Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer's disease to Parkinson's disease.

Purpose: Monitoring and predicting the cognitive state of subjects with neurodegenerative disorders is crucial to provide appropriate treatment as soon as possible. In this work, we present a machine learning approach using multimodal data (brain MRI and clinical) from two early medical visits, to predict the longer-term cognitive decline of patients. Using transfer learning, our model can be successfully transferred from one neurodegenerative disease (Alzheimer's) to another (Parkinson's).

Metabolic network reconstruction and their topological analysis.

This chapter focuses on the way to build a metabolic network and how to analyze its structure. The first part of this chapter describes the methods of the network model reconstruction from biochemical data found in specialized databases and/or literature. The second part deals with metabolic pathway analysis as a useful tool for better understanding the complex architecture of intracellular metabolism.

Fast computation of minimal cut sets in metabolic networks with a Berge algorithm that utilizes binary bit pattern trees.

Minimal cut sets are a valuable tool for analyzing metabolic networks and for identifying optimal gene intervention strategies by eliminating unwanted metabolic functions and keeping desired functionality. Minimal cut sets rely on the concept of elementary flux modes, which are sets of indivisible metabolic pathways under steady-state condition. However, the computation of minimal cut sets is nontrivial, as even medium-sized metabolic networks with just 100 reactions easily have several hundred million elementary flux modes.

Comparison between elementary flux modes analysis and 13C-metabolic fluxes measured in bacterial and plant cells.

Background: (13)C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis.

ACoM: A classification method for elementary flux modes based on motif finding.

Elementary flux mode analysis is a powerful tool for the theoretical study of metabolic networks. However, when the networks are complex, the determination of elementary flux modes leads to combinatorial explosion of their number which prevents from drawing simple conclusions from their analysis. To deal with this problem we have developed a method based on the Agglomeration of Common Motifs (ACoM) for classifying elementary flux modes.

Formal TCA cycle description based on elementary actions.

Many databases propose their own structure and format to provide data describing biological processes. This heterogeneity contributes to the difficulty of large systematic and automatic functional comparisons. To overcome these problems, we have used the BioPsi formal description scheme which allows multi-level representations of biological process information.

Pathway classification of TCA cycle.

The structural analysis of large metabolic networks exhibits a combinatorial explosion of elementary modes. A new method of classification has been developed [called aggregation around common motif (ACoM)], which groups elementary modes into classes with similar substructures. This method is applied to the tricarboxylic acid cycle and metabolite carriers.

A hyperstructure approach to mitochondria.

Several questions in our understanding of mitochondria are unanswered. These include how the ratio of mitochondrial (mt)DNA to mitochondria is maintained, how the accumulation of defective, rapidly replicating mitochondrial DNA is avoided, how the ratio of mitochondria to cells is adjusted to fit cellular needs, and why any proteins are synthesized in mitochondria rather than simply imported. In bacteria, large hyperstructures or assemblies of proteins, mRNA, lipids and ions have been proposed to constitute a level of organization intermediate between macromolecules and whole cells.

Multipoint linkage analysis using the weighted-pairwise correlation statistic.

The weighted pairwise correlation (WPC) approach provides simple and flexible non-parametric tests for genetic linkage which may be adapted to qualitative, quantitative or age-dependent traits. Although this statistic has often been used with identity-by-state (IBS) information, there is much to gain by using multipoint identity-by-descent (IBD) information. The purpose of this paper is to use the unified multipoint approach of Kruglyak et al.

WPC test based on randomization for analyzing quantitative traits on simulated pedigrees.

The weighted pairwise correlation (WPC) approach provides simple and flexible tests for genetic linkage which may be adapted to qualitative, quantitative or age-dependent traits. These tests also seem to have good power. However, when working with large pedigrees, a disease susceptibility gene not linked to the marker studied induces correlations of the trait values, leading to inflated type I errors for these tests.