Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Background: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncove...
Alessandro Di Cara, Abhishek Garg, Giovanni De Mic...
Rapid development of DNA sequencing technologies exponentially increases the amount of publicly available genomic data. Whole genome multiple sequence alignments represent a parti...
Pavol Hanus, Janis Dingel, Georg Chalkidis, Joachi...
Caches are notorious for their unpredictability. It is difficult or even impossible to predict if a memory access results in a definite cache hit or miss. This unpredictability i...
Prediction of protein tertiary structure based on amino acid sequence is one of the most challenging open questions in computational molecular biology. The two most common experim...
Paul E. Anderson, Douglas W. Raiford, Deacon J. Sw...