Bioinformatics software plays a very important role in making critical decisions within many areas including medicine and health care. However, most of the research is directed towards developing tools, and little time and effort is spent on testing the software to assure its quality. In testing, a test oracle is used to determine whether a test is passed or failed during testing, and unfortunately, for much of bioinformatics software, the exact expected outcomes are not well defined. Thus, the main challenge associated with conducting systematic testing on bioinformatics software is the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem. MT uses metamorphic relations (MRs) to determine whether a test has passed or failed and specifies how the output should change according to a specific change made to the input. In this work, we use MT to test LingPipe, a tool for processing text using computational linguistics, often used in bioinformatics for bio-entity recognition from biomedical literature. First, we identify a set of MRs for testing any bio-entity recognition program. Then we develop a set of test cases that can be used to test LingPipe's bio-entity recognition functionality using these MRs. To evaluate the effectiveness of this testing process, we automatically generate a set of faulty versions of LingPipe. According to our analysis of the experimental results, we observe that our MRs can detect the majority of these faulty versions, which shows the utility of this testing technique for quality assurance of bioinformatics software.
@cite_8 employed MT on two open-source bioinformatics programs. The first program GNLab http: en.bio-soft.net other gnlab.html is a tool for large-scale analysis and simulation of gene regulatory networks. The second program SeqMap http: www-personal.umich.edu jianghui seqmap deals with mapping a short sequence that reads with a reference genome.
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Motivation: In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert. Results: Our method integrates literature information, DNA sequences and expression arrays. A set of relevant transcription factors is defined on the basis of literature. Sequence data are used to identify potential target genes and the results are used to define a prior distribution on the topology of the regulatory network. A Bayesian hidden component model for the expression array data allows us to identify which of the potential binding sites are actually used by the regulatory proteins in the studied cell conditions, the strength of their control, and their activation profile in a series of experiments. We apply our methodology to 35 expression studies in E.Coli with convincing results. Availability: www.genetics.ucla.edu labs sabatti software.html Supplementary information: The supplementary material are available at Bioinformatics online. Contact: csabatti@mednet.ucla.edu The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease apart the new architectures and their accompanying techniques in two ways. First, we identify several key modeling and training techniques, and apply them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT'14 English to French and English to German tasks. Second, we analyze the properties of each fundamental seq2seq architecture and devise new hybrid architectures intended to combine their strengths. Our hybrid models obtain further improvements, outperforming the RNMT+ model on both benchmark datasets. Motivation: Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein–protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning. Results: We applied HyperPrior to test two arrayCGH datasets and two gene expression datasets for both cancer classification and biomarker identification. On all the datasets, HyperPrior achieved competitive classification performance, compared with SVMs and the other baselines utilizing the same prior knowledge. HyperPrior also identified several discriminative regions on chromosomes and discriminative subnetworks in the PPI, both of which contain cancer-related genomic elements. Our results suggest that HyperPrior is promising in utilizing biological prior knowledge to achieve better classification performance and more biologically interpretable findings in gene expression and arrayCGH data. Availability: http: compbio.cs.umn.edu HyperPrior Contact: kuang@cs.umn.edu Supplementary information:Supplementary data are available at bioinformatics online. Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its robustness in testing environments has not been analyzed in detail yet. In this paper, the learning mechanism in the testing environment is introduced and it is confirmed that GNP-RL can show the robustness using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to change programs using alternative actions automatically. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations.