Multi-omics Topic Modeling for Breast Cancer Classification

The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of 'omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or ``topics'' that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development, and are associated to the survival probability.

Digital Form Finding Using Voronoi Pattern

Starting from funicular models, chain models and hanging membranes, the role of 3D physical models in optimized shape research is the basis of form-finding strategies. Advances in structural optimized shape design derive from the wide spread of special digital form-finding tools. The goal of this paper is to test and evaluate interdisciplinary approaches based on computational tools useful in the form finding of efficient structural systems. This work is aimed at designing an inverse hanging shape subdivided into polygonal voussoirs (Voronoi patterns) by relaxing a planar discrete and elastic system, loaded at each point and anchored along its boundary. The workflow involves shaping, discretization (from pre-shaped paneling to digital stereotomy) and structural analysis carried out using two modeling approaches, finite element and rigid block modeling, using an in-house software tool, LiABlock_3D (MATLAB®), to check the stress state and to evaluate the equilibrium stability of the final shell.

Laterite Nickel Mine Sequence Modeling Based on Total Reserve at Block 5A, “Bonus” Pit by Surpac 6.3.2 at PT Bintang Delapan Mineral in Bahodopi District, Morowali Regency, Central Sulawesi

PT. Bintang Delapan Mineral is an active nickel mine company that produces 300,000 tons of nickel per month by open pit method. During the mine operation, the company needs to calculate the reserves periodically due to decreasing nickel reserves in the long time period. Mineral reserves estimation is a process to determine and define the grade and boundary of a mineral deposit. Reserves estimation can be done manually using several methods. One method is called block modeling which presents the estimation process in block model type by applying a mining software, called Surpac 6.3.2. It was able to present the progress of mine site activity (pushback). The result of nickel reserves estimation by Surpac 6.3.2 block modeling is 48730 m3 or equal to 73096 tons. The nickel reserves have an average grade of 1.64%. By this reserve estimation, mine sequences could be created into 7 sequences in considering bench height. Bench model specifications are bench high 4 m, berm 2 m, and 60º slope.

A clarified typology of core-periphery structure in networks

Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the k-cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: The latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.

Applying geoinformation technologies of block modelling to improve the methods of assessing quality indicators of minerals

Introduction. The article presents the results of developing the procedure of evaluating quality indicators of minerals based on the technology of block modeling with the use of modern mining and geological information systems (MGIS). The flowchart of mineral quality indicators modeling has been proposed, and the outcome of its application has been presented by the example of Serovsky complex ore deposit and Odegeldei coalfield, the Tyva Republic. The presented procedure of block modeling makes it possible to zone the industrial types and sorts of ore in the open pit with a high degree of accuracy, which helps to solve the problems of industrial design, planning and management in the conditions of economic uncertainty, deteriorating mining and geological and mining-process conditions of field development. Research aim is to develop a universal algorithm of block modeling to improve the procedures of evaluating quality indicators. Methodology. The scheme has been developed which establishes the connection between the stages of modeling and process solutions for field opencasting. Based on geologic feature block modeling, the procedure of mineral quality indicators geometrization has been widened and improved. Results. The flexibility of mineral quality block modeling procedure has been proved. It can be applied at any deposit to solve certain mining engineering objectives. Based on the presented scientific results, mineral quality control procedures may be developed, and mining planning may be improved in the mode of quality control. Summary. The proposed methodology is flexible and makes it possible to evaluate quality indicators of minerals to select rational techniques of mineral quality control.

Generic Constraint-based Block Modeling using Constraint Programming

Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings, for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally, we show an application of the CP framework for model selection using the Minimum Description Length principle.