Principles and Practice of Constraint Programming
25th International Conference, CP 2019, Stamford, CT, USA, September 30 – October 4, 2019, Proceedings
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Chapter and Conference Paper
Bi-objective Discrete Graphical Model Optimization
Discrete Graphical Models (GMs) are widely used in Artificial Intelligence to describe complex systems through a joint function of interest. Probabilistic GMs such as Markov Random Fields (MRFs) define a joint...
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Protocol
Computational Design of Miniprotein Binders
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeuti...
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Chapter
Cost Function Networks to Solve Large Computational Protein Design Problems
Proteins are chains of simple molecules called amino acids. The sequence of amino acids in the chain defines the three-dimensional shape of the protein and ultimately its biochemical function. Over millions of...
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Chapter and Conference Paper
Pushing Data into CP Models Using Graphical Model Learning and Solving
Integrating machine learning with automated reasoning is one of the major goals of modern AI systems. In this paper, we propose a non-fully-differentiable architecture that is able to extract preferences from ...
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Chapter
Valued Constraint Satisfaction Problems
an of constraint networks, valued constraint networks (or valued CSPs) define a framework for modelling optimisation problems over finite domains in which the cost domain (also denoted as the valuation struc...
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Book and Conference Proceedings
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Protocol
EuGene: An Automated Integrative Gene Finder for Eukaryotes and Prokaryotes
EuGene is an integrative gene finder applicable to both prokaryotic and eukaryotic genomes. EuGene annotated its first genome in 1999. Starting from genomic DNA sequences representing a complete genome, EuGene...
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Article
Open AccessThe sunflower genome provides insights into oil metabolism, flowering and Asterid evolution
A high-quality reference for the sunflower genome (Helianthus annuus L.) and analysis of gene networks involved in flowering time and oil metabolism provide a basis for nutritional exploitation and analyses of ad...
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Article
Triangle-based consistencies for cost function networks
Cost Function Networks (aka Weighted CSP) allow to model a variety of problems, such as optimization of deterministic and stochastic graphical models including Markov random Fields and Bayesian Networks. Solvi...
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Protocol
Deterministic Search Methods for Computational Protein Design
One main challenge in Computational Protein Design (CPD) lies in the exploration of the amino-acid sequence space, while considering, to some extent, side chain flexibility. The exorbitant size of the search s...
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Article
Multi-language evaluation of exact solvers in graphical model discrete optimization
By representing the constraints and objective function in factorized form, graphical models can concisely define various NP-hard optimization problems. They are therefore extensively used in several areas of c...
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Chapter and Conference Paper
Guaranteed Weighted Counting for Affinity Computation: Beyond Determinism and Structure
Computing the constant Z that normalizes an arbitrary distribution into a probability distribution is a difficult problem that has applications in statistics, biophysics and probabilistic reasoning. In biophysics...
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Article
Strong consistencies for weighted constraint satisfaction problems
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Chapter and Conference Paper
Approximate Counting with Deterministic Guarantees for Affinity Computation
Computational Protein Design aims at rationally designing amino-acid sequences that fold into a given three-dimensional structure and that will bestow the designed protein with desirable properties/functions. ...
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Chapter and Conference Paper
Anytime Hybrid Best-First Search with Tree Decomposition for Weighted CSP
We propose Hybrid Best-First Search (HBFS), a search strategy for optimization problems that combines Best-First Search (BFS) and Depth-First Search (DFS). Like BFS, HBFS provides an anytime global lower bound on...
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Chapter
A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context
In this chapter, we study different gene regulatory network learning methods based on penalized linear regressions (the Lasso regression and the Dantzig Selector), Bayesian networks, and random forests. We als...
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Article
Open AccessDetecting long tandem duplications in genomic sequences
Detecting duplication segments within completely sequenced genomes provides valuable information to address genome evolution and in particular the important question of the emergence of novel functions. The us...
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Chapter and Conference Paper
Computational Protein Design as a Cost Function Network Optimization Problem
Proteins are chains of simple molecules called amino acids. The three-dimensional shape of a protein and its amino acid composition define its biological function. Over millions of years, living organisms have...
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Article
Open AccessThe Medicago genome provides insight into the evolution of rhizobial symbioses
Sequencing of Medicago truncatula, a model organism of legume biology, shows that genome duplications had a role in the evolution of endosymbiotic nitrogen fixation.
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Article
Open AccessThe genome of Theobroma cacao
Xavier Argout and colleagues report the draft genome of Theobroma cacao, the tropical crop that is the source of chocolate. The sequence assembly covers approximately 80% of the genome.
