Traditional machine learning approaches to drug sensitivity prediction assume that training data and test data must be in the same feature space and have the same underlying distribution. However, in ...real-world applications, this assumption does not hold. For example, we sometimes have limited training data for the task of drug sensitivity prediction in multiple myeloma patients (target task), but we have sufficient auxiliary data for the task of drug sensitivity prediction in patients with another cancer type (related task), where the auxiliary data for the related task are in a different feature space or have a different distribution. In such cases, transfer learning, if applied correctly, would improve the performance of prediction algorithms on the test data of the target task via leveraging the auxiliary data from the related task. In this paper, we present two transfer learning approaches that combine the auxiliary data from the related task with the training data of the target task to improve the prediction performance on the test data of the target task. We evaluate the performance of our transfer learning approaches exploiting three auxiliary data sets and compare them against baseline approaches using the area under the receiver operating characteristic curve on the test data of the target task. Experimental results demonstrate the good performance of our approaches and their superiority over the baseline approaches when auxiliary data are incorporated.
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the ...task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of
Escherichia coli
and
Saccharomyces cerevisiae
, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation ...and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar ...irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase in greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of daily solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data, its superiority over related machine learning methods, and its ability to reproduce the daily variability. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099.
The ability of predicting future frames in video sequences, known as video prediction, is an appealing yet challenging task in computer vision. This task requires an in-depth representation of video ...sequences and a deep understanding of real-word causal rules. Existing approaches for tackling the video prediction problem can be classified into two categories: deterministic and stochastic methods. Deterministic methods lack the ability of generating possible future frames and often yield blurry predictions. On the other hand, although current stochastic approaches can predict possible future frames, their models lack the ability of action control in the sense that they cannot generate the desired future frames conditioned on a specific action. In this paper, we propose new generative adversarial networks (GANs) for stochastic video prediction. Our framework, called VPGAN, employs an adversarial inference model and a cycle-consistency loss function to empower the framework to obtain more accurate predictions. In addition, we incorporate a conformal mapping network structure into VPGAN to enable action control for generating desirable future frames. In this way, VPGAN is able to produce fake videos of an object moving along a specific direction. Experimental results show that the combination of VPGAN with a pre-trained image segmentation model outperforms existing stochastic video prediction methods.
Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive ...model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.
We consider a new tree mining problem that aims to discover restrictedly embedded subtree patterns from a set of rooted labeled unordered trees. We study the properties of a canonical form of ...unordered trees, and develop new Apriori-based techniques to generate all candidate subtrees level by level through two efficient rightmost expansion operations: 1) pairwise joining and 2) leg attachment. Next, we show that restrictedly embedded subtree detection can be achieved by calculating the restricted edit distance between a candidate subtree and a data tree. These techniques are then integrated into an efficient algorithm, named frequent restrictedly embedded subtree miner (FRESTM), to solve the tree mining problem at hand. The correctness of the FRESTM algorithm is proved and the time and space complexities of the algorithm are discussed. Experimental results on synthetic and real-world data demonstrate the effectiveness of the proposed approach.
RNA pseudoknots play important roles in many biological processes. Previous methods for comparative pseudoknot analysis mainly focus on simultaneous folding and alignment of RNA sequences. Little ...work has been done to align two known RNA secondary structures with pseudoknots taking into account both sequence and structure information of the two RNAs.
In this article we present a novel method for aligning two known RNA secondary structures with pseudoknots. We adopt the partition function methodology to calculate the posterior log-odds scores of the alignments between bases or base pairs of the two RNAs with a dynamic programming algorithm. The posterior log-odds scores are then used to calculate the expected accuracy of an alignment between the RNAs. The goal is to find an optimal alignment with the maximum expected accuracy. We present a heuristic to achieve this goal. The performance of our method is investigated and compared with existing tools for RNA structure alignment. An extension of the method to multiple alignment of pseudoknot structures is also discussed.
The method described here has been implemented in a tool named RKalign, which is freely accessible on the Internet. As more and more pseudoknots are revealed, collected and stored in public databases, we anticipate a tool like RKalign will play a significant role in data comparison, annotation, analysis, and retrieval in these databases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Introduction
Acute pain and chronic pain are significant burdens in the Department of Defense, compounded by the ongoing opioid crisis. Given the ubiquity of (leftover) opioid prescriptions ...following orthopedic surgery, it is essential to identify feasible and acceptable avenues of opioid risk mitigation efforts. The present quality improvement project builds on recent studies by evaluating factors related to opioid prescribing decisions in a sample of orthopedic surgery providers.
Materials and Methods
This quality improvement project received a Determination of Not Research and was conducted through a collaboration between the Department of Orthopaedic Surgery and the Department of Anesthesiology and Pain Management at Walter Reed National Military Medical Center. Providers in the Department of Orthopaedic Surgery completed an anonymous online survey assessing opioid prescribing education, factors influencing prescribing practices, opioid-safety practices, and perspectives on potential opioid safety initiatives.
Results
In total, 39 respondents completed surveys. There was variability in exposure to different types of opioid prescribing education, with some variation between attendings/physician assistants and residents. Patients’ acute postsurgical pain, using a standardized amount for most patients, and prescription histories were the three most influential factors. Concern of patients running out and fear of patient dissatisfaction were the least influential factors. Respondents commonly reported engagement in promoting nonpharmacological pain management, as well as coordinating with chronic pain providers when applicable, but did not commonly report educating patients on leftover opioid disposal. Respondents indicated that a barrier to opioid risk mitigation was the difficulty of accessing appropriate electronic health record data to inform decisions. Lastly, they reported openness to proposed opioid safety initiatives.
Conclusion
The results of this quality improvement project identified several target areas for future initiatives focused on improving opioid prescribing practices. This included a provider training program, improved patient education system, increased awareness and use of opioid tracking databases, and development of a standardized (but adaptable per patient characteristics and history) recommended dose for common orthopedic surgeries. Future projects will target tailored development, implementation, and evaluation of such efforts.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK, VSZLJ
The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount ...of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.
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