HyMM: Hybrid method for disease-gene prediction by integrating multiscale module structures

Motivation: Identifying disease-related genes is important for the study of human complex diseases. Module structures or community structures are ubiquitous in biological networks. Although the modular nature of human diseases can provide useful insights, the mining of information hidden in multiscale module structures has received less attention in disease-gene prediction. Results: We propose a hybrid method, HyMM, to predict disease-related genes more effectively by integrating the information from multiscale module structures. HyMM consists of three key steps: extraction of multiscale modules, gene rankings based on multiscale modules and integration of multiple gene rankings. The statistical analysis of multiscale modules extracted by three multiscale-module-decomposition algorithms (MO, AS and HC) shows that the functional consistency of the modules gradually improves as the resolution increases. This suggests the existence of different levels of functional relationships in the multiscale modules, which may help reveal disease-gene associations. We display the effectiveness of multiscale module information in the disease-gene prediction and confirm the excellent performance of HyMM by 5-fold cross-validation and independent test. Specifically, HyMM with MO can more effectively enhance the ability of disease-gene prediction; HyMM (MO, RWR) and HyMM (MO, RWRH) are especially preferred due to their excellent comprehensive performance, and HyMM (AS, RWRH) is also good choice due to its local performance. We anticipate that this work could provide useful insights for disease-module analysis and disease-gene prediction based on multi-scale module structures. Availability: https://github.com/xiangju0208/HyMM

PENGEMBANGAN MODUL BERBASIS PENEMUAN TERBIMBING PADA MATA KULIAH GEOMETRI TRANSFORMASI

Based on the results of observations, the learning process still uses conventional methods and the textbooks used in the lecture process have not been able to lead to finding a concept. The aim of the study was to design a guided discovery-based module in the transformation geometry course at STKIP PGRI West Sumatra. This type of research is development research using the Plomp model which consists of 3 phases, namely preliminary research, prototyping phase, and assessment phase (Plomp, 2013: 19). The preliminary research stage consists of interviews with students and teachers, syllabus analysis, and textbook analysis. The prototyping phase is designing systematics and module structures, developing prototypes, and expert reviews. The results obtained at the preliminary research stage were based on interviews with lecturers and students who did not understand the basic concepts of geometry and were less able to prove the proposition. In the analysis of the syllabus and literature that was carried out was that the material taught was in accordance with the syllabus. The results of the module validity from the experts show that the guided discovery-based module is quite valid in the category.

Short Duplex Module Coupled to G-Quadruplexes Increases Fluorescence of Synthetic GFP Chromophore Analogues

Aptasensors became popular instruments in bioanalytical chemistry and molecular biology. To increase specificity, perspective signaling elements in aptasensors can be separated into a G-quadruplex (G4) part and a free fluorescent dye that lights up upon binding to the G4 part. However, current systems are limited by relatively low enhancement of fluorescence upon dye binding. Here, we added duplex modules to G4 structures, which supposedly cause the formation of a dye-binding cavity between two modules. Screening of multiple synthetic GFP chromophore analogues and variation of the duplex module resulted in the selection of dyes that light up after complex formation with two-module structures and their RNA analogues by up to 20 times compared to parent G4s. We demonstrated that the short duplex part in TBA25 is preferable for fluorescence light up in comparison to parent TBA15 molecule as well as TBA31 and TBA63 stabilized by longer duplexes. Duplex part of TBA25 may be partially unfolded and has reduced rigidity, which might facilitate optimal dye positioning in the joint between G4 and the duplex. We demonstrated dye enhancement after binding to modified TBA, LTR-III, and Tel23a G4 structures and propose that such architecture of short duplex-G4 signaling elements will enforce the development of improved aptasensors.

Quantitative Trait Module-Based Genetic Analysis of Alzheimer’s Disease

The pathological features of Alzheimer’s Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson’s correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus–cerebellum–brainstem–pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.

Combinatorial Test Design Using Design-Time Decisions for Variability

Design-time artifacts such as module structures from the module view, component diagrams from the component-and-connector view, sequence diagrams, communication diagrams, activity diagrams, or state-machine diagrams from the behavioral view of software architecture can be used to generate effective test cases for fault detection at integration testing level. Various combinatorial test design (CTD) methods have been developed for generating valid and representative product configurations for a software product line, but these methods assume that all bindings for variabilities are performed at run time. Thus, these methods have a limitation in that they do not consider design decisions for variability represented in design-time artifacts. This paper aims to propose a CTD method that uses design-time variability, called the DesignTimeCTD method, considering variability-related decisions at design stage and investigating the effectiveness of reducing the number of tests. The DesignTimeCTD uses variability model, variation points in design-time artifacts, and variability implementation mechanisms used for implementation together in order to reduce the number of tests. As a result, the CTD method, which uses design-time decisions for variability, contributes to reducing the test case by approximately 14% by allowing test engineers to consider design-time variability-related decisions together.

Cycle and flow trusses in directed networks

When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.