A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications.

The development of precision medicine approaches for diffuse large B cell lymphoma (DLBCL) is confounded by its pronounced genetic, phenotypic, and clinical heterogeneity. Recent multiplatform genomic studies revealed the existence of genetic subtypes of DLBCL using clustering methodologies. Here, we describe an algorithm that determines the probability that a patient's lymphoma belongs to one of seven genetic subtypes based on its genetic features.

RNA Sequencing in B-Cell Lymphomas.

High-throughput mRNA sequencing (RNA-Seq) provides both qualitative and quantitative evaluation of the transcriptome. This method uses complementary DNA (cDNA) to generate several millions of short sequence reads that are aligned to a reference genome allowing the comprehensive characterization of the transcripts in a cell. RNA-Seq has a wide variety of applications which lead to a pervasive adoption of this method well beyond the genomics community and a deployment of this technique as a standard part of the toolkit applied in life sciences.

Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma.

Background: Diffuse large B-cell lymphomas (DLBCLs) are phenotypically and genetically heterogeneous. Gene-expression profiling has identified subgroups of DLBCL (activated B-cell-like [ABC], germinal-center B-cell-like [GCB], and unclassified) according to cell of origin that are associated with a differential response to chemotherapy and targeted agents. We sought to extend these findings by identifying genetic subtypes of DLBCL based on shared genomic abnormalities and to uncover therapeutic vulnerabilities based on tumor genetics.

Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies.

Background: Mapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components.

Methods: Job title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes.

A Longitudinal Support Vector Regression for Prediction of ALS Score.

Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In this work, we propose a novel longitudinal support vector regression (LSVR) algorithm that not only takes the advantage of one of the most popular machine learning methods, but also is able to model the temporal nature of longitudinal data by taking into account observational dependence within subjects.

Computer-Based Coding of Occupation Codes for Epidemiological Analyses.

Mapping job titles to standardized occupation classification (SOC) codes is an important step in evaluating changes in health risks over time as measured in inspection databases. However, manual SOC coding is cost prohibitive for very large studies. Computer based SOC coding systems can improve the efficiency of incorporating occupational risk factors into large-scale epidemiological studies.

Three-Dimensional Spot Detection in Ratiometric Fluorescence Imaging For Measurement of Subcellular Organelles.

Lysosomes are subcellular organelles playing a vital role in the endocytosis process of the cell. Lysosomal acidity is an important factor in assuring proper functioning of the enzymes within the organelle, and can be assessed by labeling the lysosomes with pH-sensitive fluorescence probes. To enhance our understanding of the acidification mechanisms, the goal of this work is to develop a method that can accurately detect and characterize the acidity of each lysosome captured in ratiometric fluorescence images.

Classification of Alzheimer Diagnosis from ADNI Plasma Biomarker Data.

Research into modeling the progression of Alzheimer's disease (AD) has made recent progress in identifying plasma proteomic biomarkers to identify the disease at the pre-clinical stage. In contrast with cerebral spinal fluid (CSF) biomarkers and PET imaging, plasma biomarker diagnoses have the advantage of being cost-effective and minimally invasive, thereby improving our understanding of AD and hopefully leading to early interventions as research into this subject advances. The Alzheimer's Disease Neuroimaging Initiative* (ADNI) has collected data on 190 plasma analytes from individuals diagnosed with AD as well subjects with mild cognitive impairment and cognitively normal (CN) controls.

Reconstruction for time-domain in vivo EPR 3D multigradient oximetric imaging--a parallel processing perspective.

Three-dimensional Oximetric Electron Paramagnetic Resonance Imaging using the Single Point Imaging modality generates unpaired spin density and oxygen images that can readily distinguish between normal and tumor tissues in small animals. It is also possible with fast imaging to track the changes in tissue oxygenation in response to the oxygen content in the breathing air. However, this involves dealing with gigabytes of data for each 3D oximetric imaging experiment involving digital band pass filtering and background noise subtraction, followed by 3D Fourier reconstruction.

Introducing meta-services for biomedical information extraction.

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; http://bcms.bioinfo.cnio.

Rule-based human gene normalization in biomedical text with confidence estimation.

The ability to identify gene mentions in text and normalize them to the proper unique identifiers is crucial for "down-stream" text mining applications in bioinformatics. We have developed a rule-based algorithm that divides the normalization task into two steps. The first step includes pattern matching for gene symbols and an approximate term searching technique for gene names.