Practice Management

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. ...

The Positive Effect of MiR1 Antagomir on Ischemic Neurological Disorders Via Changing the Expression of Bcl-w and Bad Genes.

INTRODUCTION: MicroRNAs (miRNAs or miRs) are non-coding RNAs. Studies have shown that miRNAs are expressed aberrantly in stroke. The miR1 enhances ischemic damage, and a previous study has demonstrated that reduction of miR1 level has a neuroprotective effect on the Middle Cerebral Artery Occlusion (MCAO). Since apoptosis is one of the important processes in neural protection, the possible effect ...

Population coding in the cerebellum: a machine learning perspective.

The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer...

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision.

Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in ...

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA.

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease a...

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.

Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide significant information on its function. Due to the lac...

LncMirNet: Predicting LncRNA-miRNA Interaction Based on Deep Learning of Ribonucleic Acid Sequences.

Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cum...

A scoping review of machine learning in psychotherapy research.

Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review...

ncRDeep: Non-coding RNA classification with convolutional neural network.

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and canc...

Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

OBJECTIVE: We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-codi...

A Predictive-Coding Network That Is Both Discriminative and Generative.

Predictive coding (PC) networks are a biologically interesting class of neural networks. Their layered hierarchy mimics the reciprocal connectivity pa...

A combined HMM-PCNN model in the contourlet domain for image data compression.

Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, bu...

Feature Selection for Health Care Costs Prediction Using Weighted Evidential Regression.

Although many authors have highlighted the importance of predicting people's health costs to improve healthcare budget management, most of them do not...

lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning.

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-codi...

Computer Vision-Based Grasp Pattern Recognition With Application to Myoelectric Control of Dexterous Hand Prosthesis.

Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel co...

Neural networks of different species, brain areas and states can be characterized by the probability polling state.

Cortical networks are complex systems of a great many interconnected neurons that operate from collective dynamical states. To understand how cortical...

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical c...

Understanding the relationship between patient language and outcomes in internet-enabled cognitive behavioural therapy: A deep learning approach to automatic coding of session transcripts.

Understanding patient responses to psychotherapy is important in developing effective interventions. However, coding patient language is a resource-i...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassif...

Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron.

We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding....