Protein function in precision medicine: deep understanding with machine learning

Burkhard Rost, Predrag Radivojac, Yana Bromberg
<span title="">2016</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kn6dhptylrb77b5atyiom5ysjm" style="color: black;">FEBS Letters</a> </i> &nbsp;
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do
more &raquo; ... r sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/1873-3468.12307">doi:10.1002/1873-3468.12307</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27423136">pmid:27423136</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5937700/">pmcid:PMC5937700</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f7yfs3d5lbcvboroe6ndspfyfq">fatcat:f7yfs3d5lbcvboroe6ndspfyfq</a> </span>
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