AIMC Topic: Deep Learning

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Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting.

Neural computation
Deep learning is often criticized by two serious issues that rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labeled data, which has little knowledge behind the instance-label pairs. Whe...

A self-attention model for inferring cooperativity between regulatory features.

Nucleic acids research
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...

Low-cost smartphone-based LIBS combined with deep learning image processing for accurate lithology recognition.

Chemical communications (Cambridge, England)
A low-cost and multi-channel smartphone-based spectrometer was developed for LIBS. As the CMOS detector is two-dimensional, simultaneous multichannel detection was achieved by coupling a linear array of fibres for light collection. Thus, besides the ...

A deep learning approach for filtering structural variants in short read sequencing data.

Briefings in bioinformatics
Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accurate detection of structural variants is a challenging task. Especially existing structural variant ...

Deep inverse reinforcement learning for structural evolution of small molecules.

Briefings in bioinformatics
The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the p...

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.

Briefings in bioinformatics
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some comput...

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.

Briefings in bioinformatics
Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechan...

Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...

Identification of pan-cancer Ras pathway activation with deep learning.

Briefings in bioinformatics
The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several...

Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method.

Briefings in bioinformatics
As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been developed to detect Kcr sites. However, experimental approa...