: April 2018

Abstract

Motivation
Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs.

Results
We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.

Availability and implementation
Code is available at https://github.com/aalto-ics-kepaco.

Supplementary information
Supplementary data are available at Bioinformatics online.
Viral quasispecies reconstruction via tensor factorization with successive read removal
Authors: Ahn Soyeon , Ke Ziqi , Vikalo Haris 
Published: April 2018

Abstract

Motivation
As RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains––a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform frequencies of the strains, and is further exacerbated when the genetic distances between the strains are small.

Results
This paper presents TenSQR, an algorithm that utilizes tensor factorization framework to analyze HTS data and reconstruct viral quasispecies characterized by highly uneven frequencies of its components. Fundamentally, TenSQR performs clustering with successive data removal to infer strains in a quasispecies in order from the most to the least abundant one; every time a strain is inferred, sequencing reads generated from that strain are removed from the dataset. The proposed successive strain reconstruction and data removal enables discovery of rare strains in a population and facilitates detection of deletions in such strains. Results on simulated datasets demonstrate that TenSQR can reconstruct full-length strains having widely different abundances, generally outperforming state-of-the-art methods at diversities 1–10% and detecting long deletions even in rare strains. A study on a real HIV-1 dataset demonstrates that TenSQR outperforms competing methods in experimental settings as well. Finally, we apply TenSQR to analyze a Zika virus sample and reconstruct the full-length strains it contains.

Availability and implementation
TenSQR is available at https://github.com/SoYeonA/TenSQR.

Supplementary information
Supplementary data are available at Bioinformatics online.
Modeling polypharmacy side effects with graph convolutional networks
Authors: Zitnik Marinka , Agrawal Monica , Leskovec Jure 
Published: April 2018

Abstract

Motivation
The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug–drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.

Results
Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein–protein interactions, drug–protein target interactions and the polypharmacy side effects, which are represented as drug–drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug–drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.

Availability and implementation
Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.
Effectiveness of 13-Valent Pneumococcal Conjugate Vaccine Against Hospitalization for Community-Acquired Pneumonia in Older US Adults: A Test-Negative Design
Authors: McLaughlin John M , Jiang Qin , Isturiz Raul E , Sings Heather L , Swerdlow David L , Gessner Bradford D , Carrico Ruth M , Peyrani Paula , Wiemken Timothy L , Mattingly William A , Ramirez Julio A , Jodar Luis 
Published: April 2018

Abstract

Background
Following universal recommendation for use of 13-valent pneumococcal conjugate vaccine (PCV13) in US adults aged ≥65 years in September 2014, we conducted the first real-world evaluation of PCV13 vaccine effectiveness (VE) against hospitalized vaccine-type community-acquired pneumonia (CAP) in this population.

Methods
Using a test-negative design, we identified cases and controls from a population-based surveillance study of adults in Louisville, Kentucky, who were hospitalized with CAP. We analyzed a subset of CAP patients enrolled 1 April 2015 through 30 April 2016 who were aged ≥65 years and consented to have their pneumococcal vaccination history confirmed by health insurance records. Cases were defined as hospitalized CAP patients with PCV13 serotypes identified via culture or serotype-specific urinary antigen detection assay. Remaining CAP patients served as test-negative controls.

Results
Of 2034 CAP hospitalizations, we identified PCV13 serotypes in 68 (3.3%) participants (ie, cases), of whom 6 of 68 (8.8%) had a positive blood culture. Cases were less likely to be immunocompromised (29.4% vs 46.4%, P = .02) and overweight or obese (41.2% vs 58.6%, P = .01) compared to controls, but were otherwise similar. Cases were less likely to have received PCV13 than controls (3/68 [4.4%] vs 285/1966 [14.5%]; unadjusted VE, 72.8% [95% confidence interval, 12.8%−91.5%]). No confounding was observed during adjustment for patient characteristics, including immunocompromised status, body mass index, and history of influenza and pneumococcal polysaccharide vaccination (adjusted VE range, 71.1%−73.3%).

Conclusions
Our study is the first to demonstrate real-world effectiveness of PCV13 against vaccine-type CAP in adults aged ≥65 years following introduction into a national immunization program.
High discordance in blood and genital tract HIV-1 drug resistance in Indian women failing first-line therapy
Authors: Saravanan Shanmugam , Gomathi Selvamurthi , Delong Allison , Kausalya Bagavathi , Sivamalar Sathasivam , Poongulali Selvamuthu , Brooks Katherine , Kumarasamy Nagalingeswaran , Balakrishnan Pachamuthu , Solomon Sunil S , Cu-Uvin Susan , Kantor Rami 
Published: April 2018

AbstractObjectivesExamine HIV-1 plasma viral load (PVL) and genital tract (GT) viral load (GVL) and drug resistance in India.MethodsAt the YRG Centre for AIDS Research and Education, Chennai, we tested: PVL in women on first-line ART for ≥6 months; GVL when PVL >2000 copies/mL; and plasma, genital and proviral reverse transcriptase drug resistance when GVL >2000 copies/mL. Wilcoxon rank-sum and Fisher's exact tests were used to identify failure and resistance associations. Pearson correlations were calculated to evaluate PVL–GVL associations. Inter-compartmental resistance discordance was evaluated using generalized estimating equations.ResultsOf 200 women, 37% had detectable (>400 copies/mL) PVL and 31% had PVL >1000 copies/mL. Of women with detectable PVL, 74% had PVL >2000 copies/mL, of which 74% had detectable GVL. Higher PVL was associated with higher GVL. Paired plasma and genital sequences were available for 21 women; mean age of 34 years, median ART duration of 33 months, median CD4 count of 217 cells/mm3, median PVL of 5.4 log10 copies/mL and median GVL of 4.6 log10 copies/mL. Drug resistance was detected in 81%–91% of samples and 67%–76% of samples had dual-class resistance. Complete three-compartment concordance was seen in only 10% of women. GT–proviral discordance was significantly larger than plasma–proviral discordance. GT or proviral mutations discordant from plasma led to clinically relevant resistance in 24% and 30%, respectively.ConclusionsWe identified high resistance and high inter-compartmental resistance discordance in Indian women, which might lead to unrecognized resistance transmission and re-emergence compromising treatment outcomes, particularly relevant to countries like India, where sexual HIV transmission is predominant.
Provider and patient satisfaction with the integration of ambulatory and hospital EHR systems
Authors: Meyerhoefer Chad D , Sherer Susan A , Deily Mary E , Chou Shin-Yi , Guo Xiaohui , Chen Jie , Sheinberg Michael , Levick Donald 
Published: April 2018

AbstractObjectiveThe installation of EHR systems can disrupt operations at clinical practice sites, but also lead to improvements in information availability. We examined how the installation of an ambulatory EHR at OB/GYN practices and its subsequent interface with an inpatient perinatal EHR affected providers’ satisfaction with the transmission of clinical information and patients’ ratings of their care experience.MethodsWe collected data on provider satisfaction through 4 survey rounds during the phased implementation of the EHR. Data on patient satisfaction were drawn from Press Ganey surveys issued by the healthcare network through a standard process. Using multivariable models, we determined how provider satisfaction with information transmission and patient satisfaction with their care experience changed as the EHR system allowed greater information flow between OB/GYN practices and the hospital.ResultsOutpatient OB/GYN providers became more satisfied with their access to information from the inpatient perinatal triage unit once system capabilities included automatic data flow from triage back to the OB/GYN offices. Yet physicians were generally less satisfied with how the EHR affected their work processes than other clinical and non-clinical staff. Patient satisfaction dropped after initial EHR installation, and we find no evidence of increased satisfaction linked to system integration.ConclusionsDissatisfaction of providers with an EHR system and difficulties incorporating EHR technology into patient care may negatively impact patient satisfaction. Care must be taken during EHR implementations to maintain good communication with patients while satisfying documentation requirements.
Primary Melanoma Histologic Subtype: Impact on Survival and Response to Therapy
Authors: Lattanzi Michael , Lee Yesung , Simpson Danny , Moran Una , Darvishian Farbod , Kim Randie H , Hernando Eva , Polsky David , Hanniford Doug , Shapiro Richard , Berman Russell , Pavlick Anna C , Wilson Melissa A , Kirchhoff Tomas , Weber Jeffrey S , Zhong Judy , Osman Iman 
Published: April 2018

Abstract

Background
Two primary histologic subtypes, superficial spreading melanoma (SSM) and nodular melanoma (NM), comprise the majority of all cutaneous melanomas. NM is associated with worse outcomes, which have been attributed to increased thickness at presentation, and it is widely expected that NM and SSM would exhibit similar behavior once metastasized. Herein, we tested the hypothesis that primary histologic subtype is an independent predictor of survival and may impact response to treatment in the metastatic setting.

Methods
We examined the most recent Surveillance, Epidemiology, and End Results (SEER) cohort (n = 118 508) and the New York University (NYU) cohort (n = 1621) with available protocol-driven follow-up. Outcomes specified by primary histology were studied in both the primary and metastatic settings with respect to BRAF-targeted therapy and immunotherapy. We characterized known driver mutations and examined a 140-gene panel in a subset of NM and SSM cases using next-generation sequencing. All statistical tests were two-sided.

Results
NM was an independent risk factor for death in both the SEER (hazard ratio [HR] = 1.55, 95% confidence interval [CI] = 1.41 to 1.70, P &lt; .001) and NYU (HR = 1.47, 95% CI = 1.05, 2.07, P = .03) cohorts, controlling for thickness, ulceration, stage, and other variables. In the metastatic setting, NM remained an independent risk factor for death upon treatment with BRAF-targeted therapy (HR = 3.33, 95% CI = 1.06 to 10.47, P = .04) but showed no statistically significant difference with immune checkpoint inhibition. NM was associated with a higher rate of NRAS mutation (P &lt; .001), and high-throughput sequencing revealed NM-specific genomic alterations in NOTCH4, ANK3, and ZNF560, which were independently validated.

Conclusions
Our data reveal distinct clinical and biological differences between NM and SSM that support revisiting the prognostic and predictive impact of primary histology subtype in the management of cutaneous melanoma.