Genetics

Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction.

Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, there...

Tissue enrichment analysis for C. elegans genomics.

BACKGROUND: Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these datasets and forming hypotheses based on them remains challenging. One way to analyze these datasets is to associate ontologies (hierarchical, descriptive vocabularies with controlled re...

GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.

BACKGROUND: High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume ...

Unsupervised detection of cancer driver mutations with parsimony-guided learning.

Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput sequencing cancer data sets...

Combined thioflavin T-Congo red fluorescence assay for amyloid fibril detection.

Fluorescence represents one of the most powerful tools for the detection and structural characterization of the pathogenic protein aggregates, amyloid...

Skeleton Genetics: a comprehensive database for genes and mutations related to genetic skeletal disorders.

Genetic skeletal disorders (GSD) involving the skeletal system arises through disturbances in the complex processes of skeletal development, growth an...

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The pre...

FFPred 3: feature-based function prediction for all Gene Ontology domains.

Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-w...

Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.

BACKGROUND: The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical...

Using the Generalized Index of Dissimilarity to Detect Gene-Gene Interactions in Multi-Class Phenotypes.

To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimens...

Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology.

Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analys...

Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.

BACKGROUND: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to id...

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene reg...

Thought-Controlled Nanoscale Robots in a Living Host.

We report a new type of brain-machine interface enabling a human operator to control nanometer-size robots inside a living animal by brain activity. R...

Extensive complementarity between gene function prediction methods.

MOTIVATION: The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated functio...

DMAK: A curated pan-cancer DNA methylation annotation knowledgebase.

Pan-cancer analysis can identify cell- and tissue-specific genomic loci and regions with underlying biological functions. Here we present an online cu...

Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding.

This study presents a machine learning method that increases the number of identified bases in Sanger Sequencing. The system post-processes a KB basec...

Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach.

DNA Fragment assembly - an NP-Hard problem - is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, incl...

TargetM6A: Identifying N-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

As one of the most ubiquitous post-transcriptional modifications of RNA, N-methyladenosine ( [Formula: see text]) plays an essential role in many vita...

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

BACKGROUND: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphism...