AIMC Topic: Machine Learning

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A computational strategy for finding novel targets and therapeutic compounds for opioid dependence.

PloS one
Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and ...

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal of affective disorders
BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine lea...

Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Journal of chemical information and modeling
Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered...

In Silico Prediction of O⁶-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

Molecules (Basel, Switzerland)
O⁶-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O⁶-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer r...

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.

Scientific reports
Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishmen...

A method of inferring the relationship between Biomedical entities through correlation analysis on text.

Biomedical engineering online
BACKGROUND: One of the most important processes in a machine learning-based natural language processing is to represent words. The one-hot representation that has been commonly used has a large size of vector and assumes that the features that make u...

Machine learning in medicine: Addressing ethical challenges.

PLoS medicine
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians.

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

Psychological medicine
BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.

IEEE transactions on bio-medical engineering
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) usi...