AIMC Topic: Neural Networks, Computer

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DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.

Briefings in bioinformatics
MOTIVATION: Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experime...

How much can deep learning improve prediction of the responses to drugs in cancer cell lines?

Briefings in bioinformatics
The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction....

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding.

Briefings in bioinformatics
Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computat...

A general optimization protocol for molecular property prediction using a deep learning network.

Briefings in bioinformatics
The key to generating the best deep learning model for predicting molecular property is to test and apply various optimization methods. While individual optimization methods from different past works outside the pharmaceutical domain each succeeded i...

mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences.

Briefings in bioinformatics
In the past decade, convolutional neural networks (CNNs) have been used as powerful tools by scientists to solve visual data tasks. However, many efforts of convolutional neural networks in solving protein function prediction and extracting useful in...

Graph representation learning in bioinformatics: trends, methods and applications.

Briefings in bioinformatics
Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeed...

Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review.

Studies in health technology and informatics
Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of th...

Artificial Intelligence Models for Heart Chambers Segmentation from 2D Echocardiographic Images: A Scoping Review.

Studies in health technology and informatics
Echocardiography (echo) is a non-invasive, safe, widely available imaging modality that is frequently used to assess the heart structure and function. Accurate heart chamber segmentation is an essential step to quantify certain parameters, including ...

A Deep Learning Program to Predict Acute Kidney Injury.

Studies in health technology and informatics
Acute kidney injury is a dangerous and sometime fatal clinical situation, which can cause irreversible damage. If we can predict it earlier and make appropriate prevention before its outbreak, kidney injury could be avoided. One challenge of early re...

ALLD: Acute Lymphoblastic Leukemia Detector.

Studies in health technology and informatics
Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we d...