AIMC Topic: Computational Biology

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Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.

Computational and mathematical methods in medicine
Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based...

Deep Learning in Virtual Screening: Recent Applications and Developments.

International journal of molecular sciences
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied...

Protein design and variant prediction using autoregressive generative models.

Nature communications
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for importa...

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Computational and mathematical methods in medicine
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation...

Iterative single-cell multi-omic integration using online learning.

Nature biotechnology
Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for...

FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.

Journal of biomolecular NMR
In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies o...

iRNA-m5U: A sequence based predictor for identifying 5-methyluridine modification sites in Saccharomyces cerevisiae.

Methods (San Diego, Calif.)
The 5-methyluridine (mU)modification plays important roles in a series of biological processes. Accurate identification of mU sites will be helpful to decode its biological functions. Although experimental techniques have been proposed to detect mU, ...

Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning.

Nature communications
An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there ar...

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Frontiers in immunology
Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM...

Opportunities and Challenges in Democratizing Immunology Datasets.

Frontiers in immunology
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniqu...