Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations

Panzner M, Cimiano P. Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations. In: Pardalos PM, Conca P, Giuffrida G, Nicosia G, eds. Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016. Revised Selected Papers. Lecture Notes in Computer Science. Vol 10122. Cham: Springer International Publishing; 2016: 94-105

Accurate prediction of kinase-substrate networks using knowledge graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder)

Multi-objective optimization of genome-scale metabolic models: the case of ethanol production

Ethanol is among the largest fermentation product used worldwide, accounting for more than 90% of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to 19.74mmolgDW−1h−1 (+832.88% with respect to wild-type) and biomass production of 0.02h−1 (−98.06% ). The analyses on E. coli highlighted a single knockout strategy producing 16.49mmolgDW−1h−1 (+679.29% ) ethanol, with biomass equals to 0.23h−1 (−77.45% ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is +195.24%

An Adaptive Framework for Classification of Concept Drift with Limited Supervision

This thesis deals with the problem of classification of data affected by concept drift. In particular, it investigates the area of unsupervised model updating in which a classification model is updated without using information about the changing distributions of the classes. An adaptive framework that contains an ensemble of classifiers is developed. These can be mature or naive. In particular, only mature classifiers generate decisions, through majority voting, while naive classifiers are candidate to become mature. The first novelty of the proposed framework is a technique of feedback that combines concepts from ensemble-learning with concepts from self-training. In particular, naive classifiers are trained using unlabelled data and labels generated by mature classifiers over that data, by means of voting. This technique allows updates of the model of the framework in absence of supervision, namely, without using the true classes of the data. The second novelty is a technique that infers the presence of concept drift by measuring the similarity between the decisions of mature classifiers and the decisions of naive classifiers. When concept drift is inferred, a naive classifier is selected to become mature, and a mature classifier is deleted. A series of experiments are performed. They show that the framework can classify data with Gaussian distribution, and that this capability regards different classification techniques. The experiments also reveal that the framework cannot deal with the concept drift of a uniformly distributed dataset. Moreover, further experiments show that the inference of drift combines quick adapation with low false detections, thus leading to higher classification performance than comparative methods. However, this technique is not able to detect concept drift if the classes are separable

An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits

In electronics, there are two major classes of circuits, analog and digital electrical circuits. While digital circuits use discrete voltage levels, analog circuits use a continuous range of voltage. The synthesis of analog circuits is known to be a complex optimization task, due to the continuous behaviour of the output and the lack of automatic design tools; actually, the design process is almost entirely demanded to the engineers. In this research work, we introduce a new clonal selection algorithm, the elitist Immune Programming, (eIP) which uses a new class of hypermutation operators and a network-based coding. The eIP algorithm is designed for the synthesis of topology and sizing of analog electrical circuits; in particular, it has been used for the design of passive filters. To assess the effectiveness of the designed algorithm, the obtained results have been compared with the passive filter discovered by Koza and co-authors using the Genetic Programming (GP) algorithm. The circuits obtained by eIP algorithm are better than the one found by GP in terms of frequency response and number of components required to build it. © 2008 Springer-Verlag Berlin Heidelberg

An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments

This paper introduces an adaptive framework that makes use of ensemble classification and self-training to maintain high classification performance in datasets affected by concept drift without the aid of external supervision to update the model of a classifier. The updating of the model of the framework is triggered by a mechanism that infers the presence of concept drift based on the analysis of the differences between the outputs of the different classifiers. In order to evaluate the performance of the proposed algorithm, comparisons were made with a set of unsupervised classification techniques and drift detection techniques. The results show that the framework is able to react more promptly to performance degradation than the existing methods and this leads to increased classification accuracy. In addition, the framework stores a smaller amount of instances with respect to a single-classifier approach

Receptor tyrosine kinase pathway analysis sheds light on similarities between clear-cell sarcoma and metastatic melanoma

To highlight possible similarities and differences in receptor tyrosine kinase (RTK) and downstream signalling activation profiles between clear-cell sarcomas (CCS) and metastatic melanomas (MM), frozen, and paired-matched fixed samples of six CCS with EWSR1 rearrangement (EWSR1+), five CCS without EWSR1 rearrangement (EWSR1-), and seven MM were investigated by means of biochemical, immunohistochemical, FISH, molecular analyses, and immunofluorescence confocal microscopy. Fixed samples of a further 10 CCS and 14 MM were investigated by means of sequencing for BRAF, NRAS, and KRAS mutations and FISH analyses for the gain of chromosomes 22 and 8. RTK analysis of all CCS/MM samples showed activation of short-form (sf) recepteur d'origine nantais (RON) RTK and of PDGFRB, MET, and HER3. Analysis of downstream signaling revealed consistent phosphorylation patterns of PI3K/AKT, RSK, and the mTOR targets S6 and 4EBP1. Analysis of frozen and fixed material from 21 CCS and 21 MM showed the presence of the V600E BRAF mutation in 2/12 EWSR1+ and 3/9 EWSR1- CCS and 9/21 MM and demonstrated a significant (P < 0.001) correlation between the gain of chromosomes 22 and 8 and EWSR1- CCS. Our results show that BRAF mutation can also be present in CCS and support the proposed aberration of chromosomes 22 and 8 as a possibly useful nonrandom hallmark of EWSR1- CCS. Besides, they broaden the spectrum of the similarities of RTK pathway activation between CCS and MM, thus suggesting that new drugs found to be active in melanoma and RON inhibitors could have a role in CCS treatment

Accurate prediction of kinase-substrate networks using knowledge graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinasesubstrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid highconfidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder)

Accurate prediction of kinase-substrate networks using knowledge graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinasesubstrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid highconfidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder)