Biology subjects

Patel, A. N.

Publications and source records attributed to Patel, A. N..

3 recordsLinked to original sources

bioRxiv2018

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants

Variant interpretation in the era of next-generation sequencing (NGS) is challenging. While many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here we present \"PeCanPIE\" - the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in Variant Call Format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4,000 pediatric cancer patients, and serves as a repository for the expert-reviewed results. While PeCanPIEs web-based interface was designed to be accessible to non-bioinformaticians, its back end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

bioinformatics↗

bioRxiv2018

Evidence for hyper-reductive redox in a subset of heart failure patients

BackgroundOxidative stress has been linked to heart failure (HF) in humans. Antioxidant-based treatments are often ineffective. Therefore, we hypothesize that some of the HF patients might have a reductive stress (RS) condition. Investigating RS-related mechanisms will aid in personalized optimization of redox homeostasis for better outcomes among HF patients.\n\nMethodsBlood samples were collected from HF patients (n=54) and healthy controls (n=42) and serum was immediately preserved in -80{degrees}C for redox analysis. Malondialdehyde (MDA; lipid peroxidation) levels by HPLC, reduced glutathione (GSH) and its redox ratio (GSH/GSSG) using enzymatic-recycling assay in the serum of HF patients were measured. Further, the kinetics of key enzymatic-antioxidant enzymes was analyzed by UV-Vis spectrophotometry. Non-invasive echocardiography was used to relate circulating redox status with cardiac function and remodeling.\n\nResultsThe circulatory redox state (GSH/MDA ratio) was used to stratify the HF patients into normal redox (NR), hyper-oxidative (HO), and hyper-reductive (HR) groups. While the majority of the HF patients exhibited the HO (42%), 41% of them had a normal redox (NR) state. Surprisingly, a subset of HF patients (17%) belonged to the hyper-reductive group, suggesting a strong implication for RS in the progression of HF. In HF patients, SOD, GPx and catalase were significantly increased while GR activity was significantly reduced relative to healthy controls. Furthermore, echocardiography analyses revealed that 55% of HO patients had higher systolic dysfunction while 75% of the hyper-reductive patients had higher diastolic dysfunction.\n\nConclusionThese results suggest that RS may be associated with HF pathogenesis for a subset of cardiac patients. Thus, stratification of HF patients based on their circulating redox status may serve as a useful prognostic tool to guide clinicians designing personalized antioxidant therapies.

biochemistry↗

bioRxiv2017

Sequence Transfer Learning for Neural Decoding

A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. in typical applications, decoders are constructed for individual subjects and with limited data leading to restrictions on the types of models that can be utilized. currently, the best performing decoders are typically linear models capable of utilizing rigid timing constraints with limited training data. Here we demonstrate the use of Long Short-Term Memory (LSTM) networks to take advantage of the temporal information present in sequential neural data collected from subjects implanted with electrocorticographic (ECoG) electrode arrays performing a finger flexion task. our constructed models are capable of achieving accuracies that are comparable to existing techniques while also being robust to variation in sample data size. Moreover, we utilize the LSTM networks and an affine transformation layer to construct a novel architecture for transfer learning. We demonstrate that in scenarios where only the affine transform is learned for a new subject, it is possible to achieve results comparable to existing state-of-the-art techniques. The notable advantage is the increased stability of the model during training on novel subjects. Relaxing the constraint of only training the affine transformation, we establish our model as capable of exceeding performance of current models across all training data sizes. Overall, this work demonstrates that LSTMS are a versatile model that can accurately capture temporal patterns in neural data and can provide a foundation for transfer learning in neural decoding.

neuroscience↗