AIMC Topic: Computational Biology

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Predicting protein residue-residue contacts using random forests and deep networks.

BMC bioinformatics
BACKGROUND: The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the c...

DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies.

Cell systems
Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, call...

Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Scientific reports
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model.

Scientific reports
Measuring conditional relatedness between a pair of genes is a fundamental technique and still a significant challenge in computational biology. Such relatedness can be assessed by gene expression similarities while suffering high false discovery rat...

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

Proteins
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential predict...

Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Antimicrobial peptides are short amino acid sequences that may be antibacterial, antifungal, and antiviral. Most machine learning methodologies applied to identifying antibacterial peptides have developed feature vectors of identical lengths for each...

Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.

Methods (San Diego, Calif.)
Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were pre...

Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.

Journal of chemical information and modeling
Predicting the outcome of biological assays based on high-throughput imaging data is a highly promising task in drug discovery since it can tremendously increase hit rates and suggest novel chemical scaffolds. However, end-to-end learning with convol...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...

DESTINI: A deep-learning approach to contact-driven protein structure prediction.

Scientific reports
The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DEST...