Machine learning in major depression: From classification to treatment outcome prediction

S Gao, VD Calhoun, J Sui - CNS neuroscience & therapeutics, 2018 - Wiley Online Library
Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …

Are innovation and new technologies in precision medicine paving a new era in patients centric care?

AA Seyhan, C Carini - Journal of translational medicine, 2019 - Springer
Healthcare is undergoing a transformation, and it is imperative to leverage new technologies
to generate new data and support the advent of precision medicine (PM). Recent scientific …

Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification

I Jain, VK Jain, R Jain - Applied Soft Computing, 2018 - Elsevier
DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and
its classification. It provides better insights of many genetic mutations occurring within a cell …

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

L Chen, YH Zhang, SP Wang, YH Zhang, T Huang… - PloS one, 2017 - journals.plos.org
Identifying essential genes in a given organism is important for research on their
fundamental roles in organism survival. Furthermore, if possible, uncovering the links …

Better prediction of functional effects for sequence variants

M Hecht, Y Bromberg, B Rost - BMC genomics, 2015 - Springer
Elucidating the effects of naturally occurring genetic variation is one of the major challenges
for personalized health and personalized medicine. Here, we introduce SNAP2, a novel …

Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography

S Narula, K Shameer, AM Salem Omar… - Journal of the American …, 2016 - jacc.org
Background: Machine-learning models may aid cardiac phenotypic recognition by using
features of cardiac tissue deformation. Objectives: This study investigated the diagnostic …

Data-driven advice for applying machine learning to bioinformatics problems

RS Olson, WL Cava, Z Mustahsan, A Varik… - Pacific symposium on …, 2018 - World Scientific
As the bioinformatics field grows, it must keep pace not only with new data but with new
algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used …

An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete

T Han, A Siddique, K Khayat, J Huang… - Construction and Building …, 2020 - Elsevier
This paper presents an ensemble machine learning (ML) model for prediction of modulus of
elasticity (MOE) of concrete formulated using recycled concrete aggregate (RCA), in relation …

[HTML][HTML] iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC

P Feng, H Yang, H Ding, H Lin, W Chen, KC Chou - Genomics, 2019 - Elsevier
Abstract N 6-methyladenine (6mA) is one kind of post-replication modification (PTM or
PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will …

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

H Yang, L Sun, W Li, G Liu, Y Tang - Frontiers in chemistry, 2018 - frontiersin.org
During drug development, safety is always the most important issue, including a variety of
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …