12 documents found, page 1 of 2

Deliverable 4.4 (D4.4): Protocol for next-gen analysis of eDNA samples, EnMetaG...

Deliverable 4.3 (D4.3): Protocol for field collection and preservation of eDNA ...

Development of a hybrid application for psychotic disorders self-management

evolvDSO Deliverable D3.3 - Advanced Methodologies and Tools for Operation and ...

evolvDSO Deliverable D3.4 - Validation of the Methodologies and Tools Developed...

Improving the performance of Transposable Elements detection tools

Boosting the Detection of Transposable Elements Using Machine Learning

Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and...

Predicting the secondary structure of proteins using Machine Learning algorithms

The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D- structure arises from a particular linear sequence of amino acids. In this paper we report the application of Mac...

On avoiding redundancy in inductive logic programming

ILP systems induce rst-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. The generation of redundant hypotheses may prevent the systems from nding good models and increases the time to induce them. In this paper we propose a classication of hypotheses redundancy and show how expert knowledge can be provided to an ILP system to avoid it. Experimental...

A parallel ILP algorithm that incorporates incremental batch learning

In this paper we tackle the problems of eciency and scala- bility faced by Inductive Logic Programming (ILP) systems. We propose the use of parallelism to improve eciency and the use of an incremental batch learning to address the scalability problem. We describe a novel parallel algorithm that incorporates into ILP the method of incremen- tal batch learning. The theoretical complexity of the algorithm indicate...