A pattern-discovery-based outcome predictive tool integrated with clinical data repository: design and a case study on contrast related acute kidney injury.

Background: Clinical data repositories (CDR) including electronic health record (EHR) data have great potential for outcome prediction and risk modeling. We built a prediction tool integrated with CDR based on pattern discovery and demonstrated a case study on contrast related acute kidney injury (AKI).

Methods: Patients undergoing cardiac catheterization from January 2015 to April 2017 were included.

Acute Coronary Syndrome Risk Prediction Based on GRACE Risk Score.

Clinical risk prediction of acute coronary syndrome (ACS) plays a critical role for clinical decision support, treatment management and quality of care assessment in ACS patients. Admission records contain a wealth of patient information in the early stages of hospitalization, which offers the opportunity to support the ACS risk prediction in a proactive manner. However, ACS patient risks aren't recorded in hospital admission records, thus impeding the construction of supervised risk prediction models.

Association Patterns of Ontological Features Signify Electronic Health Records in Liver Cancer.

Electronic Health Record (EHR) system enables clinical decision support. In this study, a set of 112 abdominal computed tomography imaging examination reports, consisting of 59 cases of hepatocellular carcinoma (HCC) or liver metastases (so-called HCC group for simplicity) and 53 cases with no abnormality detected (NAD group), were collected from four hospitals in Hong Kong. We extracted terms related to liver cancer from the reports and mapped them to ontological features using Systematized Nomenclature of Medicine (SNOMED) Clinical Terms (CT).

Imbalanced target prediction with pattern discovery on clinical data repositories.

Background: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted.

MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records.

Objectives: Major adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data.

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome.

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner.

Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records.

Alternative splicing modulated by genetic variants demonstrates accelerated evolution regulated by highly conserved proteins.

Identification of functional genetic variants and elucidation of their regulatory mechanisms represent significant challenges of the post-genomic era. A poorly understood topic is the involvement of genetic variants in mediating post-transcriptional RNA processing, including alternative splicing. Thus far, little is known about the genomic, evolutionary, and regulatory features of genetically modulated alternative splicing (GMAS).

Research Resource: Hormones, Genes, and Athleticism: Effect of Androgens on the Avian Muscular Transcriptome.

Male vertebrate social displays vary from physically simple to complex, with the latter involving exquisite motor command of the body and appendages. Studies of these displays have, in turn, provided substantial insight into neuromotor mechanisms. The neotropical golden-collared manakin (Manacus vitellinus) has been used previously as a model to investigate intricate motor skills because adult males of this species perform an acrobatic and androgen-dependent courtship display.

Discovering Binding Cores in Protein-DNA Binding Using Association Rule Mining with Statistical Measures.

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and for the deep understanding of gene regulation. Traditionally, binding cores are identified in resolved high-resolution 3D structures. However, it is expensive, labor-intensive and time-consuming to obtain these structures.

The landscape of microRNA, Piwi-interacting RNA, and circular RNA in human saliva.

Background: Extracellular RNAs (exRNAs) in human body fluids are emerging as effective biomarkers for detection of diseases. Saliva, as the most accessible and noninvasive body fluid, has been shown to harbor exRNA biomarkers for several human diseases. However, the entire spectrum of exRNA from saliva has not been fully characterized.

Modeling associated protein-DNA pattern discovery with unified scores.

Understanding protein-DNA interactions, specifically transcription factor (TF) and transcription factor binding site (TFBS) bindings, is crucial in deciphering gene regulation. The recent associated TF-TFBS pattern discovery combines one-sided motif discovery on both the TF and the TFBS sides. Using sequences only, it identifies the short protein-DNA binding cores available only in high-resolution 3D structures.

DNA motif elucidation using belief propagation.

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k=8∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations.

Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification.

Background: Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples.

Subtypes of associated protein-DNA (Transcription Factor-Transcription Factor Binding Site) patterns.

In protein-DNA interactions, particularly transcription factor (TF) and transcription factor binding site (TFBS) bindings, associated residue variations form patterns denoted as subtypes. Subtypes may lead to changed binding preferences, distinguish conserved from flexible binding residues and reveal novel binding mechanisms. However, subtypes must be studied in the context of core bindings.

Heat shock protein 27 mediates the effect of 1,3,5-trihydroxy-13,13-dimethyl-2H-pyran [7,6-b] xanthone on mitochondrial apoptosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a global public health problem which causes approximately 500,000 deaths annually. Considering that the limited therapeutic options for HCC, novel therapeutic targets and drugs are urgently needed. In this study, we discovered that 1,3,5-trihydroxy-13,13-dimethyl-2H-pyran [7,6-b] xanthone (TDP), isolated from the traditional Chinese medicinal herb, Garcinia oblongifolia, effectively inhibited cell growth and induced the caspase-dependent mitochondrial apoptosis in HCC.

Apoptosis induced by 1,3,6,7-tetrahydroxyxanthone in Hepatocellular carcinoma and proteomic analysis.

Gamboge is a traditional Chinese medicine and our previous study showed that gambogic acid and gambogenic acid suppress the proliferation of HCC cells. In the present study, another active component, 1,3,6,7-tetrahydroxyxanthone (TTA), was identified to effectively suppress HCC cell growth. In addition, our Hoechst-PI staining and flow cytometry analyses indicated that TTA induced apoptosis in HCC cells.

Discovering approximate-associated sequence patterns for protein-DNA interactions.

Motivation: The bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental protein-DNA interactions in transcriptional regulation. Extensive efforts have been made to better understand the protein-DNA interactions. Recent mining on exact TF-TFBS-associated sequence patterns (rules) has shown great potentials and achieved very promising results.

A cluster refinement algorithm for motif discovery.

Finding Transcription Factor Binding Sites, i.e., motif discovery, is crucial for understanding the gene regulatory relationship.

Discovering protein-DNA binding sequence patterns using association rule mining.

Protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play an essential role in transcriptional regulation. Over the past decades, significant efforts have been made to study the principles for protein-DNA bindings. However, it is considered that there are no simple one-to-one rules between amino acids and nucleotides.

Discovering multiple realistic TFBS motifs based on a generalized model.

Background: Identification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range.

TFBS identification based on genetic algorithm with combined representations and adaptive post-processing.

Motivation: Identification of transcription factor binding sites (TFBSs) plays an important role in deciphering the mechanisms of gene regulation. Recently, GAME, a Genetic Algorithm (GA)-based approach with iterative post-processing, has shown superior performance in TFBS identification. However, the basic GA in GAME is not elaborately designed, and may be trapped in local optima in real problems.