Genetics

Deep Learning for Population Genetic Inference.

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the inp...

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.

OBJECTIVE: Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov.

The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database.

The Rat Genome Database (RGD;http://rgd.mcw.edu/) provides critical datasets and software tools to a diverse community of rat and non-rat researchers ...

Sequencing bilateral robot-assisted arm therapy and constraint-induced therapy improves reach to press and trunk kinematics in patients with stroke.

BACKGROUND: The combination of robot-assisted therapy (RT) and a modified form of constraint-induced therapy (mCIT) shows promise for improving motor ...

REGene: a literature-based knowledgebase of animal regeneration that bridge tissue regeneration and cancer.

Regeneration is a common phenomenon across multiple animal phyla. Regeneration-related genes (REGs) are critical for fundamental cellular processes su...

Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci.

OBJECTIVE: Recent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic...

Genome-enabled prediction using probabilistic neural network classifiers.

BACKGROUND: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled predictio...

Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy.

The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emerg...

Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automa...

Detecting false positive sequence homology: a machine learning approach.

BACKGROUND: Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often dif...

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseas...

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is...

Multi-instance multi-label distance metric learning for genome-wide protein function prediction.

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each trainin...

SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was t...

Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.

BACKGROUND: High throughput technologies have been used to profile genes in multiple different dimensions, such as genetic variation, copy number, gen...

Automated single cell isolation from suspension with computer vision.

Current robots can manipulate only surface-attached cells seriously limiting the fields of their application for single cell handling. We developed a ...

Sparse Inverse Covariance Estimation with L0 Penalty for Network Construction with Omics Data.

Constructing coexpression and association networks with omics data is crucial for studying gene-gene interactions and underlying biological mechanisms...

A knowledgebase of the human Alu repetitive elements.

Alu elements are the most abundant retrotransposons in the human genome with more than one million copies. Alu repeats have been reported to participa...

Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pat...

A Factor Graph Approach to Automated GO Annotation.

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (...