Approximate Bayesian computation (ABC) techniques permit inferences in complex demographic models, but are computationally inefficient. A Markov chain Monte Carlo (MCMC) approach has been proposed (Marjoram et al. 2003), but it suffers from computational problems and poor mixing. We propose several methodological developments to overcome the shortcomings of this MCMC approach and hence realize substantial computational advances over standard ABC. The principal idea is to relax the tolerance within MCMC to permit good mixing, but retain a good approximation to the posterior by a combination of subsampling the output and regression adjustment. We also propose to use a partial least-squares (PLS) transformation to choose informative statistics. The accuracy of our approach is examined in the case of the divergence of two populations with and without migration. In that case, our ABC–MCMC approach needs considerably lower computation time to reach the same accuracy than conventional ABC. We then apply our method to a more complex case with the estimation of divergence times and migration rates between three African populations.

https://www.genetics.org/content/genetics/182/4/1207.full.pdf

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

A review on machine learning principles for multi-view biological data integration.

Allohexaploid bread wheat (Triticum aestivum L.) provides approximately 20% of calories consumed by humans. Lack of genome sequence for the three homeologous and highly similar bread wheat genomes (A, B, and D) has impeded expression analysis of the grain transcriptome. We used previously unknown genome information to analyze the cell type-specific expression of homeologous genes in the developing wheat grain and identified distinct co-expression clusters reflecting the spatiotemporal progression during endosperm development. We observed no global but cell type- and stage-dependent genome dominance, organization of the wheat genome into transcriptionally active chromosomal regions, and asymmetric expression in gene families related to baking quality. Our findings give insight into the transcriptional dynamics and genome interplay among individual grain cell types in a polyploid cereal genome.

/pdf/genome-interplay-in-the-grain-transcriptome-of-hexaploid-24rdustedf.pdf

Genome interplay in the grain transcriptome of hexaploid bread wheat

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (Nmass), 42–54% for carbon (Cmass), 17–58% for fiber (acid-digestible fiber, ADF), 7–44% for lignin (acid-digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

/pdf/spectroscopic-determination-of-leaf-morphological-and-47icthwhup.pdf

Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species

Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the approach is equivalent to a correlation problem and the technique is then called partial least square correlation (PLSC) (also sometimes called PLS-SVD). In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called partial least square regression. In this case there is one set of latent variables (derived from the predictor table) and these latent variables are required to give the best possible prediction. In this paper we present and illustrate PLSC and PLSR and show how these descriptive multivariate analysis techniques can be extended to deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.

/pdf/partial-least-squares-methods-partial-least-squares-4grnrs9xu5.pdf

Partial least squares methods: partial least squares correlation and partial least square regression

Performance and cost analysis are the two main purposes of business process modeling, where the Petri net extended with time information is an effective tool for performance analysis, but not for cost analysis. This paper proposes a time Petri net extended with price information—Price Time Petri Net. Firstly, this paper associates a price with a time transition, and gives the semantics for price time Petri net in terms of priced timed transition systems. Then it defines a priced state class as an extension of state class, and discusses the soundness and completeness for this extension. An algorithm is given to prove that the minimum-cost reachability problem for bounded price time Petri net is decidable. Finally, this paper gives an example and draws a conclusion that incorporating price information in time Petri net and applying price time Petri net to business process management

/pdf/a-time-petri-net-extended-with-price-information-3os5kmtrky.pdf

Pan Li

Papers

A Time Petri Net Extended with Price Information