Extended endocrine therapy in breast cancer: A basket of length-constraint feature selection metaheuristics to balance Type I against Type II errors.

Extended endocrine therapy beyond 5 years is of major concern to ER + breast cancer survivors. However, it might be unsuitable to apply routinely used genomic tests designed for early recurrence risks to distant recurrence within 10 years in extended treatment context. These tests initially aim at high sensitivities with Type I errors much higher than Type II. Having lower positive predictive values (PPVs), these tests can bring many false positives who might not need further treatment options to avoid adversely affecting quality of life. Alternatively, we proposed a top-down approach to the raised issues. We built 149 targeted genes from four genomic tests upon 381 ER-positive node-negative patients with either metastasis free beyond 10 years (n = 202) or metastasis within 10 years (n = 179). By a basket of SVM-wrapped length-constraint feature selection (LCFS), we discovered four genomic SVMs that traded off Type I against Type II errors. Two independent cohorts were used to validate disease outcome predictions. A 36-gene SVM balanced sensitivities with PPVs at good levels: 74% vs 76% on 10-fold cross validation (n = 347) and 75% vs 71% on a test set (n = 34). Neither Oncotype DX RS (cutoff = 18, 31, 60.97) nor PAM50 ROR-S (cutoff = 29, 53, 61.18) could. Independent cohorts showed the 36-gene SVM predicted disease free survival (n = 136, HR = 2.59; 95% CI, 1.4-4.8) and disease specific survival (n = 127, HR = 4.06; 95% CI, 1.63-10.11) better than RS (DFS, HR = 2.15; DSS, HR = 3.86) and ROR-S (DFS, HR = 2.29; DSS, HR = 2.76). The case study demonstrated how we identified a genomic test to balance Type I against Type II errors for risk stratification. The top-down approach centered around the LCFS-metaheuristics basket is a generic methodology for clinical decision-making and quality of life using targeted profiling data where the number of dimensions (p) is smaller than the number of samples (n).

iRDA: a new filter towards predictive, stable, and enriched candidate genes.

BACKGROUND: Gene expression profiling using high-throughput screening (HTS) technologies allows clinical researchers to find prognosis gene signatures that could better discriminate between different phenotypes and serve as potential biological markers in disease diagnoses. In recent years, many feature selection methods have been devised for finding such discriminative genes, and more recently information theoretic filters have also been introduced for capturing feature-to-class relevance and feature-to-feature correlations in microarray-based classification. METHODS: In this paper, we present and fully formulate a new multivariate filter, iRDA, for the discovery of HTS gene-expression candidate genes. The filter constitutes a four-step framework and includes feature relevance, feature redundancy, and feature interdependence in the context of feature-pairs. The method is based upon approximate Markov blankets, information theory, several heuristic search strategies with forward, backward and insertion phases, and the method is aiming at higher order gene interactions. RESULTS: To show the strengths of iRDA, three performance measures, two evaluation schemes, two stability index sets, and the gene set enrichment analysis (GSEA) are all employed in our experimental studies. Its effectiveness has been validated by using seven well-known cancer gene-expression benchmarks and four other disease experiments, including a comparison to three popular information theoretic filters. In terms of classification performance, candidate genes selected by iRDA perform better than the sets discovered by the other three filters. Two stability measures indicate that iRDA is the most robust with the least variance. GSEA shows that iRDA produces more statistically enriched gene sets on five out of the six benchmark datasets. CONCLUSIONS: Through the classification performance, the stability performance, and the enrichment analysis, iRDA is a promising filter to find predictive, stable, and enriched gene-expression candidate genes.

Accessibility of microRNA binding sites in metastable RNA secondary structures in the presence of SNPs.

MOTIVATION: We study microRNA (miRNA) bindings to metastable RNA secondary structures close to minimum free energy conformations in the context of single nucleotide polymorphisms (SNPs) and messenger RNA (mRNA) concentration levels, i.e. whether features of miRNA bindings to metastable conformations could provide additional information supporting the differences in expression levels of the two sequences defined by a SNP. In our study, the instances [mRNA/3'UTR; SNP; miRNA] were selected based on strong expression level analyses, SNP locations within binding regions and the computationally feasible identification of metastable conformations. RESULTS: We identified 14 basic cases [mRNA; SNP; miRNA] of 3' UTR-lengths ranging from 124 up to 1078 nt reported in recent literature, and we analyzed the number, structure and miRNA binding to metastable conformations within an energy offset above mfe conformations. For each of the 14 instances, the miRNA binding characteristics are determined by the corresponding STarMir output. Among the different parameters we introduced and analyzed, we found that three of them, related to the average depth and average opening energy of metastable conformations, may provide supporting information for a stronger separation between miRNA bindings to the two alleles defined by a given SNP. AVAILABILITY AND IMPLEMENTATION: At http://kks.inf.kcl.ac.uk/MSbind.html the MSbind tool is available for calculating features of metastable conformations determined by putative miRNA binding sites.

Mask use, risk-mitigation behaviours and pandemic fatigue during the COVID-19 pandemic in five cities in Australia, the UK and USA: A cross-sectional survey.

OBJECTIVES: To determine patterns of mask wearing and other infection prevention behaviours, over two time periods of the COVID-19 pandemic, in cities where mask wearing was not a cultural norm. METHODS: A cross-sectional survey of masks and other preventive behaviours in adults aged ≥18 years was conducted in five cities: Sydney and Melbourne, Australia; London, UK; and Phoenix and New York, USA. Data were analysed according to the epidemiology of COVID-19, mask mandates and a range of predictors of mask wearing. RESULTS: The most common measures used were avoiding public areas (80.4%), hand hygiene (76.4%), wearing masks (71.8%) and distancing (67.6%). Over 40% of people avoided medical facilities. These measures decreased from March-July 2020. Pandemic fatigue was associated with younger age, low perceived severity of COVID-19 and declining COVID-19 prevalence. Predictors of mask wearing were location (US, UK), mandates, age <50 years, education, having symptoms and knowing someone with COVID-19. Negative experiences with mask wearing and low perceived severity of COVID-19 reduced mask wearing. Most respondents (98%) believed that hand washing and distancing were necessary, and 80% reported no change or stricter adherence to these measures when wearing masks. CONCLUSION: Pandemic mitigation measures were widely reported across all cities, but decreased between March and July 2020. Pandemic fatigue was more common in younger people. Cities with mandates had higher rates of mask wearing. Promotion of mask use for older people may be useful. Masks did not result in a reduction of other hygiene measures.

A hybrid approach to protein folding problem integrating constraint programming with local search.

BACKGROUND: The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search. RESULTS: Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach. CONCLUSION: Constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.

Corrigendum: Long Non-coding RNA Structure and Function: Is There a Link?

[This corrects the article DOI: 10.3389/fphys.2018.01201.].

Long Non-coding RNA Structure and Function: Is There a Link?

RNA has emerged as the prime target for diagnostics, therapeutics and the development of personalized medicine. In particular, the non-coding RNAs (ncRNAs) that do not encode proteins, display remarkable biochemical versatility. They can fold into complex structures and interact with proteins, DNA and other RNAs, modulating the activity, DNA targets or partners of multiprotein complexes. Thus, ncRNAs confer regulatory plasticity and represent a new layer of epigenetic control that is dysregulated in disease. Intriguingly, for long non-coding RNAs (lncRNAs, >200 nucleotides length) structural conservation rather than nucleotide sequence conservation seems to be crucial for maintaining their function. LncRNAs tend to acquire complex secondary and tertiary structures and their functions only impose very subtle sequence constraints. In the present review we will discuss the biochemical assays that can be employed to determine the lncRNA structural configurations. The implications and challenges of linking function and lncRNA structure to design novel RNA therapeutic approaches will also be analyzed.

Converging and emerging threats to health security.

Advances in biological sciences have outpaced regulatory and legal frameworks for biosecurity. Simultaneously, there has been a convergence of scientific disciplines such as synthetic biology, data science, advanced computing and many other technologies, which all have applications in health. For example, advances in cybercrime methods have created ransomware attacks on hospitals, which can cripple health systems and threaten human life. New kinds of biological weapons which fall outside of traditional Cold War era thinking can be created synthetically using genetic code. These convergent trajectories are dramatically expanding the repertoire of methods which can be used for benefit or harm. We describe a new risk landscape for which there are few precedents, and where regulation and mitigation are a challenge. Rapidly evolving patterns of technology convergence and proliferation of dual-use risks expose inadequate societal preparedness. We outline examples in the areas of biological weapons, antimicrobial resistance, laboratory security and cybersecurity in health care. New challenges in health security such as precision harm in medicine can no longer be addressed within the isolated vertical silo of health, but require cross-disciplinary solutions from other fields. Nor can they cannot be managed effectively by individual countries. We outline the case for new cross-disciplinary approaches in risk analysis to an altered risk landscape.

CRISPR/Cas9 editing reveals novel mechanisms of clustered microRNA regulation and function.

MicroRNAs (miRNAs) are important regulators of diverse physiological and pathophysiological processes. MiRNA families and clusters are two key features in miRNA biology. Here we explore the use of CRISPR/Cas9 as a powerful tool to delineate the function and regulation of miRNA families and clusters. We focused on four miRNA clusters composed of miRNA members of the same family, homo-clusters or different families, hetero-clusters. Our results highlight different regulatory mechanisms in miRNA cluster expression. In the case of the miR-497~195 cluster, editing of miR-195 led to a significant decrease in the expression of the other miRNA in the cluster, miR-497a. Although no gene editing was detected in the miR-497a genomic locus, computational simulation revealed alteration in the three dimensional structure of the pri-miR-497~195 that may affect its processing. In cluster miR-143~145 our results imply a feed-forward regulation, although structural changes cannot be ruled out. Furthermore, in the miR-17~92 and miR-106~25 clusters no interdependency in miRNA expression was observed. Our findings suggest that CRISPR/Cas9 is a powerful gene editing tool that can uncover novel mechanisms of clustered miRNA regulation and function.

A new heuristic method for approximating the number of local minima in partial RNA energy landscapes.

The analysis of energy landscapes plays an important role in mathematical modelling, simulation and optimisation. Among the main features of interest are the number and distribution of local minima within the energy landscape. Granier and Kallel proposed in 2002 a new sampling procedure for estimating the number of local minima. In the present paper, we focus on improved heuristic implementations of the general framework devised by Granier and Kallel with regard to run-time behaviour and accuracy of predictions. The new heuristic method is demonstrated for the case of partial energy landscapes induced by RNA secondary structures. While the computation of minimum free energy RNA secondary structures has been studied for a long time, the analysis of folding landscapes has gained momentum over the past years in the context of co-transcriptional folding and deeper insights into cell processes. The new approach has been applied to ten RNA instances of length between 99 nt and 504 nt and their respective partial energy landscapes defined by secondary structures within an energy offset ΔE above the minimum free energy conformation. The number of local minima within the partial energy landscapes ranges from 1440 to 3441. Our heuristic method produces for the best approximations on average a deviation below 3.0% from the true number of local minima.

Random versus Deterministic Descent in RNA Energy Landscape Analysis.

Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~10(6). While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.

Computer-assisted diagnosis of focal liver lesions on CT images evaluation of the Perceptron algorithm.

RATIONALE AND OBJECTIVE: The purpose of the study was to investigate a modified version of a so-called Perceptron algorithm in detecting focal liver lesions on CT scans. MATERIALS AND METHODS: The modified Perceptron algorithm is based on simulated annealing with a logarithmic cooling schedule and was implemented on a standard workstation. The algorithm was trained with 400 normal and 400 pathologic CT scans of the liver. An additional 100 normal and 100 pathologic scans were then used to test the detection of pathology by the algorithm. The total of 1000 scans used in the study were selected from the portal venous phase of upper abdominal CT examinations performed in patients with normal findings or hypovascularized liver lesions. The pathologic scans contained 1 to 4 focal liver lesions. For the preliminary version of the algorithm used in this study, it was necessary to define regions of interest that were converted to a matrix of 119 x 119. RESULTS: Training of the algorithm with 400 examples each of normal and abnormal findings took about 75 hours. Subsequently, the testing took several seconds for processing each scan. The diagnostic accuracy in discriminating scans with and without focal liver lesions achieved for the 200 test scans was approximately 99%. The error rate for pathologic and normal scans was comparable to results reported in the literature, which, however, were obtained for much smaller test sets. CONCLUSION: The modified Perceptron algorithm has an accuracy of close to 99% in detecting pathology on CT scans of the liver showing either normal findings or hypovascularized focal liver lesions.

A new vision of evaluating gene expression signatures.

Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.

A firefly-inspired method for protein structure prediction in lattice models.

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa-Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function evaluations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models.

Derivative scores from site accessibility and ranking of miRNA target predictions.

In the present study, we define derivative scoring functions from PITA and STarMir predictions. The scoring functions are evaluated for up to five selected miRNAs with a relatively large number of validated targets reported by TarBase and miRecords. The average ranking of validated targets returned by PITA and STarMir is compared to the average ranking produced by the new derivatives scores. We obtain an average improvement of 13.6% (STD∼5.7%) relative to the average ranking of validated targets produced by PITA and STarMir.