-
Article
Open AccessDiagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and...
-
Article
Fast and memory-efficient algorithms for high-order Tucker decomposition
Multi-aspect data appear frequently in web-related applications. For example, product reviews are quadruplets of the form (user, product, keyword, timestamp), and search-engine logs are quadruplets of the form...
-
Article
Open AccessDiscriminative and Distinct Phenoty** by Constrained Tensor Factorization
Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenoty** offers a promising way...
-
Article
Open AccessDiscovery of prostate specific antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy
Prostate specific antigen (PSA) is an important biomarker to monitor the response to the treatment, but has not been fully utilized as a whole sequence. We used a longitudinal biomarker PSA to discover a new p...
-
Article
Open AccessIs lymphovascular invasion a powerful predictor for biochemical recurrence in pT3 N0 prostate cancer? Results from the K-CaP database
To assess the impact of lymphovascular invasion (LVI) on the risk of biochemical recurrence (BCR) in pT3 N0 prostate cancer, clinical data were extracted from 1,622 patients with pT3 N0 prostate cancer from th...
-
Article
Open AccessDevelo** a hybrid dictionary-based bio-entity recognition technique
Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques.
-
Article
Open AccessEfficient protein structure search using indexing methods
Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally simila...
-
Article
Open AccessCombining active learning and semi-supervised learning techniques to extract protein interaction sentences
Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract ...
-
Article
Open AccessProcessing SPARQL queries with regular expressions in RDF databases
As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf...
-
Article
Selective sampling techniques for feedback-based data retrieval
As many databases have been brought online, data retrieval—finding relevant data from large databases—has become a nontrivial task. A feedback-based data retrieval system was proposed to provide user with an i...
-
Article
Open AccessEnabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS
Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results....
-
Article
Privacy-preserving SVM classification
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, t...
-
Article
Single-Class Classification with Map** Convergence
Single-Class Classification (SCC) seeks to distinguish one class of data from universal set of multiple classes. We call the target class positive and the complement set of samples negative. In SCC problems, it i...
-
Article
Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing
Support vector machines (SVMs) have been promising methods for classification and regression analysis due to their solid mathematical foundations, which include two desirable properties: margin maximization an...
