Scalable Coupling of Deep Learning with Logical Reasoning

In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-hard reasoning problems expressed as discrete Graphical Models. Our loss function solves one of the main limitations of Besag's pseudo-loglikelihood, enabling learning of high energies. We empirically show it is able to efficiently learn how to solve NP-hard reasoning problems from natural inputs as the symbolic, visual or many-solutions Sudoku problems as well as the energy optimization formulation of the protein design problem, providing data efficiency, interpretability, and \textit{a posteriori} control over predictions.Comment: 10 pages, 2 figures, 6 tables. Published in IJCAI'2023 proceeding

Valued constraint satisfaction problems: Hard and easy problems

tschiexOtoulouse.inra.fr fargierOirit.fr verfailOcert.fr In order to deal with over-constrained Constraint Satisfaction Problems, various extensions of the CSP framework have been considered by taking into account costs, uncertainties, preferences, priorities...Each extension uses a specific mathematical operator (+, max...) to aggregate constraint violations. In this paper, we consider a simple algebraic framework, related to Partial Constraint Satisfaction, which subsumes most of these proposals and use it to characterize existing proposals in terms of rationality and computational complexity. We exhibit simple relationships between these proposals, try t

Computational protein design to accelerate the conception of fine-tuned biocatalysts

The remarkable properties of enzymes (high catalytic efficiency, regio- and stereo-selectivity) have been recognized and largely exploited in biocatalysis. Accordingly, enzyme-driven processes should play an increasing role in the next decades, potentially substituting chemical processes and contributing to the raise of bioeconomy. However, to foresee a viable future to biocatalysis, advances in R&D are required to accelerate the delivery of fine-tuned enzymes displaying high chemical specificity on non-cognate substrates, high efficiency and better stability in reaction conditions. To this end, structure-based Computational Protein Design (CPD) is a promising strategy to fully rationalize and speed-up the conception of new enzymes while reducing associated human and financial costs. By combining physico-chemical models governing relations between protein amino-acid composition and their 3D structure with optimization algorithms, CPD seeks to identify sequences that fold into a given 3D-scaffold and possess the targeted biochemical properties. Starting from a huge search space, the protein sequence-conformation space, this in silico pre-screening aims to considerably narrow down the number of mutants tested at experimental level while substantially increasing the chances of reaching the desired enzyme. While CPD is still a very young and rapidly evolving field, success stories of computationally designed proteins highlight future prospects of this field. Nonetheless, despite landmark achievements, the success rate of the current computational approaches remains low, and designed enzymes are often way less efficient than their natural counterparts. Therefore, several limitations of the CPD still need to be addressed to improve its efficiency, predictability and reliability. Herein, we present our methodological advances in the CPD field that enabled overcoming technological bottlenecks and hence propose innovative CPD methods to explore large sequence-conformation spaces while providing more accuracy and robustness than classical approaches. Our CPD methods speed-up search across vast sequence-conformation spaces by several orders of magnitude, find the minimum energy enzyme design and generate exhaustive lists of near-optimal sequences, defining small mutant libraries. These new methods, in rupture with classical approaches are based on efficient algorithms issued from recent research in artificial intelligence. The performance and accuracy of our computer-aided enzyme design methods have been evaluated and validated on various types of protein design problems. This work was partially funded by INRA/Région Midi-Pyrénées, IDEX Toulouse, Agreenskills and the French National Research Agency (PROTICAD, ANR-12-MONU-0015-03)

High resolution radiation hybrid maps of bovine chromosomes 19 and 29: comparison with the bovine genome sequence assembly

<p>Abstract</p> <p>Background</p> <p>High resolution radiation hybrid (RH) maps can facilitate genome sequence assembly by correctly ordering genes and genetic markers along chromosomes. The objective of the present study was to generate high resolution RH maps of bovine chromosomes 19 (BTA19) and 29 (BTA29), and compare them with the current 7.1X bovine genome sequence assembly (bovine build 3.1). We have chosen BTA19 and 29 as candidate chromosomes for mapping, since many Quantitative Trait Loci (QTL) for the traits of carcass merit and residual feed intake have been identified on these chromosomes.</p> <p>Results</p> <p>We have constructed high resolution maps of BTA19 and BTA29 consisting of 555 and 253 Single Nucleotide Polymorphism (SNP) markers respectively using a 12,000 rad whole genome RH panel. With these markers, the RH map of BTA19 and BTA29 extended to 4591.4 cR and 2884.1 cR in length respectively. When aligned with the current bovine build 3.1, the order of markers on the RH map for BTA19 and 29 showed inconsistencies with respect to the genome assembly. Maps of both the chromosomes show that there is a significant internal rearrangement of the markers involving displacement, inversion and flips within the scaffolds with some scaffolds being misplaced in the genome assembly. We also constructed cattle-human comparative maps of these chromosomes which showed an overall agreement with the comparative maps published previously. However, minor discrepancies in the orientation of few homologous synteny blocks were observed.</p> <p>Conclusion</p> <p>The high resolution maps of BTA19 (average 1 locus/139 kb) and BTA29 (average 1 locus/208 kb) presented in this study suggest that by the incorporation of RH mapping information, the current bovine genome sequence assembly can be significantly improved. Furthermore, these maps can serve as a potential resource for fine mapping QTL and identification of causative mutations underlying QTL for economically important traits.</p