AIMC Topic: Computational Biology

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Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers.

IEEE/ACM transactions on computational biology and bioinformatics
Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for a...

Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory.

IEEE/ACM transactions on computational biology and bioinformatics
Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed fo...

Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus.

IEEE/ACM transactions on computational biology and bioinformatics
Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrov...

A Convolutional Neural Network System to Discriminate Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or f...

iEnhancer-RD: Identification of enhancers and their strength using RKPK features and deep neural networks.

Analytical biochemistry
Enhancers are regulatory elements involved in gene expression.It is a part of DNA, which can enhance the transcription rate of gene. However, the identification of enhancer by biological experimental methods is time-consuming and expensive. Therefore...

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

Computational and mathematical methods in medicine
We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach...

Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data.

IEEE transactions on cybernetics
Non-negative matrix factorization (NMF) has become one of the most powerful methods for clustering and feature selection. However, the performance of the traditional NMF method severely degrades when the data contain noises and outliers or the manifo...

A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.

PLoS computational biology
The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucl...

A stacking ensemble deep learning approach to cancer type classification based on TCGA data.

Scientific reports
Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers...

Predicting mutant outcome by combining deep mutational scanning and machine learning.

Proteins
Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the ...