AIMC Topic: Neural Networks, Computer

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IGPRED-MultiTask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility.

IEEE/ACM transactions on computational biology and bioinformatics
Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein str...

Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.

IEEE/ACM transactions on computational biology and bioinformatics
Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the mode...

Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

IEEE/ACM transactions on computational biology and bioinformatics
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, i...

Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer us...

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data so...

Comparing representations and computations in single neurons versus neural networks.

Trends in cognitive sciences
Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of ne...

Reconstruction and analysis of negatively buoyant jets with interpretable machine learning.

Marine pollution bulletin
In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. A detailed numerical investigation is necessary to minimize harmful effects and assess environmental i...

Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?

Annual review of vision science
Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to ...

Differential evolution based dual adversarial camouflage: Fooling human eyes and object detectors.

Neural networks : the official journal of the International Neural Network Society
Deep neural network-based object detectors are vulnerable to adversarial examples. Among existing works to fool object detectors, the camouflage-based method is more often adopted due to its adaptation to multi-view scenarios and non-planar objects. ...

Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis.

Artificial intelligence in medicine
Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the presence of various image artifacts, which results in poor visual quality of the textural and lower-f...