AIMC Topic:
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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...

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

International journal of medical informatics
OBJECTIVES: Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk o...

Predicting protein complexes using a supervised learning method combined with local structural information.

PloS one
The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein i...

Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network with Coupled Softmax.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The discriminability of Bag-of-Words representations can be increased via encoding the spatial relationship among virtual words on 3D shapes. However, this encoding task involves several issues, including arbitrary mesh resolutions, irregular vertex ...