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Asa Ben-Hur

Publications and source records attributed to Asa Ben-Hur.

6 recordsLinked to original sources
PubMed2026

On the state of protein function prediction: a report on the fourth CAFA challenge.

BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). RESULTS: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

Journal Article↗
PubMed2025

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources. We argue that these models are the equivalent of foundation models in natural language processing in their utility, as they encode within them chromatin state in its different aspects, providing useful representations that allow quick deployment of accurate models of gene regulation. We demonstrate this premise by leveraging the recently created Sei model to develop simple, interpretable models of intron retention, and demonstrate their advantage over models based on the DNA language model DNABERT-2. Our work also demonstrates the impact of chromatin state on the regulation of intron retention. Using representations learned by Sei, our model is able to discover the involvement of transcription factors and chromatin marks in regulating intron retention, providing better accuracy than a recently published custom model developed for this purpose.

Deep Learning↗
PubMed2003

Remote homology detection: a motif based approach.

MOTIVATION: Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. RESULTS: We present a method for detecting remote homology that is based on the presence of discrete sequence motifs. The motif content of a pair of sequences is used to define a similarity that is used as a kernel for a Support Vector Machine (SVM) classifier. We test the method on two remote homology detection tasks: prediction of a previously unseen SCOP family and prediction of an enzyme class given other enzymes that have a similar function on other substrates. We find that it performs significantly better than an SVM method that uses BLAST or Smith-Waterman similarity scores as features.

Algorithms↗
PubMed2003

CREME: a framework for identifying cis-regulatory modules in human-mouse conserved segments.

MOTIVATION: The binding of transcription factors to specific regulatory sequence elements is a primary mechanism for controlling gene transcription. Recent findings suggest a modular organization of binding sites for transcription factors that cooperate in the regulation of genes. In this work we establish a framework for finding recurrent cis-regulatory modules in the promoters of a selected set of genes and scoring their statistical significance. RESULTS: Proceeding from a database of identified binding site motifs and their genomic locations we seek motifs whose frequency in the selected promoters is different than in a background promoter set. We present several statistical tests designed for this purpose. We provide a hashing algorithm for detecting combinations of these motifs that co-occur in clusters within the selected promoters. The significance of such co-occurrences is evaluated using novel statistical scores. Our methods are combined in CREME, a suite of software which includes a browser for viewing the pattern of occurrence of selected cis-regulatory modules. We applied our methodology to find modules within human-mouse conserved promoter segments, focusing on cell cycle regulated genes and stress response related genes. To validate the biological significance of the identified modules we tested whether the associated genes tended to be co-expressed or share similar function. In the cell cycle set five of the seven identified sets of genes were coherently expressed. On the stress response data four of the six detected sets fell predominantly into well-defined functional sub-categories.

Algorithms↗
PubMed2002

A stability based method for discovering structure in clustered data.

We present a method for visually and quantitatively assessing the presence of structure in clustered data. The method exploits measurements of the stability of clustering solutions obtained by perturbing the data set. Stability is characterized by the distribution of pairwise similarities between clusterings obtained from sub samples of the data. High pairwise similarities indicate a stable clustering pattern. The method can be used with any clustering algorithm; it provides a means of rationally defining an optimum number of clusters, and can also detect the lack of structure in data. We show results on artificial and microarray data using a hierarchical clustering algorithm.

Algorithms↗