Re: The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer.
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Area Under Curve↗
Biomedical subjects
Marco Muselli
Publications and source records attributed to Marco Muselli.
Deterministic design for neural network learning: an approach based on discrepancy.
The general problem of reconstructing an unknown function from a finite collection of samples is considered, in case the position of each input vector in the training set is not fixed beforehand but is part of the learning process. In particular, the consistency of the empirical risk minimization (ERM) principle is analyzed, when the points in the input space are generated by employing a purely deterministic algorithm (deterministic learning). When the output generation is not subject to noise, classical number-theoretic results, involving discrepancy and variation, enable the establishment of a sufficient condition for the consistency of the ERM principle. In addition, the adoption of low-discrepancy sequences enables the achievement of a learning rate of O(1/L), with L being the size of the training set. An extension to the noisy case is provided, which shows that the good properties of deterministic learning are preserved, if the level of noise at the output is not high. Simulation results confirm the validity of the proposed approach.
Artificial Intelligence↗
Alternative splicing of the human estrogen receptor alpha primary transcript: mechanisms of exon skipping.
The 1785 nucleotides of the coding region of the estrogen receptor alpha (ER-alpha) are dispersed over a region of more than 300,000 nucleotides in the primary transcript. Splicing of this precursor RNA frequently leads to variants lacking one or more exons that have been associated to breast cancer progression. The most frequent splice variant lacks exon 4 and is expressed in the human mammary carcinoma cell line MCF-7 at a level similar to that of the full-length messenger. The in silico analysis of ER-alpha splice sites by Hamming clustering, a self learning method trained on more than 28,000 experimentally proved splice sites, reveals high relevance for the 5' and 3' splice sites of exon 4. The splicing analysis of transfected mini-gene constructs containing drastically shortened introns excludes that weak splice sites, intron or exon lengths or splice enhancers are responsible for exon skipping. Exon 6 is never skipped in MCF-7 cells but is spliced out from mini-gene derived primary transcripts if inserted between exons 3 and 5 instead of exon 4. As a consequence, it appears that a particular splice site affinity of exon 3 donor (5' splice site) and exon 5 acceptor sites (3' splice site) is responsible for skipping of the exon in between.
Alternative Splicing↗