Dissimilarity-Based Multiple Instance Learning

Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the...

Sequence-based learning algorithms for understanding and improving protein characteristics

The development of high-throughput measurement techniques resulted in rapidlyincreasing amounts of biological data, which made computational methodsessential for biological research. Hence, the field of bioinformatics emergedthat since plays an important role in storing, making accessible, integrating,and analysing different types of biological...

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1-like’ or ‘non-BRCA1-like’, which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast...

Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis

Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this...