Brandon Malone
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View article: Transfer Learning for T-Cell Response Prediction
Transfer Learning for T-Cell Response Prediction Open
We study the prediction of T-cell response for specific given peptides, which could, among other applications, be a crucial step towards the development of personalized cancer vaccines. It is a challenging task due to limited, heterogeneou…
View article: A Relational-Learning Perspective To Multi-Label Chest X-Ray Classification
A Relational-Learning Perspective To Multi-Label Chest X-Ray Classification Open
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approaches make it challenging to incorporate auxiliary information…
View article: A Relational-learning Perspective to Multi-label Chest X-ray\n Classification
A Relational-learning Perspective to Multi-label Chest X-ray\n Classification Open
Multi-label classification of chest X-ray images is frequently performed\nusing discriminative approaches, i.e. learning to map an image directly to its\nbinary labels. Such approaches make it challenging to incorporate auxiliary\ninformat…
View article: Monitoring Cell-Type–Specific Gene Expression Using Ribosome Profiling In Vivo During Cardiac Hemodynamic Stress
Monitoring Cell-Type–Specific Gene Expression Using Ribosome Profiling In Vivo During Cardiac Hemodynamic Stress Open
Rationale: Gene expression profiles have been mainly determined by analysis of transcript abundance. However, these analyses cannot capture posttranscriptional gene expression control at the level of translation, which is a key step in the…
View article: m<sup>6</sup>A-mRNA methylation regulates cardiac gene expression and cellular growth
m<sup>6</sup>A-mRNA methylation regulates cardiac gene expression and cellular growth Open
Conceptually similar to modifications of DNA, mRNAs undergo chemical modifications, which can affect their activity, localization, and stability. The most prevalent internal modification in mRNA is the methylation of adenosine at the N 6 -…
View article: MedEx – Data Analytics for Medical Domain Experts in Real-Time
MedEx – Data Analytics for Medical Domain Experts in Real-Time Open
Translational research in the medical sector is dependent on clear communication between all participants. Visualization helps to represent data from different sources in a comprehensible way across disciplines. Existing tools for clinical…
View article: Learning Representations of Missing Data for Predicting Patient Outcomes
Learning Representations of Missing Data for Predicting Patient Outcomes Open
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, su…
View article: Knowledge Graph Completion to Predict Polypharmacy Side Effects
Knowledge Graph Completion to Predict Polypharmacy Side Effects Open
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work,…
View article: Bayesian prediction of RNA translation from ribosome profiling
Bayesian prediction of RNA translation from ribosome profiling Open
Ribosome profiling via high-throughput sequencing (ribo-seq) is a promising new technique for characterizing the occupancy of ribosomes on messenger RNA (mRNA) at base-pair resolution. The ribosome is responsible for translating mRNA into …
View article: Bayesian identification of bacterial strains from sequencing data
Bayesian identification of bacterial strains from sequencing data Open
Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is…
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
Benchmarking data for bacterial strain identification. See http://arxiv.org/abs/1511.06546 for details.
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
Benchmarking data for bacterial strain identification. See http://arxiv.org/abs/1511.06546 for details.
View article: Bayesian identification of bacterial strains from sequencing data
Bayesian identification of bacterial strains from sequencing data Open
Benchmarking data for bacterial strain identification as published in Microbial Genomics in the following article: 'Bayesian identification of bacterial strains from sequencing data' [DOI: 10.1099/mgen.0.000075]
View article: Bayesian identification of bacterial strains from sequencing data
Bayesian identification of bacterial strains from sequencing data Open
Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is …
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
See http://arxiv.org/abs/1511.06546 for details
View article: Exp1_Sample1
Exp1_Sample1 Open
Please view the full "Benchmarking data for bacterial strain identification set" http://figshare.com/articles/Benchmarking_data_for_bacterial_strain_identification/1617539
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
Benchmarking data for bacterial strain identification. See http://arxiv.org/abs/1511.06546 for details.
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
Benchmarking data for bacterial strain identification. See http://arxiv.org/abs/1511.06546 for details.
View article: Benchmarking data for bacterial strain identification: Exp1_Sample1
Benchmarking data for bacterial strain identification: Exp1_Sample1 Open
Benchmarking data for bacterial strain identification See http://arxiv.org/abs/1511.06546 for details
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
Benchmarking data for bacterial strain identification. See http://arxiv.org/abs/1511.06546 for details.
View article: Benchmarking data for bacterial strain identification
Benchmarking data for bacterial strain identification Open
Benchmarking data for bacterial strain identification. See http://arxiv.org/abs/1511.06546 for details.
View article: Hashing-Based Hybrid Duplicate Detection for Bayesian Network Structure Learning
Hashing-Based Hybrid Duplicate Detection for Bayesian Network Structure Learning Open
In this work, we address the well-known score-based Bayesian network structure learning problem. Breadth-first branch and bound (BFBnB) has been shown to be an effective approach for solving this problem. Delayed duplicate detection (DDD) …
View article: Empirical Behavior of Bayesian Network Structure Learning Algorithms
Empirical Behavior of Bayesian Network Structure Learning Algorithms Open
Bayesian network structure learning (BNSL) is the problem of finding a BN structure which best explains a dataset. Score-based learning assigns a score to each network structure. The goal is to find the structure which optimizes the score.…