Practice Management

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 indivi...

What Is in a Plan? Using Natural Language Processing to Read 461 California City General Plans.

Land-use control is local and highly varied. State agencies struggle to assess plan contents. Similarly, advocacy groups and planning researchers wrestle with the length of planning documents and ability to compare across plans. The goal of this research is to (1) introduce Natural Language Processing techniques that can automate qualitative coding in planning research and (2) provide policy-relev...

Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and pr...

Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.

Pathology reports represent a primary source of information for cancer registries. Hospitals routinely process high volumes of free-text reports, a va...

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights f...

Controllable stroke-based sketch synthesis from a self-organized latent space.

Learning to synthesize free-hand sketches controllably according to specified categories and sketching styles is a challenging task, due to the lack o...

Sparse deep predictive coding captures contour integration capabilities of the early visual system.

Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-depe...

What Do Patients Care About? Mining Fine-grained Patient Concerns from Online Physician Reviews Through Computer-Assisted Multi-level Qualitative Analysis.

Online physician review (OPR) websites have been increasingly used by healthcare consumers to make informed decisions in selecting healthcare provider...

A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.

BACKGROUND: Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important h...

Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition.

In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the propose...

NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image.

Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degr...

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records.

The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and rep...

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and...

Pre-training phenotyping classifiers.

Recent transformer-based pre-trained language models have become a de facto standard for many text classification tasks. Nevertheless, their utility i...

Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network.

This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition ...

The Relationship between Sparseness and Energy Consumption of Neural Networks.

About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in th...

Impact of chronic intermittent hypoxia on the long non-coding RNA and mRNA expression profiles in myocardial infarction.

Chronic intermittent hypoxia (CIH) is the primary feature of obstructive sleep apnoea (OSA), a crucial risk factor for cardiovascular diseases. Long n...

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists with...

Deep learning predicts short non-coding RNA functions from only raw sequence data.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a com...

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model.

Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numer...