Genetics

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design h...

Compositional memory in attractor neural networks with one-step learning.

Compositionality refers to the ability of an intelligent system to construct models out of reusable parts. This is critical for the productivity and generalization of human reasoning, and is considered a necessary ingredient for human-level artificial intelligence. While traditional symbolic methods have proven effective for modeling compositionality, artificial neural networks struggle to learn s...

RNA secondary structure prediction using deep learning with thermodynamic integration.

Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have...

Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis.

This paper introduces a kernel based fuzzy clustering approach to deal with the non-linear separable problems by applying kernel Radial Basis Function...

Systematic characterization of mutations altering protein degradation in human cancers.

The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel...

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in ...

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and p...

A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.

. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first ...

A novel gene selection method for gene expression data for the task of cancer type classification.

Cancer is a poligenetic disease with each cancer type having a different mutation profile. Genomic data can be utilized to detect these profiles and t...

A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications.

Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in diff...

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate p...

In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and pr...

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The ...

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because ...

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data.

In the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the pr...

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the ...

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predic...

PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses.

Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated metho...

COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network.

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymer...

Creating artificial human genomes using generative neural networks.

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased compu...