Data-driven network alignment

Biological network alignment (NA) aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for transfer of functional knowledge between the aligned nodes. However, current NA methods do not end up aligning functionally related nodes. A likely reason is that they assume it is topologically similar nodes that are functionally related. However, we show that this assumption does not hold well. So, a paradigm shift is needed with how the NA problem is approached. We redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity, like traditional NA methods do. TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns. We find that TARA is able to make accurate predictions. TARA then takes each pair of nodes that are predicted as related to be part of an alignment. Like traditional NA methods, TARA uses this alignment for the across-species transfer of functional knowledge. Clearly, TARA as currently implemented uses topological but not protein sequence information for this task. We find that TARA outperforms existing state-of-the-art NA methods that also use topological information, WAVE and SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance

Automatic Accuracy Prediction for AMR Parsing

Abstract Meaning Representation (AMR) represents sentences as directed, acyclic and rooted graphs, aiming at capturing their meaning in a machine readable format. AMR parsing converts natural language sentences into such graphs. However, evaluating a parser on new data by means of comparison to manually created AMR graphs is very costly. Also, we would like to be able to detect parses of questionable quality, or preferring results of alternative systems by selecting the ones for which we can assess good quality. We propose AMR accuracy prediction as the task of predicting several metrics of correctness for an automatically generated AMR parse - in absence of the corresponding gold parse. We develop a neural end-to-end multi-output regression model and perform three case studies: firstly, we evaluate the model's capacity of predicting AMR parse accuracies and test whether it can reliably assign high scores to gold parses. Secondly, we perform parse selection based on predicted parse accuracies of candidate parses from alternative systems, with the aim of improving overall results. Finally, we predict system ranks for submissions from two AMR shared tasks on the basis of their predicted parse accuracy averages. All experiments are carried out across two different domains and show that our method is effective.Comment: accepted at *SEM 201

Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics

For cancer genomics to fully expand its utility from research discovery to clinical adoption, somatic variant detection pipelines must be optimized and standardized to ensure identification of clinically relevant mutations and to reduce laborious and error-prone post-processing steps. To address the need for improved catalogues of clinically and biologically important somatic mutations, we developed DoCM, a Database of Curated Mutations in Cancer (http://docm.info), as described in Chapter 2. DoCM is an open source, openly licensed resource to enable the cancer research community to aggregate, store and track biologically and clinically important cancer variants. DoCM is currently comprised of 1,364 variants in 132 genes across 122 cancer subtypes, based on the curation of 876 publications. To demonstrate the utility of this resource, the mutations in DoCM were used to identify variants of established significance in cancer that were missed by standard variant discovery pipelines (Chapter 3). Sequencing data from 1,833 cases across four TCGA projects were reanalyzed and 1,228 putative variants that were missed in the original TCGA reports were identified. Validation sequencing data were produced from 93 of these cases to confirm the putative variant we detected with DoCM. Here, we demonstrated that at least one functionally important variant in DoCM was recovered in 41% of cases studied. A major bottleneck in the DoCM analysis in Chapter 3 was the filtering and manual review of somatic variants. Several steps in this post-processing phase of somatic variant calling have already been automated. However, false positive filtering and manual review of variant candidates remains as a major challenge, especially in high-throughput discovery projects or in clinical cancer diagnostics. In Chapter 4, an approach that systematized and standardized the post-processing of somatic variant calls using machine learning algorithms, trained on 41,000 manually reviewed variants from 20 cancer genome projects, is outlined. The approach accurately reproduced the manual review process on hold out test samples, and accurately predicted which variants would be confirmed by orthogonal validation sequencing data. When compared to traditional manual review, this approach increased identification of clinically actionable variants by 6.2%. These chapters outline studies that result in substantial improvements in the identification and interpretation of somatic variants, the use of which can standardize and streamline cancer genomics, enabling its use at high throughput as well as clinically

Functional characterization of single amino acid variants

Single amino acid variants (SAVs) are one of the main causes of Mendelian disorders, and play an important role in the development of many complex diseases. At the same time, they are the most common kind of variation affecting coding DNA, without generally presenting any damaging effect. With the advent of next generation sequencing technologies, the detection of these variants in patients and the general population is easier than ever, but the characterization of the functional effects of each variant remains an open challenge. It is our objective in this work to tackle this problem by developing machine learning based in silico SAVs pathology predictors. Having the PMut classic predictor as a starting point, we have rethought the entire supervised learning pipeline, elaborating new training sets, features and classifiers. PMut2017 is the first result of these efforts, a new general-purpose predictor based on SwissVar and trained on 12 different conservation scores. Its performance, evaluated bothby cross-validation and different blind tests, was in line with the best predictors published to date. Continuing our efforts in search for more accurate predictors, especially for those cases were general predictors tend to fail, we developed PMut-S, a suite of 215 protein-specific predictors. Similar to PMut in nature, Pmut-S introduced the use of co-evolution conservation features and balanced training sets, and showed improved performance, specially for those proteins that were more commonly misclassified by PMut. Comparing PMut-S to other specific predictors we proved that it is possible to train specific predictors using a unique automated pipeline and match the results of most gene specific predictors released to date. The implementation of the machine learning pipeline of both PMut and PMut-S was released as an open source Python module: PyMut, which bundles functions implementing the features computation and selection, classifier training and evaluation, plots drawing, among others. Their predictions were also made available in a rich web portal, which includes a precomputed repository with analyses of more than 700 million variants on over 100,000 human proteins, together with relevant contextual information such as 3D visualizationsof protein structures, links to databases, functional annotations, and more.Les mutacions puntuals d’aminoàcids són la principal causa de moltes malalties mendelianes, i juguen un paper important en el desenvolupament de moltes malalties complexes. Alhora, són el tipus de variant més comuna que afecta l’ADN codificant de proteïnes, sense provocar, en general, cap efecte advers. Amb l’adveniment de la seqüenciació de nova generació, la detecció d’aquestes variants en pacients i en la població general és més fàcil que mai, però la caracterització dels efectes funcionals de cada variant segueix sent un repte. El nostre objectiu en aquest treball és abordar aquest problema desenvolupant predictors de patologia in silico basats en l’aprenentatge automàtic. Prenent el predictor clàssic PMut com a punt de partida, hem repensat tot el procés d’aprenentatge supervisat, elaborant nous conjunts d’entrenament, descriptors i classificadors. PMut2017 és el primer resultat d’aquests esforços, un nou predictor basat en SwissVar i entrenat amb 12 mètriques de conservació de seqüència. La seva precisió, mesurada mitjançant validació creuada i amb tests cecs, s’ha mostrar en línia amb els millors predictors publicats a dia d’avui. Continuant els nostres esforços en la cerca de predictors més acurats, hem desenvolupat PMut-S, un conjunt de 215 predictors específics per cada proteïna. Similar a PMut en la seva concepció, PMut-S introdueix l’ús de descriptors basats en la coevolució i conjunts d’entrenament balancejats, millorant el rendiment de PMut2017 en 0.1 punts del coeficient de correlació de Matthews. Comparant PMut-S a d’altres predictors específics hem provat que és possible entrenar predictors específics seguint un únic procediment automatitzat i assolir uns resultats tan bon com els de la majoria de predictors específics publicats. La implementació del procediment d’aprenentatge automàtic tant de PMut com de PMut-S ha sigut publicat com a un mòdul de Python de codi obert: PyMut, el qual inclou les funcions que implementen el càlcul dels descriptors i la seva selecció, l’entrenament i avaluació dels classificadors, el dibuix de diverses gràfiques... Les prediccions també estan disponibles en un portal web que inclou un repositori precalculat amb els anàlisis de més de 700 milions de variants en més de 100 mil proteïnes humanes, junt a rellevant informació de context com visualitzacions 3D de les proteïnes, enllaços a bases de dades, anotacions funcionals i molt més

Predicting syntactic equivalence between source and target sentences

The translation difficulty of a text is influenced by many different factors. Some of these are specific to the source text and related to readability while others more directly involve translation and the relation between the source and the target text. One such factor is syntactic equivalence, which can be calculated on the basis of a source sentence and its translation. When the expected syntactic form of the target sentence is dissimilar to its source, translating said source sentence proves more difficult for a translator. The degree of syntactic equivalence between a word-aligned source and target sentence can be derived from the crossing alignment links, averaged by the number of alignments, either at word or at sequence level. However, when predicting the translatability of a source sentence, its translation is not available. Therefore, we train machine learning systems on a parallel English-Dutch corpus to predict the expected syntactic equivalence of an English source sentence without having access to its Dutch translation. We use traditional machine learning systems (Random Forest Regression and Support Vector Regression) combined with syntactic sentence-level features as well as recurrent neural networks that utilise word embeddings and accurate morpho-syntactic features