AIMC Topic: Software

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Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.

Analytical biochemistry
Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutaryl...

TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility.

Journal of computational chemistry
Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction ac...

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

Journal of proteome research
Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary b...

Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features ...

The Engineering of Chemical Synthesis: Humans and Machines Working in Harmony.

Angewandte Chemie (International ed. in English)
"Chemical synthesis has previously tended to rely heavily on robust labour-intensive processes. We have been advocating a machine-assisted approach to synthesis for many years. To replace a bench chemist with a machine is not only unrealistic but imp...

MetStabOn-Online Platform for Metabolic Stability Predictions.

International journal of molecular sciences
Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extrem...

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

International journal of molecular sciences
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular...

Modified Global Flower Pollination Algorithm and its Application for Optimization Problems.

Interdisciplinary sciences, computational life sciences
Flower Pollination Algorithm (FPA) has increasingly attracted researchers' attention in the computational intelligence field. This is due to its simplicity and efficiency in searching for global optimality of many optimization problems. However, ther...

High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
Although Deep learning algorithms have outperformed conventional methods in predicting the sequence specificities of DNA-protein binding, they lack to consider the dependencies among nucleotides and the diverse binding lengths for different transcrip...

SPIN2: Predicting sequence profiles from protein structures using deep neural networks.

Proteins
Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native bac...