AIMC Topic: Computational Biology

Clear Filters Showing 2481 to 2490 of 4583 articles

Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast.

Proceedings of the National Academy of Sciences of the United States of America
One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/tho...

Contributions from the 2018 Literature on Bioinformatics and Translational Informatics.

Yearbook of medical informatics
OBJECTIVES: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearboo...

Advances in gene ontology utilization improve statistical power of annotation enrichment.

PloS one
Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in ...

rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments.

PloS one
In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins fun...

BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

Nature methods
The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data f...

Deep Learning: The Good, the Bad, and the Ugly.

Annual review of vision science
Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machin...

Cross-registry neural domain adaptation to extract mutational test results from pathology reports.

Journal of biomedical informatics
OBJECTIVE: We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from pathology reports collected by cancer registries. Given the lack of hand-labeled datasets for mutational te...

SecProMTB: Support Vector Machine-Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis.

Proteomics
Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time-consuming traditional biochemical experiments, an advanced support vector machine ...

HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics
BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a singl...