AIMC Topic: Benchmarking

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Anticancer peptides prediction with deep representation learning features.

Briefings in bioinformatics
Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, ...

Identification of haploinsufficient genes from epigenomic data using deep forest.

Briefings in bioinformatics
Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been develop...

DeepCNV: a deep learning approach for authenticating copy number variations.

Briefings in bioinformatics
Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably hig...

PharmKG: a dedicated knowledge graph benchmark for bomedical data mining.

Briefings in bioinformatics
Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to ...

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

Cell reports
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-fre...

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.

GigaScience
BACKGROUND: Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice c...

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis.

JCO clinical cancer informatics
PURPOSE: Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nucl...

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain.

Database : the journal of biological databases and curation
The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where seman...

Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Emergency departments (EDs) continue to pursue optimal patient flow without sacrificing quality of care. The speed with which a healthcare provider receives pertinent information, such as results from clinical orders, can impact flow. We s...