Prescriptions

Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from p...

Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.

OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.

Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.

Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug intera...

State representation learning for control: An overview.

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a pa...

Predict effective drug combination by deep belief network and ontology fingerprints.

The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective d...

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors.

Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction...

Do a robot's social skills and its objection discourage interactants from switching the robot off?

Building on the notion that people respond to media as if they were real, switching off a robot which exhibits lifelike behavior implies an interestin...

Normalizing Spontaneous Reports Into MedDRA: Some Experiments With MagiCoder.

Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of...

Using natural language processing methods to classify use status of dietary supplements in clinical notes.

BACKGROUND: Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medicati...

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relation...

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges.

The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing a...

Artificial intelligence in drug design.

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug de...

Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data.

Development of new medications is a lengthy and costly process, and drug repositioning might help to shorten the development cycle. We present a machi...

Drug response prediction by ensemble learning and drug-induced gene expression signatures.

Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Rec...

Using a deep learning network to recognise low back pain in static standing.

Low back pain (LBP) remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognise LBP patients from healthy pop...

Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer.

This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment ...

A meta-learning framework using representation learning to predict drug-drug interaction.

MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the n...

Drug Selection via Joint Push and Learning to Rank.

Selecting the right drugs for the right patients is a primary goal of precision medicine. In this article, we consider the problem of cancer drug sele...

A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases.

BACKGROUND AND OBJECTIVE: Drug-drug interaction (DDI) is one of the main causes of toxicity and treatment inefficacy. This work focuses on non-communi...

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The...