Discovering Interpretable Dynamics with Partial Information

Es construeix un algoritme amb intel·ligència artificial i se'n demostra el seu funcionament. Aquest algoritme, donades observacions de part d'un sistema regit per una equació diferencial (ja siguin EDO's o EDP's), és capaç de recuperar l'equació diferencial del sistema.Se diseña y testa un algoritmo de inteligencia artificial. Este algoritmo, dadas observaciones de parte de un sistema mayor governado por una equación diferencial (ya sea EDO o EDP), es capaz de recuperar la equación diferencial de dicho sistema.A machine learning algorithm is constructed and tested. This algorithm, given observations of a subpart of a bigger system governed by a differential equation (either ODE or PDE) is able to recover such differential equation.Outgoin

Exploration of Boltzmann Machines via Bars and Stripes

2018/201

Exploration of Boltzmann Machines via Bars and Stripes

2018/201

Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation

Motivation: Computational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations. Results: Here we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here. Availability and implementation: All calculations were implemented by in-house PERL scripts. Supplementary information: Supplementary data are available at Bioinformatics online.This work was supported by the HHMI International Early Career Scientist Program [55007424], the MINECO [BFU2015-68723-P], Spanish Ministry of Economy and Competitiveness Centro de Excelencia Severo Ochoa 2013-2017 [grant SEV-2012-0208], Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat’s AGAUR [program 2014 SGR 0974], the European Research Council under the European Union's Seventh Framework Programme [FP7/2007-2013, ERC grant agreement 335980_EinME] and Russian Scientific Foundation (RSF #14-24-00157, the part about I-Mutant calculations). The work was started at the School of Molecular and Theoretical Biology supported by the Dynasty Foundation