AIMC Topic: Neural Networks, Computer

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Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks.

Journal of chemical information and modeling
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and cannot pro...

MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

BMC genomics
BACKGROUND: Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database sea...

iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.

BMC genomics
BACKGROUND: Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, ...

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks.

BMC bioinformatics
BACKGROUND: Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target i...

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

BMC bioinformatics
BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, m...

Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter.

BMC bioinformatics
BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, a...

DEEPSEN: a convolutional neural network based method for super-enhancer prediction.

BMC bioinformatics
BACKGROUND: Super-enhancers (SEs) are clusters of transcriptional active enhancers, which dictate the expression of genes defining cell identity and play an important role in the development and progression of tumors and other diseases. Many key canc...

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks.

Journal of neurointerventional surgery
BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in cli...

Preterm Infants' Pose Estimation With Spatio-Temporal Features.

IEEE transactions on bio-medical engineering
OBJECTIVE: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm infants' limb pose estima...

Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology.

Talanta
In the last years, regulatory agencies in biopharmaceutical industry have promoted the design and implementation of Process Analytical Technology (PAT), which aims to develop rapid and high-throughput strategies for real-time monitoring of bioprocess...