Automatically extracting phenotypes (i.e., the composite of ones observable characteristics/traits) from free text such as scientific literature or clinical notes and associating phenotypes with diseases is an important task. Such associations can be used in, for example, recommending candidate genes for diseases, investigating drug targets, or performing differential diagnosis. In this paper, we focus on extracting disease-phenotype relations with association rule mining techniques and compare results with two other methods. We show that association rule mining offers promising alternative method for detecting disease-phenotype relations.

UNLABELLED: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. AVAILABILITY AND IMPLEMENTATION: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. CONTACT: [email protected].

Predicting protein solubility has gained lots of intention in the recent years and several descriptors have been defined to describe proteins in these works. Therefore, different feature selection methods have been used for selecting the most important attributes. An empirical study, that aims to explain the relationship between the number of samples and stability of seven different feature selection techniques for protein datasets, is presented.

Data preprocessing is an important step in preparation of DNA microarray data for further analysis. There is a significant amount of genes that do not influence the final classification. One of the reasons to eliminate such genes is the increasing computational complexity of supervised machine learning methods, especially in modern microarray experiments with hundreds of samples. This empirical study aims to measure differences in classification performance when different numbers of gene expression measurements are removed in a preprocessing phase. Simple unsupervised gene selection based on variance level of genes across all samples was used to remove genes with extremely low level of variance. This study shows the importance of combining unsupervised and supervised feature selection techniques along with classification algorithm. It was shown that gene expression values removed using simple unsupervised gene selection method are not of significant importance to the final results of supervised gene selection followed by classification.

This paper presents an overview of past papers published at the CBMS symposiums from a content analysis point of view. A simple, yet effective word counting using Harvard Psycho-Social dictionary was used to estimate different aspects of sentiment that can be present even in scientific papers. Using simple statistics we uncover some of the very interesting trends in the last five CBMS symposiums. Additional to pure statistics we used some of the most advanced classification techniques to see if there are any significant differences in psycho-social texture of the accepted papers. It was also shown that building machine learning models on this kind of data can result in some very interesting generalizations of the underlying data.

In this paper we present the results of an intelligent analysis of database which was gathered during e- learningproject called DISNET [1] in which a random sample of around 300 unemployed people collaborated. The intelligent data analysis using advanced methods for decision tree construction was used in order to try to find the main factors that influence a trainee's progress in knowing how to use e-materials. We also analysed how trainee's gender influences on the usage of e-materials.