AIMC Topic: Computational Biology

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Prediction of thirty-day morbidity and mortality after laparoscopic sleeve gastrectomy: data from an artificial neural network.

Surgical endoscopy
BACKGROUND: Multiple patient factors may convey increased risk of 30-day morbidity and mortality after laparoscopic vertical sleeve gastrectomy (LVSG). Assessing the likelihood of short-term morbidity is useful for both the bariatric surgeon and pati...

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes.

PLoS computational biology
Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore imp...

Investigation of machine learning techniques on proteomics: A comprehensive survey.

Progress in biophysics and molecular biology
Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete ...

Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-Protein binding is involved in many different biological processes. With the progress of technology, more and more data are available for research. Based on these data, many prediction methods have been proposed to predict RNA-Protein binding pre...

Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network.

Computational and mathematical methods in medicine
A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocess...

Predicting gene regulatory interactions based on spatial gene expression data and deep learning.

PLoS computational biology
Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on gene co-expression analysis or TF-target binding information, where the determination of co-expression...

NeuRiPP: Neural network identification of RiPP precursor peptides.

Scientific reports
Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both...

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.

PLoS computational biology
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear ...

Analysis of distance-based protein structure prediction by deep learning in CASP13.

Proteins
This paper reports the CASP13 results of distance-based contact prediction, threading, and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by...

Deep attention networks reveal the rules of collective motion in zebrafish.

PLoS computational biology
A variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but may lack important elements necessary to predict the motion of each individual in the collective. Adding more detail incre...