Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders
In a research environment dominated by reductionist approaches to brain disease
mechanisms, gene network analysis provides a complementary framework in which to tackle …
mechanisms, gene network analysis provides a complementary framework in which to tackle …
Artificial intelligence in drug combination therapy
IF Tsigelny - Briefings in bioinformatics, 2019 - academic.oup.com
Currently, the development of medicines for complex diseases requires the development of
combination drug therapies. It is necessary because in many cases, one drug cannot target …
combination drug therapies. It is necessary because in many cases, one drug cannot target …
Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study
VR Varma, AM Oommen, S Varma, R Casanova… - PLoS …, 2018 - journals.plos.org
Background The metabolic basis of Alzheimer disease (AD) is poorly understood, and the
relationships between systemic abnormalities in metabolism and AD pathogenesis are …
relationships between systemic abnormalities in metabolism and AD pathogenesis are …
Matching matched filtering with deep networks for gravitational-wave astronomy
We report on the construction of a deep convolutional neural network that can reproduce the
sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The …
sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The …
Clustering cancer gene expression data: a comparative study
Background The use of clustering methods for the discovery of cancer subtypes has drawn a
great deal of attention in the scientific community. While bioinformaticians have proposed …
great deal of attention in the scientific community. While bioinformaticians have proposed …
On the selection of appropriate distances for gene expression data clustering
Background Clustering is crucial for gene expression data analysis. As an unsupervised
exploratory procedure its results can help researchers to gain insights and formulate new …
exploratory procedure its results can help researchers to gain insights and formulate new …
Feature selection may improve deep neural networks for the bioinformatics problems
Z Chen, M Pang, Z Zhao, S Li, R Miao, Y Zhang… - …, 2020 - academic.oup.com
Motivation Deep neural network (DNN) algorithms were utilized in predicting various
biomedical phenotypes recently, and demonstrated very good prediction performances …
biomedical phenotypes recently, and demonstrated very good prediction performances …
A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information
X Lin, F Yang, L Zhou, P Yin, H Kong, W Xing… - … of chromatography B, 2012 - Elsevier
Filtering the discriminative metabolites from high dimension metabolome data is very
important in metabolomics study. Support vector machine-recursive feature elimination …
important in metabolomics study. Support vector machine-recursive feature elimination …
Cumida: An extensively curated microarray database for benchmarking and testing of machine learning approaches in cancer research
The employment of machine learning (ML) approaches to extract gene expression
information from microarray studies has increased in the past years, specially on cancer …
information from microarray studies has increased in the past years, specially on cancer …
Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis
Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M. tb),
causes highest number of deaths globally for any bacterial disease necessitating novel …
causes highest number of deaths globally for any bacterial disease necessitating novel …